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Are broad market shocks anticipated by investors? Evidence from major equity and index options markets
International Review of Financial Analysis 20 (2011) 127–133
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International Review of Financial Analysis
Are broad market shocks anticipated by investors? Evidence from major equity and index options markets Spyros Spyrou ⁎ Athens University of Economics and Business, Department of Accounting and Finance, 76 Patision Str., 10434, Athens, Greece
a r t i c l e
i n f o
Article history: Received 21 February 2010 Received in revised form 19 January 2011 Accepted 4 February 2011 Available online 18 February 2011 JEL classification: G1 G12 G14
a b s t r a c t This paper examines trading activity in five index options markets before significant price shocks in the underlying asset (S&P100, FTSE100, CAC40, DAX30, and AEX). The results indicate abnormal call and put option trading volume before price shocks for a large number of cases, implying that market participants anticipate shocks and use the options market as the venue for their trading. This pattern is similar for all markets and persistent for three different pre-event periods (10, 20, and 30 days), two different periods used to calculate the benchmark period trading volume (100 and 140 days), and of whether open interest is used instead of trading volume. Further tests suggest that investors may use both long and short strategies. © 2011 Elsevier Inc. All rights reserved.
Keywords: Price shocks Market anticipation Option trading volume
1. Introduction In markets where prices incorporate news quickly and accurately asset prices will change due to the arrival of new information in the market. By implication, large price shocks reflect the arrival of related significant unexpected information and a significant shift in expectations. In the case of broad equity market portfolios, price shocks should be due to the arrival of significant market-wide information, although evidence suggests that this may not always be true. For instance, Cutler, Poterba, and Summers (1989) examine the 50 largest price shocks in the S&P500 for a 40-year period and find that few are related to particular news events or other information. Irrespective of the origin of a price shock, two questions arise: (a) how do investors react to large stock price changes and, (b) are stock price shocks unanticipated by investors? The literature is rich and offers interesting results regarding the first question. For example, Chan (2003), Benou and Richie (2003), Bremer, Hiraki, and Sweeney (1997), among others, report evidence consistent with reversals after extreme stock price movements (see also Atkins & Dyl, 1990; Bremer & Sweeney, 1991; Brown, Harlow, & Tinic, 1988; Cox & Peterson, 1994; Howe, 1986). Dennis and Strickland (2002) argue that abnormal returns following large price drops depend on the level of institutional ownership of a stock.
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Schnusenberg and Madura (2001) find one-day under-reaction following days on which US equity indexes experience abnormally high or low returns and significant reversals over a 60-day period for negative market shocks; Lasfer, Melnik, and Thomas (2003) employ 39 international equity market indexes and find short-term underreaction which is more pronounced for emerging markets; Kassimatis, Spyrou, and Galariotis (2008) find an initial momentum and a subsequent reversal for 17 international bond indexes. There is, however, comparatively scarce evidence in the literature regarding the second question. This paper aims to explore this issue further. More specifically, the paper addresses the question of whether investors anticipate significant broad market changes (market shocks) and trade in anticipation of the coming large price change. If price shocks are anticipated it is logical to expect that investors will take positions in the market that reflect their expectations. Moreover, it can be argued that these positions are more likely to materialize in derivative markets due to transaction costs and leverage. Indeed, as the discussion below suggests, there is a link between trading by informed investors and option markets. Thus, in this paper, spot daily return data on major European equity indexes (FTSE100, CAC40, DAX30, and AEX) are employed to identify days with abnormally high market returns, i.e. shock days, and then trading volume data from the respective index option contracts are examined in order to uncover whether there is abnormal option trading volume for the period preceding shock days. For comparative purposes and in order to establish whether there is a cross-country pattern in market participant's behavior (Fama & French, 1996) one major US index
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(S&P100) is also included in the sample. This is the first study, to the best of my knowledge, which examines whether there is an abnormal option trading volume for index option contracts during the period preceding significant price shocks. The use of options markets to uncover whether investors trade more in anticipation of market shocks is motivated by findings in the literature that examines informed trading. Chakravarty, Gulen, and Mayhew (2004), for instance, argue that investors with private information may view the options market as an ideal venue for their informed trading due to the leverage and the downside protection of options and, if that is so, price discovery can take place in the options markets; Easley, O'Hara, and Srinivas (1998) suggest that the volume of trading in the options market can help forecast equity returns (see also Booth, So, & Tse, 1999; Lee & Cheong, 2001; Mayhew, Sarin, & Shastri, 1995; Pan & Poteshman, 2006). Similar results come from a related strand in the literature that examines whether informed trading takes place in the options market prior to corporate events such as Merger and Acquisition (M&A) announcements. More specifically, a common result is that market participants with material information are likely to start abnormal trading ahead of major takeover announcements, with the option market playing an important role in information revelation during this period (see also Arnold, Erwin, Nail, & Nixon, 2006; Cao, Chen, & Griffin, 2005). For example, as Jayaraman, Frye, and Sabherwal (2001) discuss, informed market participants that anticipate the arrival of information can employ a number of option strategies prior to the event and that, irrespective of the strategy, the implication is that option trading strategies that aim to take advantage of superior information will result to increased call and put trading volumes; they find a significant increase in the trading activity of both call and put option contracts for the firms involved in an M&A prior to the announcement. The results of the paper are consistent with previous findings for firm-specific events (e.g. M&As) and suggest that, in the majority of the cases, there is an abnormal trading volume in index options contracts during the period before price shocks, implying that traders anticipate price shocks and are likely to start abnormal trading ahead of the event. This pattern is similar for all markets in the sample and persistent for all three different pre-event periods examined (10, 20, and 30 days), irrespective of the length of the periods used to calculate the benchmark period trading volume (100 and 140 days), and of whether open interest is used instead of trading volume. Further tests suggest that investors may use both long and short strategies. The findings of the paper have implications for academic research, market participants, and regulators. For example, if many marketwide shocks are not due to particular news events or other fundamental information, as Cutler et al. (1989) find, then traders may be taking positions in anticipation of a non-informational event, e.g. a shift in market sentiment. In other words, the findings may be indirect evidence that sentiment is a factor that determines equity returns. In addition, the findings in this paper could have implications for previously reported results: Lasfer et al. (2003) investigate market reaction to price shocks for 39 international equity market indexes and find that the magnitude of the shock and the abnormal post-shock reaction is significantly larger for emerging markets than for developed equity markets. It could be argued that in developed capital markets, with liquid and organized options markets where at least some traders anticipate price shocks, the options markets absorb part of the impact of shock. Thus, the lower magnitude of the shock and the lower post-shock abnormal performance for developed markets when compared to emerging markets. The implications for regulators are also important: if a market shock is due to significant fundamental information that is released to the market, the abnormal pre-event option trading volume suggests that either some investors anticipate the shock based on superior analysis or that some investors have privileged access to this in-
formation. The rest of the paper is organized as follows: Section 2 discusses the data and methodology, Section 3 presents the results, while Section 4 concludes the paper. 2. Data and methodology For the empirical analysis five well-known stock market indexes are employed as proxies for stock market activity in five major markets. More specifically, the markets are the USA, UK, France, Germany, and the Netherlands, and the indexes are the S&P100 Index, the FTSE100 Index, the CAC40 Index, the DAX30 Index, and the AEX Index, respectively. The S&P100 Index includes 100 major U.S. stocks that represent roughly half of U.S. stock market capitalization while the FTSE100 constituents are the 100 listed companies with the highest market capitalization and represent about 80% of London Stock Exchange capitalization. The CAC40 Index represents the 40 most important securities among the 100 highest capitalization stocks listed in the Euronext Paris, and the DAX is an index composed of the highest capitalization securities traded in Frankfurt Stock Exchange. Finally, the AEX Index is composed of the 25 most actively traded stocks listed in the Amsterdam Exchange. Note that the Amsterdam Exchange is one of the four constituents markets of Euronext (along with Paris, Lisbon, and Brussels), which is the second largest securities market in Europe after the London Stock Exchange. Overall, the market indexes chosen for the analysis include heavily traded securities in the USA and major European equity markets. The selected indexes all have option contracts available; Table 1 (Panel A) presents a list of the indexes and exchanges at which the sample option contracts on the respective indexes are traded, as well as the available sample period. For every index, daily price data and daily (call and put) option trading volume data are collected for the period between November 1994 and November 2009, i.e. fifteen years of data, or 3775 daily observations. The data are collected from the Datastream database. The unconditional daily change for the equity index i on day t is computed as follows: ri;t =
Pi;t −Pi;t−1 : Pi;t−1
ð1Þ
In Eq. (1), (ri,t) as the change between today's and previous day's closing price (P). Descriptive statistics for daily returns for the sample indexes are presented in Table 1, Panel B. Average daily returns are of similar magnitude in all markets and are about 0.02% for the FTSE, DAX, and AEX, 0.03% for the S&P100, and 0.02% for the CAC. The standard deviation ranges from 1.23% for the FTSE to 1.55% for the DAX. The kurtosis coefficients are positive and relatively high, indicating leptokurtic return distributions with fatter tails and that a large part of the return variance may be due to non-frequent extreme deviations. The skewness coefficients are positive and close to zero with the exception of the DAX which is comparatively higher; suggesting that the return distributions are right-skewed. Daily call and put option trading volume data are collected from the Datastream database. Daily option trading volume is defined as the number of option contracts traded on each day (total cumulative volume for all individual option series). The sample period is between November 1994 and November 2009. Table 1 presents descriptive statistics for daily option trading volumes for call contracts (Panel C) and put contracts (Panel D). As can be seen from Panel C, daily call option trading volume ranges from an average of 19,108 contracts for the FTSE Index to an average of 100,859 contracts for the DAX Index; the standard deviation ranges from 16,306 contracts for the FTSE Index to 86,983 contracts for the CAC Index. The kurtosis and skewness coefficients indicate leptokurtic and right-skewed distributions. Similarly daily put option trading volume ranges from an
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Table 1 Data and descriptive statistics. Panel A: markets and indexes Country
Index
Index option contract
Start–end date
Currency
USA UK Germany France Netherlands
S&P100 FTASE100 DAX30 CAC40 AEX
CBOE LIFFE EUREX MONEP AMS
1994–2009 1994–2009 1994–2009 1994–2009 1994–2009
US dollar Sterling Euro Euro Euro
S&P100 Panel B: descriptive daily return statistics for indexes Mean R 0.0003 St. deviation 0.0129 Kurtosis 7.49 Skewness 0.03
FTSE100
DAX30
0.0002 0.0123 6.35 0.06
0.0002 0.0155 17.17 0.80
CAC40
AEX 0.0002 0.0150 6.13 0.07
0.0002 0.0152 6.06 0.05
Panel C: call option daily volume descriptive statistics Mean Vt 46,548 St. deviation 38,433 Kurtosis 9.69 Skewness 2.56
19,108 16,306 11,83 2.07
100,859 57,545 3.92 1.57
66,405 86,983 5.44 1.88
21,944 18,367 3.46 1.63
Panel D: put options daily volume descriptive statistics Mean Vt 55,020 St. deviation 45,998 Kurtosis 8.11 Skewness 2.43
24,706 20,503 2.38 1.32
116,535 70,366 4.63 1.78
80,569 107,037 3,61 1.79
24,419 19,989 3.26 1.52
average of 24,419 contracts for the AEX Index to an average of 116,535 contracts for the DAX Index; the standard deviation ranges from 19,989 contracts for the AEX Index to 107,037 contracts for the CAC Index. The kurtosis and skewness coefficients indicate leptokurtic and right-skewed distributions for put trading volume as well. 2.1. Definition of an extreme event The relevant literature offers a variety of definitions for an extreme price movement. For example, Bremer and Sweeney (1991) suggest that an extreme price movement for a stock is when the stock price drops by at least 10%, Howe (1986) employs weekly price changes of more than 50%, Atkins and Dyl (1990) use the largest price change in a 300-day window, Benou and Richie (2003) use a rule of 20% during a specific month to define a significant stock price decline, Dennis and Strickland (2002) define large price declines as days where the absolute value of the market's return is down 2% or more, Schnusenberg and Madura (2001) use the top (bottom) 10 percentile of computed abnormal daily returns to define winner (loser) days for stock market indexes, among others. Some studies employ the market model to adjust for risk; however, this relies on the assumption that the market model is valid. Lasfer et al. (2003), who also study equity indexes, point out that some of the above definitions cannot account appropriately for issues such as the varying return volatility form asset to asset and employ a definition that is based on the distance of a certain observation from the mean value. For instance, an extreme event occurs on a day where the asset return on that day is above or below two standard deviations of the average return computed over some previous reference period.1 This paper employs a methodology similar to Lasfer et al. (2003) to identify an extreme event: a significant price shock occurs on any day where the index return is above or below three standard deviations of the average daily index return computed over [− 60 to −11] days before the given day. The window ends 10 trading days prior to the event day in order to avoid possible price lead-up preceding the shocks. The standard deviation 1 This approach also accounts for time-variation in risk premia that could lead to serial correlation in returns (Ball & Kothari, 1989; Chan, 1988).
for day t is also computed from the observations between day t-60 and day t-11. 2.2. Estimating abnormal option trading volume In order to examine whether there is an abnormal level of option trading volume during the period preceding large price shocks a comparison period approach is used, as in Jayaraman et al. (2001). That is, the pre-event option trading volume is compared to the trading volume of a benchmark period (see also Amin & Lee, 1997; Cao et al., 2005; Schachter, 1988). Because of the variation in the number of option contracts traded daily, a logarithmic transformation of the option trading volume is used (see Sanders & Zdanowicz, 1992; among others) and trading volume is defined as: Vi;t = lnð1 + number of call ðputÞ contracts on index i traded on day t Þ:
ð2Þ In Eq. (2), i = S&P100, FTSE100, CAC40, DAX30, and AEX Indexes. The benchmark period trading volume is defined as the average trading volume for a 100-day period preceding the event and ending 41 days before the event (− 141 to −41): V b;i =
−41 1 ∑ V : 100 t = −140 it
ð3Þ
The pre-event option trading volume, or testing period volume, is defined as the average trading volume of the two trading weeks (10 trading days) immediately preceding the day of the large price change: V p;i =
0 1 ∑ V : 10 t = −10 it
ð4Þ
As a robustness test, a longer benchmark period (−181 to −41) and two additional testing periods (−20 and −30 days relative to the event) are also employed in the study. The same approach is adopted in analyzing trading volume for both call and put option contracts.
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Note that there are some cases of price-shock clustering, i.e. periods where successive price shocks occur close to each other. This could affect the benchmark or testing period trading volume (or both). In order to avoid biased estimates of trading volume, the above analysis is performed only for large price shock days that have a distance of at least 80 trading days between them. If market participants anticipate a price shock and there is a link between option markets and trading in anticipation of a market shock then we should observe abnormal trading volume during the period before the price shock, in other words, the pre-event option trading volume should be different (higher) to the trading volume of the benchmark period. Thus, the null hypothesis is that the pre-event option volume is equal to the benchmark period volume: H0: Vp,i = Vb, i, and the alternative hypothesis is that the pre-event option volume is different to the benchmark period volume (H1: Vp,i ≠ Vb,i). The rejection of the null hypothesis then implies that there is abnormal option trading volume before the event. Standard t-tests are employed to investigate the significance of difference in volume between benchmark and pre-event periods.2 3. Results Table 2 presents the results for call option contracts for the five markets and for a benchmark period of 100 days. Panel A presents the results for a pre-event period of 10 days, Panel B presents the results for a pre-event period of 20 days, and Panel C presents the results for a pre-event period of 30 days. The first line in each panel presents the five markets and the number of events for each market during the sample period (N). For example, for the S&P100 Index there are 23 extreme event days included in the analysis during the sample period, i.e. 23 days where the unconditional price change of the index is above or below three standard deviations of the average daily index return. The number of events is similar for all markets. The second line in each panel presents the percentage of events for which the pre-event call option trading volume is higher than the benchmark call option trading volume for each market (Vp,i N Vb,i), i.e. the percentage of events for which pre-event trading volume is higher than normal. For instance, for the S&P100 and a pre-event period of 10 days in 69.56% of events (16 out of 23) the pre-event call option trading volume is higher from the benchmark period volume. The third and fourth lines in each panel present the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively (termed as “Reject H0 at 5%” and “Reject H0 at 10%” respectively). For the S&P100 the null hypothesis of equality between pre-event and benchmark option trading volume is rejected in 60.86% of events (14 out of 23) for both the 5% and the 10% level of significance. The next two lines in each panel present the mean benchmark volume (“Mean Vb”) for the benchmark period of 100 days (− 41 to −141) across all events for each market and the mean pre-event volume (“Mean Vp”) across all events for each market. For the S&P100 and a pre-event period of 10 days the mean benchmark volume is 2 It could be argued that since shorter-term options tend to spike in volume as they approach maturity, a finding of increased volume could be just options coming due. A cross-check of events and expirations for the sample period reveals that only a small fraction of events take place during the two-week period before expiration. For example for the S&P only 5 out of 23 events, for FTSE100 3 out of 20 events, for the DAX 4 out of 20 events, for the AEX 4 out of 23 events, and for the CAC 7 out of 22 events. Also, this issue is indirectly addressed by the research methodology and the robustness tests: three pre-event periods are examined (10, 20, and 30 days) and two benchmark periods (100 and 140 days). The argument about higher volume for shortterm options holds for the benchmark periods as well, i.e. benchmark volumes include periods of higher volume from this reason and they too could appear higher. Assuming that a near-maturity option is an option that expires in one to two weeks (i.e. 10 trading days) and that the event coincides with expiration, the choice of longer preevent periods (20 and 30 trading days) as a robustness test should reveal a potential false interpretation of the results.
Table 2 Is there abnormal option trading volume before market shocks? Evidence from stock index call option contracts. S&P100 (N = 23)
FTSE100 (N = 20)
DAX30 (N = 20)
CAC40 (N = 22)
AEX (N = 23)
Panel A: evidence for a pre-event period of 10 days Vp N Vb (% of events) 69.56% 80.00% 80.00% Reject H0 at 5% 60.86% 60.00% 75.00% Reject H0 at 10% 60.86% 70.00% 85.00% Mean Vb 10.2644 9.2909 11.3655 10.3765 9.5040 11.5420 Mean Vp Mean |t| 3.07 3.18 4.65
40.90% 50.00% 59.09% 9.8566 9.7442 2.31
82.60% 73.91% 78.26% 9.6097 9.8139 4.51
Panel B: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 20 days 52.17% 70.00% 65.00% 52.17% 60.00% 75.00% 60.86% 65.00% 80.00% 10.2644 9.2909 11.3655 10.2632 9.3656 11.4572 3.43 2.93 4.76
40.90% 59.09% 59.09% 9.8566 9.7765 2.73
69.56% 69.56% 78.26% 9.6097 9.7014 3.95
Panel C: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 30 days 56.52% 60.00% 70.00% 56.52% 50.00% 80.00% 60.86% 60.00% 80.00% 10.2644 9.2909 11.3655 10.2224 9.3660 11.4234 3.98 3.09 4.98
27.27% 59.09% 68.18% 9.8566 9.7972 3.33
73.91% 69.56% 78.26% 9.6097 9.6792 4.11
Notes: The table presents results for CALL option index contracts. “events” is the number of days for which an extreme event takes place during the sample period. The null hypothesis (H0) is that: [Vb = Vp], i.e. that the pre-event option volume is equal to the benchmark period volume. The percentage in the line denoted as “Reject H0 at 5%” and “Reject H0 at 10%” is the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively. For example, for the S&P100 and a pre-event period of 10 days the null is rejected for 14 out of the 23 cases, i.e. 60.86%. Mean volume (V) is defined as [ln(1 + number of call (put) contracts of index i traded on day t)]. “Mean Vb” is the mean volume for the benchmark period (i.e. −41 to − 141 days) across all events for each market. “Mean Vp” is the mean volume for pre-event period (− 10, −20, −30, days) across all events for each market. “Mean |t|” is the absolute mean t-statistic for the (H0).
10.2664 and the mean pre-event period volume is 10.3765. In other words the mean pre-event period volume is higher than the mean benchmark volume. Recall that volume is defined as [ln(1 + number of call (put) contracts of index i traded on day t)]. Finally, the last line in each panel presents the absolute mean t-statistic for the null hypothesis of equality, for each market (3.07 for the S&P100). Overall, with the exception of the CAC40, from the results in Table 2 (Panel A) we can see that for all indexes there is an increase in call option trading volume 10 days before the extreme event day: the percentage of events where pre-event volume is higher than normal ranges from about 70% for the S&P100 Index to about 83% for the AEX Index. Furthermore, this increase seems to be statistically significant in most cases: for example, for the DAX Index and the AEX Index in about 74%–75% of the events the null hypothesis of equality between pre-event and benchmark volume is rejected at the 5%. The events with abnormal pre-event trading volume are approximately 60% of the total for the rest of the indexes (at the 5% level) while the percentage is higher if one considers the 10% level of significance. In addition, the mean benchmark volume is lower compared to the mean pre-event volume, for every sample market. The mean benchmark volume ranges from 9.2909 for the FTSE100 Index to 11.3655 for the DAX30 Index while the mean pre-event volume ranges from 9.5040 for the FTSE100 Index to 11.5420 for the DAX Index. The results in Panels B and C are qualitatively similar to the results in Panel A, although slightly lower in magnitude. More specifically, when a 20-day pre-event period is considered (Panel B) the percentage of events for which the pre-event call trading volume is higher than the benchmark call trading volume ranges from 52.17% for the S&P100 Index to approximately 70% for the DAX30 and the
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AEX Indexes. The percentage of events with abnormal pre-event trading volume is approximately the same as in Panel A for the FTSE100, the DAX30 and the AEX, and slightly lower for the S&P100. The notable exception is again the CAC40: for this index in about 4 out of 10 extreme events the pre-event volume is higher than the benchmark volume. The results in Panel C are similar. The examination of put option trading volume (Table 3) suggests a pattern similar to that of the call option trading volume, for the UK, German, French, and Dutch markets. That is, for a large number of extreme events the pre-event put trading volume is higher to the benchmark put trading volume for all testing periods (e.g. 95% of the events for the DAX30 for the 10-day period, 80% for the 20-day period, and 70% for the 30-day period) and for a large number of extreme events the difference in volume is also statistically significant at the 5% level of significance (e.g. 70% of the events for the FTSE100 for the 10-day period, 75% for the 20-day period, and 75% for the 30-day period) and the 10% level of significance. For the CAC40 put options the pattern is also similar to that of the call options. The exception here is the S&P100 for which the pre-event put trading volume is higher to the benchmark put trading volume in about 44%–48% of the cases for all three testing periods, while the null hypothesis of equality is rejected in about 60%–70% of the cases.
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Table 4 Robustness test — benchmark period: 140 days. S&P100 (N = 23)
FTSE100 (N = 20)
DAX30 (N = 20)
CAC40 (N = 22)
AEX (N = 23)
Panel A: call volume for a pre-event period of 20 days Vp N Vb (% of events) 52.17% 68.00% 75.00% Reject H0 at 5% 63.63% 63.15% 80.00% Reject H0 at 10% 72.72% 68.42% 85.00% Mean Vb 10.2434 9.3439 11.3440 Mean Vp 10.2632 9.3656 11.4572 Mean |t| 3.33 2.90 5.90
36.36% 45.45% 54.54% 9.9927 9.7765 3.11
65.21% 73.91% 78.26% 9.5940 9.7014 4.17
Panel B: put volume for a pre-event period of 20 days 63.15% 80.00% Vp N Vb (% of events) 43.47% Reject H0 at 5% 72.72% 73.68% 65.00% Reject H0 at 10% 81.81% 73.68% 65.00% 10.4225 9.6134 11.4934 Mean Vb Mean Vp 10.4321 9.6594 11.6564 Mean |t| 3.93 3.82 4.70
40.90% 54.54% 54.54% 10,0739 9.9483 4.48
69.56% 82.60% 86.95% 9.7044 9.8051 5.33
Notes: The table presents results for call and put option index contracts, for a longer benchmark period of 140 days before the event (− 181 to − 41). See also Notes in Table 2.
3.2. Open interest instead of trading volume 3.1. A further robustness test: longer benchmark period The findings, so far, seem to be relatively robust to the length of the pre-event period (10, 20, and 30 days). As a second robustness test, the above analysis is performed with a different, longer, benchmark period, i.e. instead of the 100-day benchmark period, 140 days are used (− 181 to − 41). For indicative purposes, the results for a preevent period of 20 days and a benchmark period of 140 days are presented in Table 4 (the rest of the results are similar and available upon request). The modification of the benchmark period does not seem to affect the main finding of the paper; the results are qualitatively similar to the findings presented in Tables 2 and 3.
Table 3 Is there abnormal option trading volume before market shocks? Evidence from stock index put option contracts. S&P100 (N = 23)
FTSE100 (N = 20)
DAX30 (N = 20)
Panel A: evidence for a pre-event period of 10 days Vp N Vb (% of events) 47.82% 75.00% 95.00% Reject H0 at 5% 69.56% 70.00% 80.00% Reject H0 at 10% 73.91% 70.00% 80.00% Mean Vb 10.4547 9.5562 11.5155 Mean Vp 10.4886 9.7655 11.7833 Mean |t| 3.68 4.22 4.90 Panel B: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 20 days 43.47% 65.00% 80.00% 60.86% 75.00% 70.00% 73.91% 85.00% 75.00% 10.4547 9.5562 11.5155 10.4321 9.6594 11.6564 3.59 3.95 4.07
Panel C: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 30 days 43.47% 60.00% 70.00% 69.56% 75.00% 70.00% 73.91% 85.00% 70.00% 10.4547 9.5562 11.5155 10.3795 9.6358 11.6201 4.19 3.95 4.31
Notes: The table presents results for put option index contracts. See also Notes in Table 2.
CAC40 (N = 22)
AEX (N = 23)
45.55% 59.09% 63.63% 10.0862 9.9488 3.61
78.26% 78.26% 78.26% 9.7214 9.9200 4.84
40.90% 50.00% 63.63% 10.0862 9.9483 4.20
54.54% 50.00% 54.54% 10.0862 10.1547 4.26
65.21% 86.95% 86.95% 9.7214 9.8051 5.23
69.56% 73.91% 82.60% 9.7214 9.7779 5.38
Some studies have also employed open interest along with trading volume, although the impact of informed trading on open interest is less clear since most option trading strategies can either increase or decrease open interest (Jayaraman et al., 2001). For example, a long/short position on an option can increase both volume and open interest but closing a position will show on open interest only. In this sub-section trading volume is substituted with open interest in the analysis, and the main interest is on whether pre-event open interest is statistically different to the benchmark open interest. This can be an indication of abnormal trading activity before extreme events. Table 5 presents the findings for open interest in call (Panel A) and put (Panel B) index option contracts before extreme events, for a pre-event period of 10 days and a benchmark period of 100 days (the rest of the results are similar to the presented results and are available upon request). Note that the findings are similar to the findings for trading volume, with the exception of the FTSE100 Index options where the percentage of events for which the pre-event open interest is higher than the benchmark open interest is 50% of the events for both call and puts (down from 80% and 75% respectively). The important finding is that for both calls and puts and for all indexes the null hypothesis of equality is strongly rejected, Table 5 Robustness test — open interest instead of trading volume. S&P100
FTSE100
DAX30
CAC40
AEX
Panel A: call open interest for a pre-event period of 10 days Vp N Vb (% of events) 65.21% 50.00% 70.00% Reject H0 at 5% 82.60% 92.85% 100.00% Reject H0 at 10% 82.60% 92.85% 100.00% Mean Vb 11.9909 9.8432 14.1001 Mean Vp 11.9960 9.8582 14.1916 Mean |t| 11.94 19.79 29.39
66.66% 86.66% 86.66% 15.0577 15.1163 28.69
60.86% 95.65% 95.65% 12.8090 12.8647 29.29
Panel B: put open interest for a pre-event period of 10 days Vp N Vb (% of events) 43.47% 50.00% 95.00% Reject H0 at 5% 78.26% 85.71% 100.00% Reject H0 at 10% 82.60% 85.71% 100.00% Mean Vb 12.1704 10.0326 14.2675 Mean Vp 12.0868 10.0998 14.3990 Mean |t| 18.00 19.13 26.90
53.55% 100.00% 100.00% 15.0878 15.1609 33.13
69.56% 91.30% 91.30% 12.9697 13.0182 31.90
Notes: The table presents results for call and put option index contracts, as in Tables 2 and 3, with open interest instead of the number of contracts traded each day (trading volume). See also Notes in Table 2.
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indicating abnormal trading activity before extreme events. This finding reinforces the findings of the previous sub-sections.
Table 7 Call and put option volume and negative shocks. S&P100
3.3. Is call and put option volume different for positive and negative shocks? An interesting question is whether call and put option pre-event volume is different for positive and negative shocks. For instance, when extreme events are positive does call option volume increase prior to the event (implying a long strategy), or does put option volume increase (implying a short strategy)? Negative extreme events can be analyzed in a similar manner in which put activity is the long strategy and call activity is the short strategy. In order to investigate further this issue abnormal call and put pre-event volume is examined separately for positive and negative extreme events. The results for a pre-event period of 10 days (for the rest of the pre-event periods the findings are qualitatively the same) are presented in Table 6 (call and put option volume and positive shocks) and Table 7 (call and put option volume and negative shocks) and indicate that investors may use both a long and a short strategy in anticipation of an extreme event. For example, the findings for the S&P100 contract suggest that before a positive extreme event in 75% of the cases the pre-event call option volume is higher to the benchmark volume (Table 6, Panel A) implying a long strategy, while the pre-event put option volume is higher to the benchmark volume in 50% of the cases (Table 6, Panel B), implying a short strategy. For the FTSE100 the respective percentages are 67% and 66%, for the DAX 85% and 100%, for the AEX 100% and 83%. Thus, a clear preference for a long/short strategy cannot be inferred from the data. The only exception is the findings for CAC where neither call nor put abnormal volume is detected prior to positive shocks. For negative extreme events (Table 7) the findings for the S&P100 contract suggest that before a negative event in 66% of the cases the pre-event call option volume is higher to the benchmark volume (Panel A) implying a short strategy, while the pre-event put option volume is higher to the benchmark volume in 43% of the cases (Panel B), implying a long strategy. For the FTSE100 the respective percentages are 85% and 78%, for the DAX 76% and 92%, for CAC 63% and 63%, for the AEX 76% and 76%. Thus, again, a clear preference for a
Table 6 Call and put option volume and positive shocks. S&P100
FTSE100
DAX30
CAC40
AEX
Panel A: evidence for positive shocks Vp N Vb (% of events) 75.00% Reject H0 at 5% 75.00% Reject H0 at 10% 75.00% Mean Vb 10.2072 Mean Vp 10.4975 Mean |t| 4.34
and call options 67.00% 85.71% 66.66% 57.14% 83.33% 57.14% 9.1805 11.4858 9.3381 11.6337 2.27 3.04
00.00% 33.33% 50.00% 9.2277 8.6162 2.25
100.00% 83.33% 83.33% 9.6126 10.0019 5.28
Panel B: evidence for positive shocks Vp N Vb (% of events) 50.00% Reject H0 at 5% 62.50% Reject H0 at 10% 75.00% Mean Vb 10.3269 Mean Vp 10.4541 Mean |t| 2.87
and put options 66.66% 100.00% 66.66% 85.71% 66.66% 85.71% 9.3770 11.6405 9.4534 11.9019 2.68 4.94
25.00% 44.44% 44.44% 9.3892 8.8809 4.11
83.33% 66.66% 66.66% 9.6927 9.9620 5.96
Notes: The table presents results for call option (Panel A) and put option (Panel B) trading volume before positive shocks. The null hypothesis (H0) is that: [Vb = Vp], i.e. that the pre-event option volume is equal to the benchmark period volume. The percentage in the line denoted as “Reject H0 at 5%” and “Reject H0 at 10%” is the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively. Mean volume (V) is defined as [ln(1 + number of call (put) contracts of index i traded on day t]. “Mean Vb” is the mean volume for the benchmark period (i.e. −41 to − 141 days) across all events for each market. “Mean Vp” is the mean volume for pre-event period (− 10 days) across all events for each market. “Mean |t|” is the absolute mean t-statistic for the (H0).
CAC40
AEX
Panel A: evidence for negative shocks and call options Vp N Vb (% of events) 66.66% 85.71% 76.92% Reject H0 at 5% 53.33% 64.28% 84.61% Reject H0 at 10% 53.33% 64.28% 100.00% Mean Vb 10.3510 9.3383 11.3007 Mean Vp 10.3119 9.5751 11.4927 Mean |t| 3.17 3.57 5.52
FTSE100
DAX30
81.81% 53.33% 66.66% 10.3525 10.4756 2.31
76.47% 70.58% 76.47% 9.6086 9.7476 3.86
Panel B: evidence for negative shocks and put options Vp N Vb (% of events) 43.75% 78.57% 92.30% Reject H0 at 5% 68.75% 71.42% 84.61% Reject H0 at 10% 75.00% 71.42% 84.61% Mean Vb 10.4950 9.6331 11.4482 10.3235 9.8992 11.7195 Mean Vp Mean |t| 3.97 4.88 4.87
60.00% 66.66% 80.80% 10.5087 10.6239 3.09
76.47% 82.35% 82.35% 9.7315 9.9051 4.01
Notes: The table presents results for call option (Panel A) and put option (Panel B) trading volume before negative shocks. The null hypothesis (H0) is that: [Vb = Vp], i.e. that the pre-event option volume is equal to the benchmark period volume. The percentage in the line denoted as “Reject H0 at 5%” and “Reject H0 at 10%” is the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively. Mean volume (V) is defined as [ln(1 + number of call (put) contracts of index i traded on day t]. “Mean Vb” is the mean volume for the benchmark period (i.e. −41 to − 141 days) across all events for each market. “Mean Vp” is the mean volume for pre-event period (− 10 days) across all events for each market. “Mean |t|” is the absolute mean t-statistic for the (H0).
long/short strategy cannot be inferred from the data. Another interesting finding is that investors in the CAC option contracts seem to anticipate only negative extreme events. 4. Conclusion Large price shocks reflect the arrival of related significant unexpected information (fundamental or otherwise) and a significant shift in expectations. In the case of broad equity market portfolios, price shocks should be due to the arrival of significant market-wide information. Irrespective of the origin of a price shock, two interesting questions arise with respect to investor behavior around such an event. For instance, how do investors react to large stock price changes and, are stock price shocks anticipated? This paper focuses on the second question and uses option trading volume data from five major index option markets before significant market shocks to investigate whether investors anticipate market shocks. The results indicate that abnormal trading takes place in index option contracts during the period preceding significant price shocks, for a large number of events. This pattern is similar for most markets in the sample and persistent for all three pre-event periods examined (10, 20, and 30 days), for both periods used to calculate the benchmark period trading volume (100 and 140 days), and of whether open interest is used instead of trading volume. This finding is consistent with previous results for single stocks and firm-specific information (Arnold et al., 2006; Cao et al., 2005; Jayaraman et al., 2001; among others) and with arguments that suggest a link between informed trading and option markets (see, Chakravarty et al., 2004; Easley et al., 1998; Pan & Poteshman, 2006; among others).
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Easley, D., O'Hara, M., & Srinivas, P. S. (1998). Option volume and stock prices: Evidence on where informed traders trade. Journal of Finance, 53, 431−465. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance, 51, 55−84. Howe, J. S. (1986). Evidence on stock market overreaction. Financial Analysts Journal, 42, 74−77. Jayaraman, N., Frye, M., & Sabherwal, S. (2001). Informed trading around merger announcements: An empirical test using transaction volume and open interest in options market. Financial Review, 37, 45−74. Kassimatis, K., Spyrou, S., & Galariotis, E. (2008). Short-term patterns in government bond returns following market shocks: International evidence. International Review of Financial Analysis, 17, 903−924. Lasfer, M. A., Melnik, A., & Thomas, D. C. (2003). Short term reaction of stock markets in stressful circumstances. Journal of Banking and Finance, 27, 1959−1977. Lee, J., & Cheong, H. Y. (2001). Trade size and information-motivated trading in the options and stock markets. Journal of Financial and Quantitative Analysis, 36, 485−501. Mayhew, S., Sarin, A., & Shastri, K. (1995). The allocation of informed trading across related markets: An analysis of the impact of changes in equity-option margin requirements. Journal of Finance, 55, 1635−1653. Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. Review of Financial Studies, 19, 871−908. Sanders, R., & Zdanowicz, J. (1992). Target firm abnormal returns and trading volume around the initiation of change in control transactions. Journal of Financial and Quantitative Analysis, 27, 109−129. Schachter, B. (1988). Open interest in stock options around quarterly earnings announcements. Journal of Accounting Research, 26, 353−372. Schnusenberg, O., & Madura, J. (2001). Do U.S. stock market indexes over- or underreact? The Journal of Financial Research, 24, 179−204.
Contents lists available at ScienceDirect
International Review of Financial Analysis
Are broad market shocks anticipated by investors? Evidence from major equity and index options markets Spyros Spyrou ⁎ Athens University of Economics and Business, Department of Accounting and Finance, 76 Patision Str., 10434, Athens, Greece
a r t i c l e
i n f o
Article history: Received 21 February 2010 Received in revised form 19 January 2011 Accepted 4 February 2011 Available online 18 February 2011 JEL classification: G1 G12 G14
a b s t r a c t This paper examines trading activity in five index options markets before significant price shocks in the underlying asset (S&P100, FTSE100, CAC40, DAX30, and AEX). The results indicate abnormal call and put option trading volume before price shocks for a large number of cases, implying that market participants anticipate shocks and use the options market as the venue for their trading. This pattern is similar for all markets and persistent for three different pre-event periods (10, 20, and 30 days), two different periods used to calculate the benchmark period trading volume (100 and 140 days), and of whether open interest is used instead of trading volume. Further tests suggest that investors may use both long and short strategies. © 2011 Elsevier Inc. All rights reserved.
Keywords: Price shocks Market anticipation Option trading volume
1. Introduction In markets where prices incorporate news quickly and accurately asset prices will change due to the arrival of new information in the market. By implication, large price shocks reflect the arrival of related significant unexpected information and a significant shift in expectations. In the case of broad equity market portfolios, price shocks should be due to the arrival of significant market-wide information, although evidence suggests that this may not always be true. For instance, Cutler, Poterba, and Summers (1989) examine the 50 largest price shocks in the S&P500 for a 40-year period and find that few are related to particular news events or other information. Irrespective of the origin of a price shock, two questions arise: (a) how do investors react to large stock price changes and, (b) are stock price shocks unanticipated by investors? The literature is rich and offers interesting results regarding the first question. For example, Chan (2003), Benou and Richie (2003), Bremer, Hiraki, and Sweeney (1997), among others, report evidence consistent with reversals after extreme stock price movements (see also Atkins & Dyl, 1990; Bremer & Sweeney, 1991; Brown, Harlow, & Tinic, 1988; Cox & Peterson, 1994; Howe, 1986). Dennis and Strickland (2002) argue that abnormal returns following large price drops depend on the level of institutional ownership of a stock.
⁎ Tel.: + 30 210 8203169. E-mail address: [email protected]. 1057-5219/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.irfa.2011.02.002
Schnusenberg and Madura (2001) find one-day under-reaction following days on which US equity indexes experience abnormally high or low returns and significant reversals over a 60-day period for negative market shocks; Lasfer, Melnik, and Thomas (2003) employ 39 international equity market indexes and find short-term underreaction which is more pronounced for emerging markets; Kassimatis, Spyrou, and Galariotis (2008) find an initial momentum and a subsequent reversal for 17 international bond indexes. There is, however, comparatively scarce evidence in the literature regarding the second question. This paper aims to explore this issue further. More specifically, the paper addresses the question of whether investors anticipate significant broad market changes (market shocks) and trade in anticipation of the coming large price change. If price shocks are anticipated it is logical to expect that investors will take positions in the market that reflect their expectations. Moreover, it can be argued that these positions are more likely to materialize in derivative markets due to transaction costs and leverage. Indeed, as the discussion below suggests, there is a link between trading by informed investors and option markets. Thus, in this paper, spot daily return data on major European equity indexes (FTSE100, CAC40, DAX30, and AEX) are employed to identify days with abnormally high market returns, i.e. shock days, and then trading volume data from the respective index option contracts are examined in order to uncover whether there is abnormal option trading volume for the period preceding shock days. For comparative purposes and in order to establish whether there is a cross-country pattern in market participant's behavior (Fama & French, 1996) one major US index
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(S&P100) is also included in the sample. This is the first study, to the best of my knowledge, which examines whether there is an abnormal option trading volume for index option contracts during the period preceding significant price shocks. The use of options markets to uncover whether investors trade more in anticipation of market shocks is motivated by findings in the literature that examines informed trading. Chakravarty, Gulen, and Mayhew (2004), for instance, argue that investors with private information may view the options market as an ideal venue for their informed trading due to the leverage and the downside protection of options and, if that is so, price discovery can take place in the options markets; Easley, O'Hara, and Srinivas (1998) suggest that the volume of trading in the options market can help forecast equity returns (see also Booth, So, & Tse, 1999; Lee & Cheong, 2001; Mayhew, Sarin, & Shastri, 1995; Pan & Poteshman, 2006). Similar results come from a related strand in the literature that examines whether informed trading takes place in the options market prior to corporate events such as Merger and Acquisition (M&A) announcements. More specifically, a common result is that market participants with material information are likely to start abnormal trading ahead of major takeover announcements, with the option market playing an important role in information revelation during this period (see also Arnold, Erwin, Nail, & Nixon, 2006; Cao, Chen, & Griffin, 2005). For example, as Jayaraman, Frye, and Sabherwal (2001) discuss, informed market participants that anticipate the arrival of information can employ a number of option strategies prior to the event and that, irrespective of the strategy, the implication is that option trading strategies that aim to take advantage of superior information will result to increased call and put trading volumes; they find a significant increase in the trading activity of both call and put option contracts for the firms involved in an M&A prior to the announcement. The results of the paper are consistent with previous findings for firm-specific events (e.g. M&As) and suggest that, in the majority of the cases, there is an abnormal trading volume in index options contracts during the period before price shocks, implying that traders anticipate price shocks and are likely to start abnormal trading ahead of the event. This pattern is similar for all markets in the sample and persistent for all three different pre-event periods examined (10, 20, and 30 days), irrespective of the length of the periods used to calculate the benchmark period trading volume (100 and 140 days), and of whether open interest is used instead of trading volume. Further tests suggest that investors may use both long and short strategies. The findings of the paper have implications for academic research, market participants, and regulators. For example, if many marketwide shocks are not due to particular news events or other fundamental information, as Cutler et al. (1989) find, then traders may be taking positions in anticipation of a non-informational event, e.g. a shift in market sentiment. In other words, the findings may be indirect evidence that sentiment is a factor that determines equity returns. In addition, the findings in this paper could have implications for previously reported results: Lasfer et al. (2003) investigate market reaction to price shocks for 39 international equity market indexes and find that the magnitude of the shock and the abnormal post-shock reaction is significantly larger for emerging markets than for developed equity markets. It could be argued that in developed capital markets, with liquid and organized options markets where at least some traders anticipate price shocks, the options markets absorb part of the impact of shock. Thus, the lower magnitude of the shock and the lower post-shock abnormal performance for developed markets when compared to emerging markets. The implications for regulators are also important: if a market shock is due to significant fundamental information that is released to the market, the abnormal pre-event option trading volume suggests that either some investors anticipate the shock based on superior analysis or that some investors have privileged access to this in-
formation. The rest of the paper is organized as follows: Section 2 discusses the data and methodology, Section 3 presents the results, while Section 4 concludes the paper. 2. Data and methodology For the empirical analysis five well-known stock market indexes are employed as proxies for stock market activity in five major markets. More specifically, the markets are the USA, UK, France, Germany, and the Netherlands, and the indexes are the S&P100 Index, the FTSE100 Index, the CAC40 Index, the DAX30 Index, and the AEX Index, respectively. The S&P100 Index includes 100 major U.S. stocks that represent roughly half of U.S. stock market capitalization while the FTSE100 constituents are the 100 listed companies with the highest market capitalization and represent about 80% of London Stock Exchange capitalization. The CAC40 Index represents the 40 most important securities among the 100 highest capitalization stocks listed in the Euronext Paris, and the DAX is an index composed of the highest capitalization securities traded in Frankfurt Stock Exchange. Finally, the AEX Index is composed of the 25 most actively traded stocks listed in the Amsterdam Exchange. Note that the Amsterdam Exchange is one of the four constituents markets of Euronext (along with Paris, Lisbon, and Brussels), which is the second largest securities market in Europe after the London Stock Exchange. Overall, the market indexes chosen for the analysis include heavily traded securities in the USA and major European equity markets. The selected indexes all have option contracts available; Table 1 (Panel A) presents a list of the indexes and exchanges at which the sample option contracts on the respective indexes are traded, as well as the available sample period. For every index, daily price data and daily (call and put) option trading volume data are collected for the period between November 1994 and November 2009, i.e. fifteen years of data, or 3775 daily observations. The data are collected from the Datastream database. The unconditional daily change for the equity index i on day t is computed as follows: ri;t =
Pi;t −Pi;t−1 : Pi;t−1
ð1Þ
In Eq. (1), (ri,t) as the change between today's and previous day's closing price (P). Descriptive statistics for daily returns for the sample indexes are presented in Table 1, Panel B. Average daily returns are of similar magnitude in all markets and are about 0.02% for the FTSE, DAX, and AEX, 0.03% for the S&P100, and 0.02% for the CAC. The standard deviation ranges from 1.23% for the FTSE to 1.55% for the DAX. The kurtosis coefficients are positive and relatively high, indicating leptokurtic return distributions with fatter tails and that a large part of the return variance may be due to non-frequent extreme deviations. The skewness coefficients are positive and close to zero with the exception of the DAX which is comparatively higher; suggesting that the return distributions are right-skewed. Daily call and put option trading volume data are collected from the Datastream database. Daily option trading volume is defined as the number of option contracts traded on each day (total cumulative volume for all individual option series). The sample period is between November 1994 and November 2009. Table 1 presents descriptive statistics for daily option trading volumes for call contracts (Panel C) and put contracts (Panel D). As can be seen from Panel C, daily call option trading volume ranges from an average of 19,108 contracts for the FTSE Index to an average of 100,859 contracts for the DAX Index; the standard deviation ranges from 16,306 contracts for the FTSE Index to 86,983 contracts for the CAC Index. The kurtosis and skewness coefficients indicate leptokurtic and right-skewed distributions. Similarly daily put option trading volume ranges from an
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Table 1 Data and descriptive statistics. Panel A: markets and indexes Country
Index
Index option contract
Start–end date
Currency
USA UK Germany France Netherlands
S&P100 FTASE100 DAX30 CAC40 AEX
CBOE LIFFE EUREX MONEP AMS
1994–2009 1994–2009 1994–2009 1994–2009 1994–2009
US dollar Sterling Euro Euro Euro
S&P100 Panel B: descriptive daily return statistics for indexes Mean R 0.0003 St. deviation 0.0129 Kurtosis 7.49 Skewness 0.03
FTSE100
DAX30
0.0002 0.0123 6.35 0.06
0.0002 0.0155 17.17 0.80
CAC40
AEX 0.0002 0.0150 6.13 0.07
0.0002 0.0152 6.06 0.05
Panel C: call option daily volume descriptive statistics Mean Vt 46,548 St. deviation 38,433 Kurtosis 9.69 Skewness 2.56
19,108 16,306 11,83 2.07
100,859 57,545 3.92 1.57
66,405 86,983 5.44 1.88
21,944 18,367 3.46 1.63
Panel D: put options daily volume descriptive statistics Mean Vt 55,020 St. deviation 45,998 Kurtosis 8.11 Skewness 2.43
24,706 20,503 2.38 1.32
116,535 70,366 4.63 1.78
80,569 107,037 3,61 1.79
24,419 19,989 3.26 1.52
average of 24,419 contracts for the AEX Index to an average of 116,535 contracts for the DAX Index; the standard deviation ranges from 19,989 contracts for the AEX Index to 107,037 contracts for the CAC Index. The kurtosis and skewness coefficients indicate leptokurtic and right-skewed distributions for put trading volume as well. 2.1. Definition of an extreme event The relevant literature offers a variety of definitions for an extreme price movement. For example, Bremer and Sweeney (1991) suggest that an extreme price movement for a stock is when the stock price drops by at least 10%, Howe (1986) employs weekly price changes of more than 50%, Atkins and Dyl (1990) use the largest price change in a 300-day window, Benou and Richie (2003) use a rule of 20% during a specific month to define a significant stock price decline, Dennis and Strickland (2002) define large price declines as days where the absolute value of the market's return is down 2% or more, Schnusenberg and Madura (2001) use the top (bottom) 10 percentile of computed abnormal daily returns to define winner (loser) days for stock market indexes, among others. Some studies employ the market model to adjust for risk; however, this relies on the assumption that the market model is valid. Lasfer et al. (2003), who also study equity indexes, point out that some of the above definitions cannot account appropriately for issues such as the varying return volatility form asset to asset and employ a definition that is based on the distance of a certain observation from the mean value. For instance, an extreme event occurs on a day where the asset return on that day is above or below two standard deviations of the average return computed over some previous reference period.1 This paper employs a methodology similar to Lasfer et al. (2003) to identify an extreme event: a significant price shock occurs on any day where the index return is above or below three standard deviations of the average daily index return computed over [− 60 to −11] days before the given day. The window ends 10 trading days prior to the event day in order to avoid possible price lead-up preceding the shocks. The standard deviation 1 This approach also accounts for time-variation in risk premia that could lead to serial correlation in returns (Ball & Kothari, 1989; Chan, 1988).
for day t is also computed from the observations between day t-60 and day t-11. 2.2. Estimating abnormal option trading volume In order to examine whether there is an abnormal level of option trading volume during the period preceding large price shocks a comparison period approach is used, as in Jayaraman et al. (2001). That is, the pre-event option trading volume is compared to the trading volume of a benchmark period (see also Amin & Lee, 1997; Cao et al., 2005; Schachter, 1988). Because of the variation in the number of option contracts traded daily, a logarithmic transformation of the option trading volume is used (see Sanders & Zdanowicz, 1992; among others) and trading volume is defined as: Vi;t = lnð1 + number of call ðputÞ contracts on index i traded on day t Þ:
ð2Þ In Eq. (2), i = S&P100, FTSE100, CAC40, DAX30, and AEX Indexes. The benchmark period trading volume is defined as the average trading volume for a 100-day period preceding the event and ending 41 days before the event (− 141 to −41): V b;i =
−41 1 ∑ V : 100 t = −140 it
ð3Þ
The pre-event option trading volume, or testing period volume, is defined as the average trading volume of the two trading weeks (10 trading days) immediately preceding the day of the large price change: V p;i =
0 1 ∑ V : 10 t = −10 it
ð4Þ
As a robustness test, a longer benchmark period (−181 to −41) and two additional testing periods (−20 and −30 days relative to the event) are also employed in the study. The same approach is adopted in analyzing trading volume for both call and put option contracts.
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Note that there are some cases of price-shock clustering, i.e. periods where successive price shocks occur close to each other. This could affect the benchmark or testing period trading volume (or both). In order to avoid biased estimates of trading volume, the above analysis is performed only for large price shock days that have a distance of at least 80 trading days between them. If market participants anticipate a price shock and there is a link between option markets and trading in anticipation of a market shock then we should observe abnormal trading volume during the period before the price shock, in other words, the pre-event option trading volume should be different (higher) to the trading volume of the benchmark period. Thus, the null hypothesis is that the pre-event option volume is equal to the benchmark period volume: H0: Vp,i = Vb, i, and the alternative hypothesis is that the pre-event option volume is different to the benchmark period volume (H1: Vp,i ≠ Vb,i). The rejection of the null hypothesis then implies that there is abnormal option trading volume before the event. Standard t-tests are employed to investigate the significance of difference in volume between benchmark and pre-event periods.2 3. Results Table 2 presents the results for call option contracts for the five markets and for a benchmark period of 100 days. Panel A presents the results for a pre-event period of 10 days, Panel B presents the results for a pre-event period of 20 days, and Panel C presents the results for a pre-event period of 30 days. The first line in each panel presents the five markets and the number of events for each market during the sample period (N). For example, for the S&P100 Index there are 23 extreme event days included in the analysis during the sample period, i.e. 23 days where the unconditional price change of the index is above or below three standard deviations of the average daily index return. The number of events is similar for all markets. The second line in each panel presents the percentage of events for which the pre-event call option trading volume is higher than the benchmark call option trading volume for each market (Vp,i N Vb,i), i.e. the percentage of events for which pre-event trading volume is higher than normal. For instance, for the S&P100 and a pre-event period of 10 days in 69.56% of events (16 out of 23) the pre-event call option trading volume is higher from the benchmark period volume. The third and fourth lines in each panel present the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively (termed as “Reject H0 at 5%” and “Reject H0 at 10%” respectively). For the S&P100 the null hypothesis of equality between pre-event and benchmark option trading volume is rejected in 60.86% of events (14 out of 23) for both the 5% and the 10% level of significance. The next two lines in each panel present the mean benchmark volume (“Mean Vb”) for the benchmark period of 100 days (− 41 to −141) across all events for each market and the mean pre-event volume (“Mean Vp”) across all events for each market. For the S&P100 and a pre-event period of 10 days the mean benchmark volume is 2 It could be argued that since shorter-term options tend to spike in volume as they approach maturity, a finding of increased volume could be just options coming due. A cross-check of events and expirations for the sample period reveals that only a small fraction of events take place during the two-week period before expiration. For example for the S&P only 5 out of 23 events, for FTSE100 3 out of 20 events, for the DAX 4 out of 20 events, for the AEX 4 out of 23 events, and for the CAC 7 out of 22 events. Also, this issue is indirectly addressed by the research methodology and the robustness tests: three pre-event periods are examined (10, 20, and 30 days) and two benchmark periods (100 and 140 days). The argument about higher volume for shortterm options holds for the benchmark periods as well, i.e. benchmark volumes include periods of higher volume from this reason and they too could appear higher. Assuming that a near-maturity option is an option that expires in one to two weeks (i.e. 10 trading days) and that the event coincides with expiration, the choice of longer preevent periods (20 and 30 trading days) as a robustness test should reveal a potential false interpretation of the results.
Table 2 Is there abnormal option trading volume before market shocks? Evidence from stock index call option contracts. S&P100 (N = 23)
FTSE100 (N = 20)
DAX30 (N = 20)
CAC40 (N = 22)
AEX (N = 23)
Panel A: evidence for a pre-event period of 10 days Vp N Vb (% of events) 69.56% 80.00% 80.00% Reject H0 at 5% 60.86% 60.00% 75.00% Reject H0 at 10% 60.86% 70.00% 85.00% Mean Vb 10.2644 9.2909 11.3655 10.3765 9.5040 11.5420 Mean Vp Mean |t| 3.07 3.18 4.65
40.90% 50.00% 59.09% 9.8566 9.7442 2.31
82.60% 73.91% 78.26% 9.6097 9.8139 4.51
Panel B: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 20 days 52.17% 70.00% 65.00% 52.17% 60.00% 75.00% 60.86% 65.00% 80.00% 10.2644 9.2909 11.3655 10.2632 9.3656 11.4572 3.43 2.93 4.76
40.90% 59.09% 59.09% 9.8566 9.7765 2.73
69.56% 69.56% 78.26% 9.6097 9.7014 3.95
Panel C: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 30 days 56.52% 60.00% 70.00% 56.52% 50.00% 80.00% 60.86% 60.00% 80.00% 10.2644 9.2909 11.3655 10.2224 9.3660 11.4234 3.98 3.09 4.98
27.27% 59.09% 68.18% 9.8566 9.7972 3.33
73.91% 69.56% 78.26% 9.6097 9.6792 4.11
Notes: The table presents results for CALL option index contracts. “events” is the number of days for which an extreme event takes place during the sample period. The null hypothesis (H0) is that: [Vb = Vp], i.e. that the pre-event option volume is equal to the benchmark period volume. The percentage in the line denoted as “Reject H0 at 5%” and “Reject H0 at 10%” is the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively. For example, for the S&P100 and a pre-event period of 10 days the null is rejected for 14 out of the 23 cases, i.e. 60.86%. Mean volume (V) is defined as [ln(1 + number of call (put) contracts of index i traded on day t)]. “Mean Vb” is the mean volume for the benchmark period (i.e. −41 to − 141 days) across all events for each market. “Mean Vp” is the mean volume for pre-event period (− 10, −20, −30, days) across all events for each market. “Mean |t|” is the absolute mean t-statistic for the (H0).
10.2664 and the mean pre-event period volume is 10.3765. In other words the mean pre-event period volume is higher than the mean benchmark volume. Recall that volume is defined as [ln(1 + number of call (put) contracts of index i traded on day t)]. Finally, the last line in each panel presents the absolute mean t-statistic for the null hypothesis of equality, for each market (3.07 for the S&P100). Overall, with the exception of the CAC40, from the results in Table 2 (Panel A) we can see that for all indexes there is an increase in call option trading volume 10 days before the extreme event day: the percentage of events where pre-event volume is higher than normal ranges from about 70% for the S&P100 Index to about 83% for the AEX Index. Furthermore, this increase seems to be statistically significant in most cases: for example, for the DAX Index and the AEX Index in about 74%–75% of the events the null hypothesis of equality between pre-event and benchmark volume is rejected at the 5%. The events with abnormal pre-event trading volume are approximately 60% of the total for the rest of the indexes (at the 5% level) while the percentage is higher if one considers the 10% level of significance. In addition, the mean benchmark volume is lower compared to the mean pre-event volume, for every sample market. The mean benchmark volume ranges from 9.2909 for the FTSE100 Index to 11.3655 for the DAX30 Index while the mean pre-event volume ranges from 9.5040 for the FTSE100 Index to 11.5420 for the DAX Index. The results in Panels B and C are qualitatively similar to the results in Panel A, although slightly lower in magnitude. More specifically, when a 20-day pre-event period is considered (Panel B) the percentage of events for which the pre-event call trading volume is higher than the benchmark call trading volume ranges from 52.17% for the S&P100 Index to approximately 70% for the DAX30 and the
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AEX Indexes. The percentage of events with abnormal pre-event trading volume is approximately the same as in Panel A for the FTSE100, the DAX30 and the AEX, and slightly lower for the S&P100. The notable exception is again the CAC40: for this index in about 4 out of 10 extreme events the pre-event volume is higher than the benchmark volume. The results in Panel C are similar. The examination of put option trading volume (Table 3) suggests a pattern similar to that of the call option trading volume, for the UK, German, French, and Dutch markets. That is, for a large number of extreme events the pre-event put trading volume is higher to the benchmark put trading volume for all testing periods (e.g. 95% of the events for the DAX30 for the 10-day period, 80% for the 20-day period, and 70% for the 30-day period) and for a large number of extreme events the difference in volume is also statistically significant at the 5% level of significance (e.g. 70% of the events for the FTSE100 for the 10-day period, 75% for the 20-day period, and 75% for the 30-day period) and the 10% level of significance. For the CAC40 put options the pattern is also similar to that of the call options. The exception here is the S&P100 for which the pre-event put trading volume is higher to the benchmark put trading volume in about 44%–48% of the cases for all three testing periods, while the null hypothesis of equality is rejected in about 60%–70% of the cases.
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Table 4 Robustness test — benchmark period: 140 days. S&P100 (N = 23)
FTSE100 (N = 20)
DAX30 (N = 20)
CAC40 (N = 22)
AEX (N = 23)
Panel A: call volume for a pre-event period of 20 days Vp N Vb (% of events) 52.17% 68.00% 75.00% Reject H0 at 5% 63.63% 63.15% 80.00% Reject H0 at 10% 72.72% 68.42% 85.00% Mean Vb 10.2434 9.3439 11.3440 Mean Vp 10.2632 9.3656 11.4572 Mean |t| 3.33 2.90 5.90
36.36% 45.45% 54.54% 9.9927 9.7765 3.11
65.21% 73.91% 78.26% 9.5940 9.7014 4.17
Panel B: put volume for a pre-event period of 20 days 63.15% 80.00% Vp N Vb (% of events) 43.47% Reject H0 at 5% 72.72% 73.68% 65.00% Reject H0 at 10% 81.81% 73.68% 65.00% 10.4225 9.6134 11.4934 Mean Vb Mean Vp 10.4321 9.6594 11.6564 Mean |t| 3.93 3.82 4.70
40.90% 54.54% 54.54% 10,0739 9.9483 4.48
69.56% 82.60% 86.95% 9.7044 9.8051 5.33
Notes: The table presents results for call and put option index contracts, for a longer benchmark period of 140 days before the event (− 181 to − 41). See also Notes in Table 2.
3.2. Open interest instead of trading volume 3.1. A further robustness test: longer benchmark period The findings, so far, seem to be relatively robust to the length of the pre-event period (10, 20, and 30 days). As a second robustness test, the above analysis is performed with a different, longer, benchmark period, i.e. instead of the 100-day benchmark period, 140 days are used (− 181 to − 41). For indicative purposes, the results for a preevent period of 20 days and a benchmark period of 140 days are presented in Table 4 (the rest of the results are similar and available upon request). The modification of the benchmark period does not seem to affect the main finding of the paper; the results are qualitatively similar to the findings presented in Tables 2 and 3.
Table 3 Is there abnormal option trading volume before market shocks? Evidence from stock index put option contracts. S&P100 (N = 23)
FTSE100 (N = 20)
DAX30 (N = 20)
Panel A: evidence for a pre-event period of 10 days Vp N Vb (% of events) 47.82% 75.00% 95.00% Reject H0 at 5% 69.56% 70.00% 80.00% Reject H0 at 10% 73.91% 70.00% 80.00% Mean Vb 10.4547 9.5562 11.5155 Mean Vp 10.4886 9.7655 11.7833 Mean |t| 3.68 4.22 4.90 Panel B: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 20 days 43.47% 65.00% 80.00% 60.86% 75.00% 70.00% 73.91% 85.00% 75.00% 10.4547 9.5562 11.5155 10.4321 9.6594 11.6564 3.59 3.95 4.07
Panel C: evidence for a Vp N Vb (% of events) Reject H0 at 5% Reject H0 at 10% Mean Vb Mean Vp Mean |t|
pre-event period of 30 days 43.47% 60.00% 70.00% 69.56% 75.00% 70.00% 73.91% 85.00% 70.00% 10.4547 9.5562 11.5155 10.3795 9.6358 11.6201 4.19 3.95 4.31
Notes: The table presents results for put option index contracts. See also Notes in Table 2.
CAC40 (N = 22)
AEX (N = 23)
45.55% 59.09% 63.63% 10.0862 9.9488 3.61
78.26% 78.26% 78.26% 9.7214 9.9200 4.84
40.90% 50.00% 63.63% 10.0862 9.9483 4.20
54.54% 50.00% 54.54% 10.0862 10.1547 4.26
65.21% 86.95% 86.95% 9.7214 9.8051 5.23
69.56% 73.91% 82.60% 9.7214 9.7779 5.38
Some studies have also employed open interest along with trading volume, although the impact of informed trading on open interest is less clear since most option trading strategies can either increase or decrease open interest (Jayaraman et al., 2001). For example, a long/short position on an option can increase both volume and open interest but closing a position will show on open interest only. In this sub-section trading volume is substituted with open interest in the analysis, and the main interest is on whether pre-event open interest is statistically different to the benchmark open interest. This can be an indication of abnormal trading activity before extreme events. Table 5 presents the findings for open interest in call (Panel A) and put (Panel B) index option contracts before extreme events, for a pre-event period of 10 days and a benchmark period of 100 days (the rest of the results are similar to the presented results and are available upon request). Note that the findings are similar to the findings for trading volume, with the exception of the FTSE100 Index options where the percentage of events for which the pre-event open interest is higher than the benchmark open interest is 50% of the events for both call and puts (down from 80% and 75% respectively). The important finding is that for both calls and puts and for all indexes the null hypothesis of equality is strongly rejected, Table 5 Robustness test — open interest instead of trading volume. S&P100
FTSE100
DAX30
CAC40
AEX
Panel A: call open interest for a pre-event period of 10 days Vp N Vb (% of events) 65.21% 50.00% 70.00% Reject H0 at 5% 82.60% 92.85% 100.00% Reject H0 at 10% 82.60% 92.85% 100.00% Mean Vb 11.9909 9.8432 14.1001 Mean Vp 11.9960 9.8582 14.1916 Mean |t| 11.94 19.79 29.39
66.66% 86.66% 86.66% 15.0577 15.1163 28.69
60.86% 95.65% 95.65% 12.8090 12.8647 29.29
Panel B: put open interest for a pre-event period of 10 days Vp N Vb (% of events) 43.47% 50.00% 95.00% Reject H0 at 5% 78.26% 85.71% 100.00% Reject H0 at 10% 82.60% 85.71% 100.00% Mean Vb 12.1704 10.0326 14.2675 Mean Vp 12.0868 10.0998 14.3990 Mean |t| 18.00 19.13 26.90
53.55% 100.00% 100.00% 15.0878 15.1609 33.13
69.56% 91.30% 91.30% 12.9697 13.0182 31.90
Notes: The table presents results for call and put option index contracts, as in Tables 2 and 3, with open interest instead of the number of contracts traded each day (trading volume). See also Notes in Table 2.
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indicating abnormal trading activity before extreme events. This finding reinforces the findings of the previous sub-sections.
Table 7 Call and put option volume and negative shocks. S&P100
3.3. Is call and put option volume different for positive and negative shocks? An interesting question is whether call and put option pre-event volume is different for positive and negative shocks. For instance, when extreme events are positive does call option volume increase prior to the event (implying a long strategy), or does put option volume increase (implying a short strategy)? Negative extreme events can be analyzed in a similar manner in which put activity is the long strategy and call activity is the short strategy. In order to investigate further this issue abnormal call and put pre-event volume is examined separately for positive and negative extreme events. The results for a pre-event period of 10 days (for the rest of the pre-event periods the findings are qualitatively the same) are presented in Table 6 (call and put option volume and positive shocks) and Table 7 (call and put option volume and negative shocks) and indicate that investors may use both a long and a short strategy in anticipation of an extreme event. For example, the findings for the S&P100 contract suggest that before a positive extreme event in 75% of the cases the pre-event call option volume is higher to the benchmark volume (Table 6, Panel A) implying a long strategy, while the pre-event put option volume is higher to the benchmark volume in 50% of the cases (Table 6, Panel B), implying a short strategy. For the FTSE100 the respective percentages are 67% and 66%, for the DAX 85% and 100%, for the AEX 100% and 83%. Thus, a clear preference for a long/short strategy cannot be inferred from the data. The only exception is the findings for CAC where neither call nor put abnormal volume is detected prior to positive shocks. For negative extreme events (Table 7) the findings for the S&P100 contract suggest that before a negative event in 66% of the cases the pre-event call option volume is higher to the benchmark volume (Panel A) implying a short strategy, while the pre-event put option volume is higher to the benchmark volume in 43% of the cases (Panel B), implying a long strategy. For the FTSE100 the respective percentages are 85% and 78%, for the DAX 76% and 92%, for CAC 63% and 63%, for the AEX 76% and 76%. Thus, again, a clear preference for a
Table 6 Call and put option volume and positive shocks. S&P100
FTSE100
DAX30
CAC40
AEX
Panel A: evidence for positive shocks Vp N Vb (% of events) 75.00% Reject H0 at 5% 75.00% Reject H0 at 10% 75.00% Mean Vb 10.2072 Mean Vp 10.4975 Mean |t| 4.34
and call options 67.00% 85.71% 66.66% 57.14% 83.33% 57.14% 9.1805 11.4858 9.3381 11.6337 2.27 3.04
00.00% 33.33% 50.00% 9.2277 8.6162 2.25
100.00% 83.33% 83.33% 9.6126 10.0019 5.28
Panel B: evidence for positive shocks Vp N Vb (% of events) 50.00% Reject H0 at 5% 62.50% Reject H0 at 10% 75.00% Mean Vb 10.3269 Mean Vp 10.4541 Mean |t| 2.87
and put options 66.66% 100.00% 66.66% 85.71% 66.66% 85.71% 9.3770 11.6405 9.4534 11.9019 2.68 4.94
25.00% 44.44% 44.44% 9.3892 8.8809 4.11
83.33% 66.66% 66.66% 9.6927 9.9620 5.96
Notes: The table presents results for call option (Panel A) and put option (Panel B) trading volume before positive shocks. The null hypothesis (H0) is that: [Vb = Vp], i.e. that the pre-event option volume is equal to the benchmark period volume. The percentage in the line denoted as “Reject H0 at 5%” and “Reject H0 at 10%” is the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively. Mean volume (V) is defined as [ln(1 + number of call (put) contracts of index i traded on day t]. “Mean Vb” is the mean volume for the benchmark period (i.e. −41 to − 141 days) across all events for each market. “Mean Vp” is the mean volume for pre-event period (− 10 days) across all events for each market. “Mean |t|” is the absolute mean t-statistic for the (H0).
CAC40
AEX
Panel A: evidence for negative shocks and call options Vp N Vb (% of events) 66.66% 85.71% 76.92% Reject H0 at 5% 53.33% 64.28% 84.61% Reject H0 at 10% 53.33% 64.28% 100.00% Mean Vb 10.3510 9.3383 11.3007 Mean Vp 10.3119 9.5751 11.4927 Mean |t| 3.17 3.57 5.52
FTSE100
DAX30
81.81% 53.33% 66.66% 10.3525 10.4756 2.31
76.47% 70.58% 76.47% 9.6086 9.7476 3.86
Panel B: evidence for negative shocks and put options Vp N Vb (% of events) 43.75% 78.57% 92.30% Reject H0 at 5% 68.75% 71.42% 84.61% Reject H0 at 10% 75.00% 71.42% 84.61% Mean Vb 10.4950 9.6331 11.4482 10.3235 9.8992 11.7195 Mean Vp Mean |t| 3.97 4.88 4.87
60.00% 66.66% 80.80% 10.5087 10.6239 3.09
76.47% 82.35% 82.35% 9.7315 9.9051 4.01
Notes: The table presents results for call option (Panel A) and put option (Panel B) trading volume before negative shocks. The null hypothesis (H0) is that: [Vb = Vp], i.e. that the pre-event option volume is equal to the benchmark period volume. The percentage in the line denoted as “Reject H0 at 5%” and “Reject H0 at 10%” is the percentage of events for which the null is rejected at the 5% and 10% levels of significance, respectively. Mean volume (V) is defined as [ln(1 + number of call (put) contracts of index i traded on day t]. “Mean Vb” is the mean volume for the benchmark period (i.e. −41 to − 141 days) across all events for each market. “Mean Vp” is the mean volume for pre-event period (− 10 days) across all events for each market. “Mean |t|” is the absolute mean t-statistic for the (H0).
long/short strategy cannot be inferred from the data. Another interesting finding is that investors in the CAC option contracts seem to anticipate only negative extreme events. 4. Conclusion Large price shocks reflect the arrival of related significant unexpected information (fundamental or otherwise) and a significant shift in expectations. In the case of broad equity market portfolios, price shocks should be due to the arrival of significant market-wide information. Irrespective of the origin of a price shock, two interesting questions arise with respect to investor behavior around such an event. For instance, how do investors react to large stock price changes and, are stock price shocks anticipated? This paper focuses on the second question and uses option trading volume data from five major index option markets before significant market shocks to investigate whether investors anticipate market shocks. The results indicate that abnormal trading takes place in index option contracts during the period preceding significant price shocks, for a large number of events. This pattern is similar for most markets in the sample and persistent for all three pre-event periods examined (10, 20, and 30 days), for both periods used to calculate the benchmark period trading volume (100 and 140 days), and of whether open interest is used instead of trading volume. This finding is consistent with previous results for single stocks and firm-specific information (Arnold et al., 2006; Cao et al., 2005; Jayaraman et al., 2001; among others) and with arguments that suggest a link between informed trading and option markets (see, Chakravarty et al., 2004; Easley et al., 1998; Pan & Poteshman, 2006; among others).
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