The 2016 U.S. presidential election and the Stock, FX and VIX markets

North American Journal of Economics and Finance 42 (2017) 546–563

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North American Journal of Economics and Finance j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e c o fi n

The 2016 U.S. presidential election and the Stock, FX and VIX markets Imlak Shaikh Department of Accounting and Finance, Management Development Institute Gurgaon, Gurugram, Haryana 122007, India

a r t i c l e

i n f o

Article history: Received 21 June 2017 Received in revised form 28 August 2017 Accepted 30 August 2017

JEL Classification: G11 G14 G15 Keywords: Stock market U.S. presidential election Stock returns FX market Implied volatility index

a b s t r a c t The U.S. presidential election is one of the global political events that have the profound effects on the Global Financial Markets (GFMs). The aim of the study is to examine Stock, FX and VIX markets under the U.S. presidential election 2016. The findings strongly suggest that ‘U.S. presidential election effects’ hold in equity and FX markets across the GFMs. The empirical outcome signifies that markets are inefficient in the short-run (election year) and allows the opportunity to make abnormal gains from the market. The ‘Republican president elect’ has shown negative effects on the Nifty50, S&PASX200, and IPC equity markets while FTSE100, DJIA, Top40, EuroStoxx50 and Nikkei225 have reported positive returns. The Trumps’ proposal on international trade has caused major loss in the global currency market against the U.S. dollar. The investors’ sentiment to be measured extremely low on the poll announcement day but VXJ and AXVI based market participants have shown very high degree of concern. The Bearish-run election effects to be observed during the election period while post election period has shown Bull-run effects (Asiapacific markets). Ó 2017 Elsevier Inc. All rights reserved.

1. Introduction There is no reason to believe that polices and regulations imposed by the president and administration do have significant effects on the economy and general sense of well-being of the citizen. The performance of the financial market is linked to the performance of the economy, and performance of the economy influenced by the political uncertainty. Those government policies and actions lead to positive effects on the economy, lead to positive effect on the stock market. The macroeconomic fundamentals such as lower general price level, low interest rate, and low taxes contributes more money supply in the system, and which results in strong economic growth, business gain and growing economy. These positive trends give positive shock to the stock market and strong optimism among the investors. At this point, one can say that elections and stock market are closely associated; as the presidential election approaches-market enters into temporarily turbulence phase. The presidential election of U.S. 2016 holds special importance for the domestic and global investor community, and those who deal in the Stock, FX, and volatility as an asset class. The starting month ‘January’ of the election year starts with dominant uncertainty, ‘Who will be the next president elect’? There is general wisdom, more the uncertainty about president elect, results in poor performance of the market during election period. Once the winner is declared typically stock market enters in the positive direction. Indeed, the election year generally exhibit a bullish rally in the stock market. It has been

E-mail addresses: [email protected], [email protected] http://dx.doi.org/10.1016/j.najef.2017.08.014 1062-9408/Ó 2017 Elsevier Inc. All rights reserved.

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observed over the last 28 presidential election years, the stock market rallied during September and October 16-times, and slumped 12 –times (e.g. see, early evidences Niederhoffer, Gibbs, & Bullock, 1970 and Nordhaus, 1975). This explains that stock market closely follows the outcomes and debates of presidential election years. The financial dailies report that markets are outperforming under Democratic presidents (WSJ., 2016; WSJ, 2016). The S&P 500 yielded 12.18% positive returns under Obamas’ presidentialship 2012, but during May 2015 to May 2016 market has reported negative returns with
2.79%. There is a lack of theoretical guidance in explaining the casual relationship between financial markets and political uncertainty. For example, Pástor and Veronesi (2012, 2013) attempt to explain political uncertainty and stock market behavior. Moreover, Baker, Bloom, and Davis (2016) construct the economic policy uncertainty index (EPU Index) which measures the policy related economic uncertainty and they examin employment, investment, stock price volatility etc. Pástor and Veronesi (2013) build general equilibrium model by taking into account the political uncertainty and show that equity risk premium is commanded by governments’ policy uncertainty. They document that stock volatility and correlation remain higher under weak economic conditions. They describe that assets prices are effected by three types of shocks: Capital Shocks, Impact Shocks and Political Shocks. The first two shocks are due to aggregate capital shocks also known as fundamental economic shock. The third one is political shock due to uncertainty of future plan of government and are orthogonal to economic shocks. In a nutshell, the equity risk premium mainly consists of these three components as discussed above. The demand for excess equity risk premium purely determined by debates (presidential election debates) and negotiations of the future government. More specifically, risk premium is considered as political risk premiums associated with agents’ belief about the new government policy. It is believed that political risk premium remains lower when economic conditions are stronger. The present study documents the presidential election debates (PEDs) to analyze the effects of political debates and negotiations on the investors’ sentiment. The equity risk premium and political uncertainty are strongly associated. The weak economic conditions not only influence the risk premium but also the assets’ returns and volatility. The assets’ volatility will be higher when potential new government proposals are perceived as more heterogeneous a priori. The empirical findings support the high volatility and increased level of expected stock market volatility (implied volatility) during the political debates and negotiations. In order to derive robust empirical outcomes, the study incorporates the policy uncertainty index also known as economic policy uncertainty index (EPUI) as developed by Baker et al. (2016). The EPU index and presidential election year 2016 has been considered to examine the global financial markets in terms of Stock, FX and volatility index (VIX). It is believed that when future policy is uncertain and economy is weak it leads to higher degree of political uncertainty (i.e. spikes in EPU index), and also adversely affecting on financial assets. Moreover, it commands the equity risk premium. The findings are consistent with these statements. There is a dearth of studies (e.g. Allvine & O’Neill, 1980; Bowen, Castanias, & Daley, 1983; Herbst & Slinkman, 1984; Hill & Schneeweis, 1983; Hobbs & Riley, 1984; Huang, 1985; Zivney & Marcus, 1989; Homaifar, Randolph, Helms, and Haddad, 1988) explore, the stock market returns, stock price Intra-Industry effects, U.S. Treasury market and Defense stock following the presidential election cycle. The abovementioned studies have well documented the effects of U.S. presidential elections on the stock and treasury market. Moreover, Foerster and Schmitz (1997), Browning (2000), Pantzalis, Stangeland, and Turtle (2000) and Nippani, Liu, and Schulman (2001) describes how uncertainty of election polls effects on the domestic and global stock market in stipulations of stock returns and Treasury defaults. Nippani and Medlin (2002) and Nippani and Arize (2005) report that delay in the declaration of presidential candidate after the election, adversely effected the stock market. The above early evidences clearly speak that presidential elections of U.S. affects stock market returns. Some of the interesting attempts e.g. Nordhaus (1975) and MacRae (1977), Allivne and O’Neil (1980) and Herbst et al. (1984) has first time addressed the economic consequences of presidential elections, and stock prices and elections cycles. Herbst and Slinkman (1984) examine the stock market and U.S. presidential elections for the period 1926–1977. The study observed the existence of both two and four-year election cycles. The political economic cycle affects the stock market returns in 48-month cycle. Huang (1985) describes the U.S. equity market under the regime of Republican and Democrat and present the evidences on Four-year cycle and cycle based strategies. By extending the work of Allvine and O’Neill (1980) Huang (1985) also confirmed the existence of political cycle in the stock market. Findings reveal that investor take lower risk through a switching strategy rather than in-stock strategy. Not only the election stock markets there are some studies e.g. Herron, Lavin, Cram, and Silver (1999) and Henry (2000) describes the effects of presidential elections on American economic sectors, defense policy, environmental issues, FII’s, stock market liberalization and cost of equity capital. The empirical results present adequate evidences that presidential elections influenced the above areas. Hansen, Schmidt, and Strobel (2004) explores the manipulation in the political stock markets using the Berlin 1999 election data and conclude Political Stock Market is subject to manipulation. To control the manipulation, market imperfection should be reduced and filtering of the prognosis advised. Moreover, Brüggelambert (2004) examines the political stock market in terms of information and market efficiency and it hold good in U.S. but did not performs as well in the German market. The studies for the Canadian, Mexican, and Brazilian stock market on election stock market (ESM) e.g. Nippani and Arize (2005) and Jensen and Schmith (2005). The studies document the impact of delay in the declaration of president after the election and the information contained in the elections campaign and its impact on the Canadian, Mexican and Brazilian economy.

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Some of the earlier studies (e.g. Hood & Nofsinger, 2008; Füss & Bechtel, 2008; Mattozzi, 2008 and Sturm, 2009) on the ‘‘Politics & stock market performance: 2002 German elections”, ‘‘PEC & January effects”, and ‘‘Hedge on the political uncertainty”. These are the work nexuses between the market anomaly/in-efficiency and random nature of the stock price following the presidential election cycle. There is a modest amount of empirical studies (e.g. Hüning, 2017; Khalifa et al., 2016; Padhi & Shaikh, 2014; Shaikh & Padhi, 2014; Eksi & Tas, 2017; Bekiros, Jlassi, Lucey, Naoui, & Uddin, 2017) on asset pricing, and implied volatility, market efficiency, herding behavior, fed policy and US uncertainty measures. The present study is an extension of the previous works on asset pricing under EPU index and presidential election 2016. The work is very comprehensive and analyzes the 27-global equity, FX and VIX based markets. There are quite good number of recent attempts (e.g. Chien, Mayer, & Wang, 2014; Ejara, Nag, & Upadhyaya, 2012; Hung, 2013; Lin, Ho, Shen, & Wang, 2016; Sturm, 2013; Zouaoui, Nouyrigat, & Beer, 2011 and Jang & Chang, 2016) discusses on the investors sentiment and crises, opinion poll and stock market, PEC & EP and stock market, U.S. presidential elections and Taiwan stock market, Politics and investment trading in emerging markets and vote buying and election victory. But, there are no single studies that examine the global financial market in terms of Stock, FX and VIX markets. The present work is novel among the previous work; the reason it analyses the 27 markets and U.S. presidential elections 2016, presented through event study and dummy regression model. The empirical outcome signifies that markets are inefficient in the short-run (election year) and allows the opportunity to make abnormal gains from the market. The Republican president elect has shown negative effects on the Nifty50, S&PASX200, and IPC equity markets while FTSE100, DJIA, Top40, EuroStoxx50 and Nikkei225 has reported positive returns. The Trumps’ proposal on international trade has caused major loss in global currency market against the USD. The investors’ sentiment to be measured extremely low on the poll announcement day but VXJ and AXVI based market participants has shown very high concern. The bearish-run election effects to be observed during the election period while post election period has shown bull-run effects (Asia-pacific markets). The U.S. presidential election based GFM analysis hold two practical implications: First, short-run market in-efficiency provides opportunity to gain from the GFMs. Second, the FPI can formulate their profitable strategy to gain from the FX market trading. The article has been drafted as: Section 2 provides the detailed information about the data sources and summary of descriptive measures. Section 3 offers the empirical model followed by hypotheses and Section 4 displays the empirical outcomes and discussions. Section 5 presents the robustness check. The last section ends with the summary and conclusion. 2. Data sources and description To examine the global financial markets: Stock, FX and VIX markets have been considered. The period of investigation ranges from 1st January 2016 to 31st January 2017. The sample period is an important period for financial study the reason is that Presidential election 2016 is one of the global political event and investors closely eyes on the political movements and campaign of the political party. There is no reason to believe that economics and politics are closely associated, and political uncertainty significantly influences the investing community. The rational investors invest in the diversified portfolio in order to minimize the market specific risk. The election influenced rational investor would like to perform financial planning by investing in Stock, FX, ETF and F&O on volatility index, so as to gain something abnormal from market anomalies. In particular, the study examines 27 financial markets in terms of Stock, FX and investor sentiment index (VIX). The U.S presidential election year 2016 based equity market consists of Nikkei 225, HSI, NIFTY50, FTSE100, DJIA, EuroStoxx, S&PASX200, IPC and Top40. On the other hand, the FX market encompasses AUD, CAD, EUR, GBP, HKD, MXN, ZAR, INR and JPY against the USD. The most popular VIX indices are VXN, VXD, VIXC, VFTSE, NVIX, VXJ, VHSI, AXVI and VDAX. In order to control the behavior of Stock, FX and VIX market, some of the global benchmark stock and currency indices are considered. The Global Dow (GDOW) is the stock index made of 150 blue-chip companies traded across the globe. The GDOW incorporates the stocks from developed and emerging markets. It is constructed and disseminated by Dow Jones & Co. Moreover, FX market controlled using the U.S. Dollar index (DXY). The DXY index commonly known as basket of partners’ currency index actively traded with U.S. The U.S. DXY is consisting of worlds’ major currencies such as Euro, Japanese Yen, Pound Sterling, Swiss Franc, Swedish Krona and Canadian Dollar. The USDX goes up when dollar become stronger against the other currencies in international trade. The presidential election 2016 has been classified in three event windows namely Pre election period (PREP), Election period (EP) and Post election period (PSEP). Appendix A describes the details on sub-event windows followed by the above classification. The trading days under evaluation are respectively 196-days Pre election, 32-days Election period and 57-days Post election period. The U.S. presidential election 2016 held on 8th November 2016 and election polls were announced on 9th November 2016. All the financial data analyzed in this work has been sourced from Bloomberg database. Fig. 1 displays the time series plot of global Stock, FX and VIX markets for the election year 2016. It is clearly visible from the graph that there were significant ups and down prevailed in the Equity, FX and VIX markets. The changes are more apparent in the opening months of the election year, and mid of June and July and further followed by October and November. For now, consider Fig. 2, describes the sample point more specifically for the election period, that is one of the most volatile and uncertain period. The first panel exhibits the plot of major stock indices across the globe during October and November. The Global Dow index of U.S. traded very low in the Pre election week with below 2400 points. The graphical

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Fig. 1. Time series plot of Stock, FX and VIX markets.

Fig. 2. Time series plot of Stock, FX and VIX markets during Election Period (October–November).

view explains that due to U.S. presidential election 2016, the most effected global equity market in the Post election period were Australia(ASX), Mexico(IPC), India(Nifty50), Hon Kong (HSI), Eurozone (EuroStoxx50) and U.K.(FTSE). Indeed, Japan (Nikkie225) and U.S. (DJIA) lost more in the Pre election period (i.e. October). The second panel of the Fig. 2 displays the graphical view of the FX market in the U.S. dollar. The USD DXY is the global dollar index which displays very low during the Pre election window, while it is higher in the Post election period. The FX market such as Australia (AUD/USD) and Eurozone (EUR/USD) has gained more in terms of ‘Post election period effects’ on the domestic exchange rate. On the other hand,

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currencies such as Canadian (CAD/USD), Britain (GBP/USD), Hon Kong (HKD/USD), India (INR/USD), Japan (JPY/USD), Mexico (MXN/USD) and South Africa (ZAR/USD) have lost their value against the USD. Now moving on the last panel, shows the time series plot of volatility index (VIX) of major stock market volatility. The first graph displays the Global Dow index, and rests of the graphs are volatility indices. It is apparent from the close observation that GD Index and VIX are negatively associated. It is clearly seen that around the election period expected stock market volatility appears to be very volatile. On the 8th November, it was traded more than 28% for the German market, and stood highest for the Japan (about 28%). On the U.S. presidential election 2016, more quantitative observations are offered in Table 1. Table 1 summarizes the descriptive statistics for the U.S. presidential election 2016 across the global Stock, FX and VIX markets. Panel A shows the summary statistic for nine global equity markets. The average arithmetic annualized return across the nine markets was 0.36% with median return of 1.10%. The maximum and minimum market returns was respectively 3.72% and
5.03%. One of the important observations (except to Japan) is that eight equity markets have reported marginally positive returns. For now, consider Panel B of Table 1 that describes the FX market around the U.S. presidential election 2016. The study considers worlds’ major traded currency as mentioned in the table. Out of nine there are four markets (CAD/USD, GBP/USD, ZAR/USD, and JPY/USD) that was gained during the election year. By looking at the standard deviations of respective FX markets, Mexico, Australia, India, U.K. and Japan exhibited highest volatility during the election year. The descriptive statistics shown in the Panel C inarguably signifies that expected stock market volatility was very volatile followed by the U.S. presidential election 2016. The average changes in percentage across the VIX market it was measured
2.55%, this signifies that market was in the bearish rally. Only except to Nifty VIX, rest of the volatility index reported negative change in the implied volatility index. On an average, the maximum VIX level of 35% noticed during the election period with highest standard deviation of Japan-VXJ and Germany-VXD. Table 2 shows the Kruskal–Wallis H –test to validate the null ‘‘The U.S. presidential election 2016 does not effects the global Stock, FX and VIX market”. The H –test clearly evidence that in the Pre election period and Post election period the global Stock, FX and VIX markets have been distracted in different directions. Further supportive evidences are reported in the next section.

3. Empirical model To analyze the effects of U.S. presidential election 2016 mainly three financial markets are considered: Stock, FX and VIX. To account for the presidential election effects the sample period classified in three event windows (e.g. Kaeppel, 2009): Pre election period (PREP), Election period (EP) and Post election period (PSEP). For more detailed information consult Appendix A. The study employs event study methodology in terms of election period dummy. In order to measure the effects of U.S. presidential election 2016 on the GFMs two dummies are considered in the Pre election period, five dummies for the election period and two for the Post election period. Let Sit is the stock price index i = Nikkei 225, HIS, NIFTY 50, FTSE100, DJIA, EuroStoxx, S&PASX200, IPC, Top40 and continuously compounded log-transformed returns is

(

Rit ¼ ln

Sit

)

ð1Þ

Sit
1

and FX it is the foreign exchange value of globally traded currencies, where i = AUD, CAD, EUR, GBP, HKD, MXN, ZAR, INR, JPY against U.S dollar. The FX log-transformed returns is calculated as

(

i

RFX ¼ ln t

FX it

)

ð2Þ

FX it
1

Similarly, the percentage change in the implied volatility index has been expressed as (see, e.g. Fleming, Ostdiek, & Whaley, 1995),

DVIX it ¼ VIX it
VIX it
1

ð3Þ

where, i = VXN, VXD, VIXC, VFTSE, NVIX, VXJ, VHSI, AXVI, VDAX The following empirical specification is the extension of the work of Mattozzi (2008), Nippani and Medlin (2002) and Zouaoui et al. (2011). The dummy OLS regression specifications have been expressed as follows: 3.1. Pre election period and global financial markets

Rit ¼ a0 þ

130 X

66 X

j¼1

j¼1

a1 DPREP1 þ jt

a2 DPREP2 þ a3 GDRT t þ a4 Rit
1 þ ePREP jt t

ð4Þ

Table 1 Summary statistics. Panel A: World Stock Market and U.S. Election Year 2016 Underlying

Japan Nikkei 225

China HSI

India NIFTY 50

UK FTSE100

USA DJIA

Europe Euro Stoxx

Australia S&PASX200

Mexico IPC

South Africa Top40

Returns

Index

Returns

Index

Returns

Index

Returns

Index

Returns

Index

Returns

Index

Returns

Index

Returns

Index

Returns

Index

Mean Median Maximum Minimum S.D. Numbers


0.7493 1.5160 5.6557
7.5037 1.5114 281

17104 16857 19602 15095 1068 282

0.4143 1.2827 3.4000
4.2386 1.0922 264

21539 21589 24014 18279 1452 265

0.5127 0.6445 5.8742
5.6917 1.0448 264

8118 8168 8969 7024 490.5 265

0.8890 1.2287 3.5150
3.5192 1.0184 273

6531 6672 7338 5537 437.7 274

0.7890 0.9588 2.2332
3.3510 0.7202 271

18066 18081 20103 15692 1040 272

0.1178 0.6540 3.4090
9.0110 1.3213 278

3027 3020 3326 2680 129.1 279

0.4651 1.6557 3.2864
3.2233 0.8916 272

5310 5340 5807 4765 236.9 273

0.6382 1.4208 2.8779
4.6789 0.8719 273

45681 45709 48695 40265 1815 274

0.1730 0.5127 3.2841
4.0700 1.1204 269

5125 5143 5471 4706 162.4 270

Panel B: World FX Market and U.S. Election Year 2016 Underlying

Australia AUD

Canada CAD

Europe EUR

UK GBP

China HKD

Mexico MXN

South Africa ZAR

India INR

Japan JPY

Statistics

Returns

FX

Returns

FX

Returns

FX

Returns

FX

Returns

FX

Returns

FX

Returns

FX

Returns

FX

Returns

FX

Mean Median Maximum Minimum S.D. Numbers

0.0173 0.1053 1.9046
2.4028 0.7144 278

0.744 0.749 0.781 0.687 0.021 283


0.0218
0.0193 1.9331
1.9545 0.5942 280

1.324 1.316 1.458 1.253 0.039 283

0.00225
0.0043 1.9185
2.4085 0.5155 280

1.104 1.111 1.153 1.039 0.027 283


0.0564
0.037 2.9846
8.4016 0.8801 282

1.346 1.326 1.488 1.205 0.088 283

0.00041
0.0026 0.2786
0.2561 0.0441 273

7.762 7.758 7.819 7.75 0.012 283

0.0686 0.0231 8.0742
2.8139 1.0683 281

18.9 18.59 21.96 17.18 1.167 283


0.049
0.0844 4.8967
3.214 1.2931 282

14.61 14.4 16.88 13.19 0.899 283

0.00681
0.0135 1.0414
0.8282 0.3066 281

67.24 67.13 68.78 66.11 0.646 283


0.0229
0.0043 2.2228
3.7722 0.794 282

109.3 109.3 121.1 99.89 5.782 283

Panel C: World Stock Market Volatility (VIX) and U.S. Election Year 2016 Underlying

USA VXN

USA VXD

Canada VIXC

UK VFTSE

India NVIX

Japan VXJ

China VHSI

Australia AXVI

Germany VDAX

Statistics

Change

Index

Change

Index

Change

Index

Change

Index

Change

Index

Change

Index

Change

Index

Change

Index

Change

Index

Mean Median Maximum Minimum S.D. Numbers


3.00
9.00 6.67 4.49 1.22 271

17.84 16.52 31.91 12.3 4.29 272


2.54
7.00 6.41
3.98 1.14 271

15.11 13.83 27.39 10.56 3.58 272


3.41
14.00 5.78
5.74 1.55 271

16.3 15.06 31.99 6.5 4.75 272


3.40
3.00 4.94
4.25 1.40 278

17.11 15.74 32.48 10.64 4.98 279

0.65
9.75 4.60
2.30 0.82 266

16.51 16.24 25.97 12.75 2.15 267


0.98
5.00 9.71
9.02 1.97 263

25.75 24.75 49.84 17.28 6.13 264


4.19
19.00 6.67
4.69 1.33 263

20.6 19.95 37.99 12.5 4.70 264


2.41
11.90 4.78
5.35 1.09 273

16.52 15.92 30.29 10.22 3.75 273


3.71
7.00 4.28
4.30 1.37 276

22.23 20.92 37.39 14.23 5.23 277

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

Statistics

[Table displays the summary statistics for sample period January 2016 to January 2017. Table summarizes the descriptive measures for Stock, FX and VIX markets following U.S. political uncertainty.]

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Table 2 Kruskal–Wallis H-test. Stock Market

H-stat p-value

FX Market

VIX index

Pre election period

Post election period

Pre election period

Post election period

Pre election period

Post election period

87.374a 0.000

89.122a 0.000

109.208a 0.000

120.311a 0.000

101.639a 0.000

98.211a 0.000

[Table 2 presents the test of null ‘‘The U.S. presidential election 2016 does not effects the global Stock, FX and VIX market”. Significant at a1%, b5%, c10% level]

3.2. Election period and global financial markets

Rit ¼ b0 þ

20 X

b1 DEP1 jt þ

j¼1


5 X

b2 DEP2 jt þ

j¼
1


1 X

EP4 b3 DEP3 jt þ b4 D0t þ þ

j¼
1

5 X

i EP b5 DEP5 jt þ b6 GDRT t þ b7 Rt
1 þ et

ð5Þ

j¼1

3.3. Post election period and global financial markets

Rit ¼ c0 þ

35 X

c1 DPSEP1 þ jt

j¼1

22 X

c2 DPSEP2 þ c3 GDRT t þ c4 Rit
1 þ ePSEP jt t

ð6Þ

j¼1

where i = Stock, FX and VIX a = coefficient for Pre election period b = coefficient for Election period c = coefficient for Post election period Djt = Event window/Dummies et = classical error term GDRT = Global Dow returns (Note: for FX model, DXY dollar index returns have been considered) Note: for VIX market analysis, Rit has been replaced with DVIX it = VIX it
VIX it
1 on the left hand side of Eq. (4)–(6). 3.4. Hypotheses of the model Global Financial Market (GFM) Event Window

Estimate

STOCK

FX

VIX

Pre election period

a

The positive (negative) significant slopes indicate domestic currency has depreciated against the USD

Due to ambiguity of the Election year and unknown outcome lead to rise in the level of implied volatility index

Election period

b

The positive (negative) significant slopes indicate domestic currency has depreciated against the USD

The slopes should appear positive significant irrespective of the nature of presidential election outcomes

Post election period

c

The positive (negative) significant slopes indicate that presidential elections 2016 have shown favorable (adverse) effects on the global equity markets The period with high degree of uncertainty. The significant positive (negative) slopes confirms the bull-run (bearish-run) with market inefficiently The period of certainty. The expected sign of slope should be positive

The expected sign of slopes +/
depending upon the U.S. international trade policy

The VIX level should go down as election uncertainty resolved, hence slopes should appear negative

4. Empirical results and discussion This section presents the performance of global Stock, FX and VIX market during the U.S. presidential election 2016. First, the equity market has been analyzed; second FX market and third VIX based equity market. The empirical evidences classified in three different panels. Panel A shows the performance of global financial markets during Pre election period (e.g. Kaeppel, 2009)) followed by two dummies. Panel B is the election period (i.e. October and November) evaluated by

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introducing five dummies. Lastly, panel C offers the GFMs behavior in the Post election period. The estimated tables report adj. R2, DW-stat and Wald F-statistics for the respective models. Further, t-statistics are based on heteroskedasticity and autocorrelation consistent (HAC) standard errors. An autoregressive term has been added in each model to control the issue of autocorrelation. 4.1. Global equity market and U.S. presidential election The results displayed in Table 3 are inarguably impressive to say that presidential election 2016 has shown significant effects on the global equity markets. For now, consider Panel A of Table 3, reports the global equity market in the Pre election period (i.e. January to June and January to September). It is apparent from the outcome that Japan, USA and Europe has negatively opening-up with the election year 2016, but not affected significantly, only Japan has influenced at 5% level of significance. The rest of the equity market has responded with positive returns followed by the Pre election period. The Wald F – statistics appears to be statistically significant for eight markets, signifies that Pre election period does matter for the market participant. Overall, the result speaks that Pre election period show ‘bearish-run election effects’ on the equity market. Further, Panel B of Table 3 which displays the election period and stock market performance. The October is the month with full amount of uncertainty followed by presidential election debates between the potential candidates for the U.S. president post. Hence, the markets are supposed to be more volatile during this election period. The estimates on the indicator variable DEP1 clearly show that three markets have reported negative returns, and six have posted positive returns. It is jt observed that Japan and Eurozone equity market has reported significant positive returns on the uncertainty of U.S. presidential election 2016. The next election period dummy is DEP2 this measures the stock market behavior in a window of five trading days before jt the electoral voting take place. It is clearly seen that seven markets have yielded negative returns in the fear of ‘who will be the winner of presidential election 20160 ? The t –statistics appears statistically significant, implies that investors have taken is the actual poll day short position in anticipation that stock price will fall after the election. The election period dummy DEP3 jt (i.e. 8th November 2016) and the day with high degree of ambiguity about future president elect 2016. It has been apparent from the estimates that market has responded positively one day ahead from the poll announcement. The possible reason may be the belief that Trump will be the winner, and Republican always lead to bulls rally in the market. But rest of the market noticed negative returns. The 9th November 2016 is the day of announcement of president elect based on the count of electoral votes. The market trend followed the previous day but one can see that due to winning of Republican; India, Australia and Mexico has experienced significant sell-off in the equity markets. This is due to the strict foreign policy of Trump against the international trading partners. The indicator variable DEP5 measures the global equity market after the president jt elect announced, one can see that following the previous outcome China, U.K., Mexico and South Africa have adjusted the market with negative sentiment. Again, the significant Wald F-stat clearly show that U.S. presidential election 2016 has disrupted the world stock market. This happens due to the ‘short-run market inefficiency’, which yields the abnormal gain to the market player. Last Panel C demonstrates the global equity market after the election uncertainty resolved. Generally speaking uncertainty leads to abnormal gain while certainty only yields normal profit from the equity trading. The Post election period dummy DPSEP1 and DPSEP2 reports that five markets moved in the positive sentiment and rest traded in the loss zone. The Chijt jt nese equity market has recovered significantly while European and Australian counterpart adjusted with the negative returns. 4.2. FX market and U.S. presidential election In order to analyze FX market, worlds’ major currencies have been evaluated during the election year 2016. The positive slopes indicate depreciation of domestic currency against the U.S. dollar. Table 4, Panel A displays the behavior of global FX market at the start of election year 2016. It is apparent from the results that five markets have lost the value of domestic currency against the U.S. dollar, while rests have gained. One of the interesting facts visible from the table that Japanese Yen has gained significantly before the USD during January to September 2016. Panel B of Table 4 analyzes the FX market followed by the election period (i.e. October to November). Exactly one month before the poll day: - Australia, Canada, Eurozone, South Africa and Japan has experienced decline in the domestic currency against the dollar but not significant. Before five days from the poll day, again majority of the markets lost their power in international currency market. The implications of the poll day on Australia and Japan FX market has been felt significantly negative while Canada, Eurozone, Hon Kong, Mexico, South Africa and India has recovered significantly against the USD. Albeit, one can see that on the day of announcement of president elect Canada, U.K., Hon Kong, Mexico, South Africa and India has suffered a profound loss in exchange of USD. The Post election period window (of five trading days) speaks that U.K., India and Japan kept on losing international purchasing power of national currency against the USD. The significant Wald F-statistic make sense that election period encompassed important information about the international trade politics between domestic and international currency (USD). The significant F-statistic explains that global FX market has adjusted the presidential election 2016 in terms of managing the FX market risk.

554

Table 3 DOLS on U.S. Presidential Election 2016 and Global Stock Market. Panel A Pre election period and global stock market Country Underlying

Japan Nikkei 225 Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

a0 aPREP1 1 aPREP2 2 aPREP3 3 a4ARð1Þ

0.0015
0.0041

1.54
1.99b


0.0008 0.0005


0.93 0.41


0.0002 0.0004


0.26 0.36


0.0004 0.0009


0.69 1.01

0.0009
0.0006

1.63
0.84

0.0000
0.0009

0.05
1.03


0.0002 0.0006


0.37 0.72


0.0004 0.0010


0.32 0.83


0.0009 0.0013


1.05 1.22


0.0006


0.38

0.0022

1.88c

0.0006

0.50

0.0005

0.63


0.0008


1.22


0.0005


0.62

0.0004

0.41

0.0001

0.09

0.0001

0.10

0.1739

1.52

0.5836

7.42a

0.3166

5.32a

0.9178

13.69a

0.3586

6.07a

1.2844

17.90a

0.5125

9.71a

0.6288

11.40a

0.8082

14.53a

0.0240

0.23


0.2691


4.14a


0.2559


2.61a


0.1173


1.78


0.3035


3.41a


0.1412


2.23b


0.2050


3.67

0.1611

2.09b


0.0716


1.49

0.03 1.99 1.90 0.130

India NIFTY 50

0.15 2.07 21.02a 0.000

UK FTSE100

USA DJIA

c

Europe Euro Stoxx

Australia S&PASX200

Mexico IPC

a

South Africa Top40

0.08 2.08 11.42a 0.000

0.60 2.02 62.60a 0.000

0.12 2.07 13.01a 0.000

0.70 2.01 113.90a 0.000

0.22 2.05 32.66a 0.000

0.40 1.93 46.42a 0.000

0.39 2.05 70.88a 0.000

India NIFTY 50

UK FTSE100

USA DJIA

Europe Euro Stoxx

Australia S&PASX200

Mexico IPC

South Africa Top40

Panel B Election period and global stock market Country Underlying

Japan Nikkei 225 Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

b0


0.0008 0.0038


0.77 2.52b

0.0002
0.0016

0.28
0.79

0.0003
0.0001

0.55
0.09

0.0002 0.0007

0.44 0.60

0.0003
0.0002

0.87
0.57


0.0007 0.0024


1.62 3.12

0.0003
0.0016

0.62
1.29

0.0003 0.0007

0.73 0.73


0.0001
0.0011


0.24
1.08

bEP2 2


0.0040


1.83c


0.0015


0.76


0.0034


2.12b


0.0037


4.57a


0.0019


4.48a


0.0009


0.95


0.0022


1.89c

0.0032

0.45

0.0000


0.03

bEP3 3

0.0065

4.56a

0.0093

10.71a


0.0019


1.50

0.0016

2.43a

0.0113

12.94a


0.0006


0.85


0.0008


0.74

0.0046

4.09a

0.0034

5.24a

bEP4 4 bEP5 5 bEP5 6 ARð1Þ b7 2

0.0048

3.38a

0.0021

1.68c


0.0497


10.14a

0.0094

8.59a

0.0062

3.98a

0.0118

13.28a


0.0148


4.88a


0.0192


9.63a

0.0086

10.21a

0.0069

1.09


0.0062


2.66a

0.0100

0.82


0.0046


1.53

0.0057

2.59a


0.0009


0.69

0.0057

1.22


0.0115


1.55


0.0045

-1.51

0.18

1.51

0.58

7.31a

0.29

4.83a

0.91

13.54a

0.36

6.13a

1.29

18.00a

0.51

9.48a

0.63

11.72a

0.80

14.47a

0.03

0.27


0.27


4.16


0.15


1.94


0.11


1.66


0.33


3.74


0.14


2.13


0.19


3.33

0.07

1.23


0.08


1.71

bEP1 1

adj.R DW-stat Wald F-stat p-value

China HSI

0.02 2.00 11.19a 0.000

0.14 2.07 79.07a 0.000

0.17 2.06 33.92a 0.000

0.61 2.02 85.76a 0.000

0.15 2.11 200.82a 0.000

0.70 2.01 161.69a 0.000

0.23 2.04 26.29a 0.000

0.44 1.96 51.35a 0.000

0.38 2.04 85.80a 0.000

India NIFTY 50

UK FTSE100

USA DJIA

Europe Euro Stoxx

Australia S&PASX200

Mexico IPC

South Africa Top40

Panel C Post election period and global stock market Country Underlying

Japan Nikkei 225 Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

c0 cPSEP1 1 cPSEP2 2 cGDRT 3 cARð1Þ 4


0.0009 0.0031


0.80 1.76c

0.0000
0.0019

0.03
1.34

0.0001
0.0012

0.21
0.84

0.0002 0.0003

0.48 0.35

0.0003 0.0010

0.90 1.46


0.0006 0.0016


1.27 1.58

0.0002 0.0007

0.46 0.70

0.0001
0.0003

0.27
0.20


0.0001
0.0015


0.31
0.93

0.0011

0.52

0.0023

2.07b

0.0020

1.88c


0.0017


1.50


0.0005


0.76


0.0017


2.19b


0.0021


2.43a

0.0005

0.29

0.0008

0.44

0.1628

1.41

0.5832

7.43a

0.3170

5.49a

0.9173

13.64a

0.3586

6.05a

1.2856

18.02a

0.5139

9.65a

0.6276

11.28a

0.8070

14.59a

0.0372

0.34


0.2675


4.16


0.2620


2.70


0.1185


1.81


0.3042


3.39


0.1483


2.34


0.2108


3.80

0.1643

2.09


0.0711


1.46

adj.R2 DW-stat Wald F-stat p-value

0.02 2.00 1.75 0.157

China HSI

0.15 2.07 21.23a 0.000

0.09 2.08 13.66a 0.000

0.60 2.02 64.01a 0.000

0.12 2.08 13.40a 0.000

0.70 2.02 114.91a 0.000

0.22 2.06 33.59a 0.000

0.40 1.93 43.33a 0.000

0.39 2.05 70.94a 0.000

[Table reports the estimates for the regression Eqs. (4)–(6), for the Equity market. To account for the presidential election effects the sample period classified in three event windows: Pre election period (PREP), Election period (EP) and Post election period (PSEP). GDRT = Global Dow returns. The t -statistics are consistent of HAC of Newey and West standard errors. Significant at a1%, b5%, c10% level].

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

adj.R2 DW-stat Wald F-stat p-value

China HSI

Table 4 DOLS on U.S. Presidential Election 2016 and Global FX Market. Panel A Pre election period and global stock market Country Underlying

Australia AUD

Canada CAD

Europe EUR

UK GBP

China HKD

Mexico MXN

South Africa ZAR

India INR

Japan JPY

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat


1.47E
05
0.0001


0.03
0.07


0.0005 0.0001


1.02 0.19


6.40E
06
0.0001


0.03
0.25


0.0001
0.0010


0.08
0.91

6.15E
06
8.22E
07

0.32
0.01

0.0009
0.0002

0.66
0.12


0.0004 0.0003


0.50 0.20

0.0001 4.23E
05

0.46 0.11

0.0010
0.0020

1.89
2.58a

0.0003

0.35

0.0008

1.24

0.0001

0.24


0.0005


0.49


1.43E
05


0.54

0.0001

0.08


0.0004


0.26


0.0003


0.80


0.0011


1.15


0.8482


10.69a

0.6119

8.64a


1.0271


39.06a


1.1291


4.44a

0.0127

2.20b

0.5465

2.91a

1.2403

6.70a

0.2884

7.33a

0.8121

3.74a

0.1539

c

0.0891

1.34


0.0068


0.10


0.0290


0.35


0.0904


1.52

adj.R2 DW-stat Wald F-stat p-value

0.30 2.00 38.90a 0.000


0.0699


1.04


0.0378


0.48

0.22 1.99 25.60a 0.000


0.2442


4.23

a

0.0541

0.63

1.82

0.86 2.04 510.54a 0.000

0.34 2.01 7.78a 0.000

0.02 2.06 1.81 0.146

0.05 1.99 2.86b 0.038

0.18 1.99 15.05a 0.000

0.18 2.00 18.58a 0.000

0.23 2.03 11.33a 0.000

Europe EUR

UK GBP

China HKD

Mexico MXN

South Africa ZAR

India INR

Japan JPY

Panel B Election period and global FX market Country Underlying

Australia AUD

Canada CAD

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

bEP1 1

2.35E
05 0.0002

0.06 0.24


0.0002 0.0004


0.62 0.36


4.10E
05 0.0006


0.42 1.47


0.0007
0.0015


1.38
1.03

2.96E
06
2.83E
05

0.08
0.64

0.0007
0.0016

1.23
0.81


0.0006 0.0003


0.84 0.19

3.25E
05
0.0002

0.19
0.72


0.0004 0.0012


1.06 1.19

b0 bEP2 2

0.0014

0.90


0.0003


0.20

2.02E
05

0.08

0.0009

0.51

1.66E
05

0.37


0.0027


0.36

0.0011

0.27

0.0007

1.21

0.0012

0.67

bEP3 3

0.0057

6.99a


0.0060


10.10a


0.0006


3.70a

0.0004

0.48


0.0001


2.59a


0.0158


12.76a


0.0137


8.70a


0.0079


22.76a

0.0071

7.58a

bEP4 4 bEP5 5 bDXYRT 6 ARð1Þ b7 2


0.0111


16.71a

0.0064

9.65


0.0034


9.62a

0.0098

4.70a

0.0001

2.29b

0.0758

47.05a

0.0132

8.51a

0.0023

7.07a

0.0004

0.26


0.0012


0.91


0.0016


1.00


0.0005


1.68c

0.0053

2.00b

0.0001

1.31

0.0014

0.18

0.0085

1.12

0.0034

2.98a

0.0041

2.93a


0.8304


9.99a

0.6067

8.17a


1.0244


39.39a


1.1413


4.34a

0.0126

2.14b

0.4689

2.61a

1.2064

6.24a

0.2808

6.89a

0.8018

3.53a


0.0738


1.06


0.0379


0.48


0.2547


4.51a

0.0391

0.43

0.1540

1.79c

0.0359

0.69


0.0240


0.36


0.0548


0.66


0.0878


1.42

adj.R DW-stat Wald F-stat p-value

0.30 1.99 397.27a 0.000

0.21 1.99 349.91a 0.000

0.86 2.05 667.48a 0.000

0.35 2.01 18.31a 0.000

0.04 2.06 121.33a 0.000

0.24 2.01 1458.88a 0.000

0.19 1.99 189.62a 0.000

0.22 2.00 654.71a 0.000

0.21 2.03 43.60a 0.000

Europe EUR

UK GBP

China HKD

Mexico MXN

South Africa ZAR

India INR

Japan JPY

Panel C Post election period and global FX market Country Underlying

Australia AUD

Canada CAD

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

c0 cPSEP1 1 cPSEP2 2 cDXYRT 3 cARð1Þ 4

4.88E
05
0.0010

0.11
1.24


0.0001
0.0009


0.17
1.17

1.28E
05
0.0002

0.12
0.71


0.0008 0.0010


1.44 1.09

7.79E
07
1.87E
05

0.02
0.31

0.0010
0.0017

1.34
1.10


0.0002
0.0021


0.27
1.49

0.0001
2.45E
05

0.43
0.05


0.0005 0.0024


1.21 2.63a

0.0012

1.40


0.0006


0.53


0.0001


0.38

0.0003

0.17

4.42E
05

1.08

0.0001

0.05

0.0009

0.70


7.93E
06


0.02


0.0001


0.10


0.8350


10.39a

0.6109

8.39a


1.0260


38.77a


1.1278


4.42a

0.0129

2.24

0.5555

2.94a

1.2558

6.68a

0.2887

7.23a

0.8100

3.70a


0.0782


1.17


0.0360


0.46


0.2443


4.31a

0.0564

0.65

0.1538

1.79c

0.0872

1.33


0.0114


0.16


0.0273


0.34


0.0846


1.39

adj.R2 DW-stat Wald F-stat p-value

0.30 2.00 40.95a 0.000

0.22 1.99 26.66a 0.000

0.86 2.04 524.60a 0.000

0.34 2.01 7.36a 0.000

0.04 2.06 2.17c 0.092

0.05 1.99 2.90b 0.035

0.19 1.99 14.96a 0.000

0.18 2.00 18.30a 0.000

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

Estimate

a0 aPREP1 1 aPREP2 2 aDXYRT 3 aARð1Þ 4

0.23 2.03 10.83a 0.000

[Table reports the estimates for the regression Eqs. (4)–(6), for the FX market. To account for the presidential election effects the sample period classified in three event windows: Pre election period (PREP), Election period (EP) and Post election period (PSEP). DXYRT = dollar index returns (DXY). The t-statistics are consistent of HAC of Newey and West standard errors. Significant at a1%, b5%, c10% level] 555

556

Table 5 DOLS on U.S. Presidential Election 2016 and Global Stock market volatility (VIX). Panel A Pre election period and Global Stock market volatility (VIX) Country Underlying

USA VXN

USA VXD

Canada VIXC

UK VFTSE

India NVIX

Japan VXJ

China VHSI

Australia AXVI

Germany VDAX

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

0.0313
0.0838

0.36
0.68

0.0309
0.0728

0.38
0.67

0.0338
0.1151

0.43
1.03

0.0494
0.0663

0.71
0.51


0.0335 0.0007


0.70 0.01

0.0129 0.0148

0.14 0.08


0.0194
0.0180


0.27
0.15

0.0173
0.0801

0.20
0.70

0.0413
0.0695

0.47
0.48

0.0180

0.17

0.0298

0.30

0.0162

0.16


0.0424


0.44

0.1013

1.05


0.0466


0.27

0.0540

0.55


0.0619


0.52


0.0281


0.25


95.3752


13.05a


92.4205


13.58a


86.1404


12.23a


99.5426


5.92a


38.7169


4.58a


89.7731


6.08a


71.6162


4.96a


45.7008


6.49a


108.4678


7.73a


0.1090


1.31


0.1659


1.83c


0.3192


3.73


0.1378


2.06


0.1779


3.02a


0.2552


3.34a


0.2077


3.14a


0.2002


2.62a

0.0269

0.29

adj.R2 DW-stat Wald F-stat p-value

0.46 2.03 56.78 0.000

0.52 2.05 63.14 0.000

a

0.34 2.13 51.48 0.000

a

a

0.38 2.03 15.83 0.000

a

b

a

0.19 2.05 7.86 a 0.000

0.19 1.88 12.47 0.000

India NVIX

Japan VXJ

0.23 2.20 11.39 0.000

a

0.13 2.13 14.29 0.000

a

0.45 1.99 25.36 0.000

a

a

Panel B Election period and Global Stock market volatility (VIX) Country Underlying

USA VXN

USA VXD

Canada VIXC

UK VFTSE

China VHSI

Australia AXVI

Germany VDAX

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

bEP1 1


0.0031 0.1113


0.06 0.85

0.0137 0.0820

0.31 0.72


0.0261 0.0359


0.49 0.25

0.0128
0.0186

0.18
0.14


0.0037
0.1697


0.09
1.31

0.0278
0.2007

0.28
1.72


0.0115
0.0517


0.18
0.32


0.0373 0.0949


0.77 0.80

0.0102
0.0325

0.14
0.23

0.5981

4.38a

0.1794

1.97b

0.7851

2.46b

0.5306

3.37a

0.6336

2.33b

0.5136

2.86a


0.3771


3.89

0.5030

7.05


0.6550


2.28b

0.3948

2.25


0.2047


0.91

1.3438

17.03a

b0

c

bEP2 2


0.0232


0.07

0.0902

0.29

0.1910

0.70

bEP3 3

0.9024

6.86a


0.0079


0.04


0.6682


3.90

bEP4 4


3.0380


29.86a


3.2657


31.71a


2.2425


9.71a


2.6769


25.58a


0.3960


2.76a

3.8475

5.75a


0.5328


1.20

1.9948

4.07a


4.4724


50.21a

bEP5 5


0.0046


0.01


0.24


1.68c

0.7926

4.16a


0.1219


0.68

0.1444

0.55


1.3189


2.24b


0.4845


1.48


1.2179


2.33b


0.1407


0.95

bGDRT 6


96.6779


12.97a


92.04


13.68a


84.6192


12.00a


98.9888


5.79

a


38.4618


4.49a


90.1115


6.01a


71.6283


4.87a


45.2594


6.38a


109.3866


7.76a

ARð1Þ b7 2


0.0488


0.70


0.16


1.60


0.3323


3.88a


0.1443


2.11

b


0.1861


3.07a


0.2437


3.10


0.2190


3.21a


0.1986


2.54b

0.0512

0.54

adj.R DW-stat Wald F-stat p-value

0.48 2.02 193.21 0.000

0.55 2.06 282.91 0.000

a

0.34 2.13 98.61 0.000

a

a

0.40 2.03 266.56 0.000

a

a

0.19 2.04 11.81 0.000

a

a

0.21 1.87 16.80 0.000

a

a

0.23 2.21 11.86 0.000

a

b

0.18 2.13 14.58 0.000

a

0.49 1.98 3582.53 0.000

a

a

Panel C Post election period and Global Stock market volatility (VIX) Country Underlying

USA VXN

USA VXD

Canada VIXC

UK VFTSE

India NVIX

Japan VXJ

China VHSI

Australia AXVI

Germany VDAX

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

c0 cPSEP1 1 cPSEP2 2 cGDRT 3 cARð1Þ 4


0.0020
0.0168


0.03
0.09


0.0046 0.0384


0.09 0.31


0.0327 0.0633


0.61 0.39

0.0002
0.0390

0.00
0.34


0.0064
0.0810


0.15
1.04


0.0037 0.0810


0.03 0.47


0.0191 0.0101


0.31 0.09


0.0408
0.0404


0.74
0.34


0.0050
0.0002


0.06 0.36

adj.R2 DW-stat Wald F-stat p-value

0.46 2.02 57.91 0.000

c

b

0.0145

0.16

0.0663

0.84

0.1176

1.15

0.1729

1.68

0.0892

1.34

0.0368

0.26


0.0628


0.77

0.1697

2.55

0.1007

0.77


95.0826


13.15a


92.2590


13.65a


85.8138


12.16a


99.5336


5.89a


38.4418


4.52a


89.9163


6.09a


72.2413


5.58a


45.7341


6.46a


108.4143


7.67a


0.1068


1.28


0.1633


1.79


0.3190


3.74


0.1386


2.08


0.1771


3.04a


0.2551


3.37a


0.2994


4.29a


0.2020


2.65a

0.0268

0.29

a

0.52 2.05 62.77 0.000

a

c

0.33 2.13 49.64 0.000

a

a

0.38 2.03 15.10 0.000

a

b

0.19 2.05 8.52 a 0.000

0.19 1.88 12.55 0.000

a

0.27 2.06 10.6 7 0.000

a

0.13 2.13 17.26 0.000

a

0.45 1.99 21.78 0.000

a

[Table reports the estimates for the regression Eqs. (4)–(6), for the VIX market. Note: for VIX market analysis Rit has been replaced with DVIX it = VIX it
VIX it
1 on the left hand side of Eq. (4)–(6). To account for the presidential election effects the sample period classified in three event windows: Pre election period (PREP), Election period (EP) and Post election period (PSEP). GDRT = Global Dow returns. The t -statistics are consistent of HAC of Newey and West standard errors. Significant at a1%, b5%, c10% level]

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

Estimate

a0 aPREP1 1 aPREP2 2 aGDRT 3 aARð1Þ 4

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

557

Panel C of Table 4 offers the Post election period performance of global FX market. One of the important observations from two dummies, which reveals that Japanese Yen has kept on losing against the USD and rest of the market has shown mixed behavior. 4.3. Investors’ fear and U.S. presidential election The stock market volatility and stock returns are associated. The asymmetric relation of returns with volatility causes higher degree of volatility for negative returns shock (e.g. Fleming et al., 1995). The reason behind this phenomenon is that investors’ overreaction is higher for negative returns than the positive returns. The CBOE’s VIX measures the investors’ fear

Fig. 3. Political Uncertainty and Global Financial Markets.

558

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

resulted as the trading in the options market, now majority of the stock exchanges have adopted the CBOE’s VIX methodology to calculate the expected stock market volatility in percentage term. As stock returns and volatility are negatively associated, hence the slopes on the Global Dow returns should appear negative. Table 5 Panel A explains the investors’ perception during the Pre election period of 2016 across the global equity markets. It is clearly observable during the January to June/ January to September 2016 there was some co-movements between the stock market performances and volatility. But, there is no significant measurable fact that explains degree of anxiety in the market. Indeed, looking at the Wald F –statistic, it is noticed that global equity market has experienced significant amount of market uncertainly eyed on the ‘who will be the next president of U.S.? The next panel B explores the investors’ fear and greed during the peak period of U.S presidential election 2016. The market participants closely follow the presidential election debates and nomination for the U.S presidential election candidates and accordingly plan their future investment strategy. Now starting with the October month, the implied volatility level was marginally up for the USA, Canada and Australia while UK, India, Japan and China, Germany has reported decrease in the general level of implied volatility. However, before the one week from the day of poll the overreaction of investor was very high. Almost every equity market has reported an increase of 0.44 point basis VIX level with significant t-statistics. The Japanese VXJ has traded with 0.79 point basis higher among all other markets. The effects of election poll day on the global investors sentiment index has to be observed adversely on VXN, NVIX, VHSI and VDAX, and statistically significant. The Election Day has caused an increase of VDAX by 1.34 point basis. The rests of the five implied volatility indices were closed with significant fall. The election stock market uncertainty increases the general level of VIX index followed by the uncertainty about the future events. The VIX level keeps on rising till the uncertainty resolved. The 9th November 2016 is the announcement of U.S. president elect and once the winner declared market process the news and VIX goes normal. Therefore, one can see that on 9th November seven markets has reported decrease in the implied volatility level by on an average
2.37 point basis. As election stock market uncertainty resolved the VIX level kept on adjusting to normal level for the next few trading days. Panel C of Table 5 ends with the discussion of investors’ sentiment index in the Post election period. The two separate Post election period dummies displays that some of the market has already accustomed the U.S. president elect in their portfolio planning and some markets still reported an increase in the VIX level. The second Post election period dummy measures the investors’ sentiment in the global equity market, as 21st January 2017 is the day of oath taking ceremony. During this period

Table 6 Political Uncertainty and Global Stock Markets. Panel A Political Uncertainty and Stock Returns Election Year 2016


5


1

0

+5

Market

Underlying

estimate

t-stat

estimate

t-stat

estimate

t-stat

estimate

t-stat

estimate

t-stat

Global market Japan Hon Kong India UK USA Europe Australia Mexico South Africa

Global Dow Nikkei 225 HSI NIFTY 50 FTSE100 DJI Stoxx50 S&PASX 200 IPC Top40


0.0012
0.0009
0.0018
0.0001
0.0004 0.0009
0.0017
0.0008
0.0023
0.0024


0.63
0.50
1.15
0.07
0.27 0.74
0.56
0.54
1.25
1.36


0.0003
0.0066
0.0034
0.0048
0.0065
0.0034
0.0024
0.0036 0.0104
0.0020


0.08
1.70
1.54
1.34
1.19
1.61
0.40
0.82 1.14
0.45

0.0013 0.0051 0.0103 0.0005 0.0033 0.0116 0.0031 0.0006 0.0023 0.0048

1.40 5.92 16.91 0.73 2.77 27.37 2.82 0.66 1.60 5.64


0.0010 0.0007 0.0007
0.0233 0.0037 0.0014 0.0043
0.0081
0.0101 0.0024


4.45 2.07 1.59
90.21 12.78 2.72 13.45
37.02
35.77 8.35

0.0001
0.0011
0.0033 0.0104
0.0024 0.0033
0.0007 0.0038
0.0053
0.0014

0.27
0.33
3.75 2.18
1.88 3.16
1.21 1.34
1.69
0.99

Panel B Political Uncertainty and Stock Volatility Market

Underlying

Election Year 2016 estimate t-stat


5 estimate

t-stat


1 estimate

t-stat

0 estimate

t-stat

+5 estimate

t-stat

Global market Japan Hon Kong India UK USA Europe Australia Mexico South Africa

Global Dow Nikkei 225 HSI NIFTY 50 FTSE100 DJI Stoxx50 S&PASX 200 IPC Top40

1.49E
05
1.88E
06
4.75E
07 3.45E
06 6.42E
06
6.05E
07 4.71E
05
1.17E
06 6.78E
06 1.59E
05


5.4E
06 4.2E
06
2.8E
07 9.04E
06 2.28E
05 1.88E
06
1.1E
05 3.91E
06
1.8E
06 5.78E
06


1.63 3.47
0.53 1.99 3.10 4.37
0.78 4.53
0.43 1.16

4.7E
05
1.1E
06 1.05E
06 4.04E
06
2.1E
05 2.65E
07 1.14E
05
6.0E
06 1.39E
04
8.6E
06

25.18
2.65 4.39 1.19
7.98 1.05 1.62
11.08 152.67
4.33


5.9E
06
2.3E
08
1.1E
06 2.3E
06
3.7E
06
4.7E
06
1.1E
05 1.54E
06
3.7E
07
4.8E
06


4.76
0.11
9.98 1.40
3.78
38.77
3.30 6.68
0.12
5.40


5.2E
06
1.1E
05
2.0E
07 1.29E
05 4.18E
06
1.58E
06
1.01E
05
5.04E
06 1.78E
05 1.31E
05


4.27
2.08
0.77 0.75 1.36
3.33
2.41
2.37 2.03 1.89

1.01
0.58
0.90 1.07 0.80
0.78 1.00
1.57 2.32 1.48

X
6 X
1 [Table reports the estimates for the regression equation RV t ¼ k0 þ k1 EPUIt þ k2 RV t
1 þ et and RV t ¼ k0 þ k Di EPUIt þ k Di EPUIt þ i¼
2 1 1 i¼
1 2 2 X þ5 k Di EPUIt þ k5 RV t
1 þ et , for the equity market. Where, RV stands for either Returns or Volatility. The first regression specification k3 D03 EPUIt þ i¼þ1 4 4 measures the global Stock markets under political uncertainty followed by presidential election 2016. The second equation explains the returns and volatility behavior around the election window i.e.
5,
1, 0, +5. The regression specifications are expressed in the form of dummies and interaction dummy variable. The t -statistics are consistent of HAC of Newey and West standard errors. The Bold numbers indicate significant either at a1%, or b5%, or c10% level]

559

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563 Table 7 Political Uncertainty and Global FX markets. Panel A Political Uncertainty and FX Returns Election Year 2016


5

Market

Underlying

estimate

t-stat

estimate

t-stat


1 estimate

t-stat

estimate

t-stat

estimate

t-stat

Dollar Index US Dollar Index Australia Canadian Europe UK Hon Kong Mexico South Africa India Japan

WSJ DXY AUD CAD EUR GBP HKD MXN ZAR INR JPY

0.0011 0.0010
0.0005 0.0000
0.0013
0.0022 0.0000 0.0021 0.0012 0.0004 0.0004

1.70 1.33
0.42 0.04
1.56
0.76 0.70 0.82 0.51 0.90 0.26


0.0036
0.0017 0.0039
0.0013 0.0021 0.0040 0.0000
0.0079
0.0018 0.0004 0.0010


5.43
0.78 3.35
1.69 1.01 1.25 0.16
1.22
0.56 1.09 0.23

0.0041 0.0007 0.0040
0.0043
0.0014
0.0004
0.0001
0.0111
0.0096
0.0063 0.0061

18.99 2.66 7.94
13.97
4.59
0.40
3.28
7.81
10.80
37.68 7.14

0.0002 0.0027
0.0067 0.0042
0.0044 0.0011 0.0001 0.0334 0.0084 0.0016 0.0022

1.21 23.54
32.59 23.95
35.15 4.95 8.63 68.51 22.29 6.26 7.93

0.0027 0.0019
0.0019 0.0004
0.0024 0.0007 0.0000 0.0016 0.0074 0.0020 0.0041

6.26 6.05
4.06 0.64
4.96 0.52 1.32 0.59 2.02 5.83 8.25

0

+5

Panel B Political Uncertainty and FX Volatility Election Year 2016


5

Market

Underlying

estimate

t-stat

estimate

t-stat


1 estimate

t-stat

estimate

t-stat

estimate

t-stat

Dollar Index US Dollar Index Australia Canadian Europe UK Hon Kong Mexico South Africa India Japan

WSJ DXY AUD CAD EUR GBP HKD MXN ZAR INR JPY


4.09E
07
1.28E
07
1.23E
06
5.12E
07
7.23E
08 2.26E
05
1.85E
09 1.91E
05 5.98E
06 1.48E
07 2.37E
06


1.31
0.66
1.79
1.26
0.10 1.37
0.69 2.19 1.04 0.64 2.37

1.50E
06 4.57E
07 1.68E
06 7.87E
08
2.13E
07 1.65E
05
6.90E
09 6.76E
06
1.43E
05
6.42E
07
3.51E
06

4.03 0.88 6.78 0.33
0.16 1.70
1.51 1.00
1.26
3.89
4.39

1.12E
06
5.56E
07 3.38E
07 3.23E
07
1.87E
06
8.14E
06
4.33E
09 0.000173
2.34E
05
4.21E
07 1.28E
05

4.54
6.69 2.42 2.43
6.06
2.55
1.64 69.31
14.57
4.77 30.25


2.86E
08
1.85E
07 4.25E
07 4.52E
07
7.12E
07
6.88E
06
2.13E
09 3.66E
05
7.55E
06 3.31E
06 1.07E
06


0.23
4.52 6.25 7.13
4.59
4.94
1.66 6.56
8.96 75.89 4.89


3.90E
07
3.36E
07
1.51E
06
6.34E
07
1.29E
06
1.65E
06
7.54E
10 5.96E
05 2.58E
05 3.32E
08 2.71E
06


1.62
5.96
1.91
1.76
5.95
0.67
0.49 2.67 2.95 0.17 2.38

0

+5

X
6 X
1 k Di EPUIt þ k Di EPUIt þ k3 D03 EPUIt þ [Table reports the estimates for the regression equation RV t ¼ k0 þ k1 EPUIt þ k2 RV t
1 þ et and RV t ¼ k0 þ i¼
2 1 1 i¼
1 2 2 Xþ5 k Di EPUIt þ k5 RV t
1 þ et , for the FX market. Where, RV stands for either Returns or Volatility. The first regression specification measures the global i¼þ1 4 4 FX markets under political uncertainty followed by presidential election 2016. The second equation explains the returns and volatility behavior around the election window i.e.
5,
1, 0, +5. The regression specifications are expressed in the form of dummies and interaction dummy variable. The t -statistics are consistent of HAC of Newey and West standard errors. The Bold numbers indicate significant either at a1%, or b5%, or c10% level]

VFTSE and AXVI based investors have shown their high degree of concerns over the U.S. and domestic equity market. The Wald F-stat clearly signifies that Post election period of U.S. presidential election 2016 has given important insights to the investing community to optimize their future portfolio planning and investments. Moreover, Post election period is period of certainty about the U.S. politics and it has clearly reflected in the fair prices of global equity market. In the Post election period most of the VIX indices traded in the normal volatility zone. The below 15% level indicate the ‘bearish-run market’ across the global equity markets. 5. Robustness check This section presents some more empirical evidences on presidential election 2016 and global financial markets, further it is studied under the U.S. policy uncertainty index. The novel aspect of the robustness check is that previous results are empirically validated using the proxy for political uncertainty. The economic policy uncertainty index (EPU) is the proxy for political uncertainty in U.S. and it is prepared by Baker et al. (2016)1. The EPU index produced as a weighted average of three items namely: policy related uncertainty, federal tax code and inflation and government spending. The EPU index available since 1985 on daily and monthly frequencies. The index mainly considers 10 leading U.S. newspapers contains the terms: ‘‘Economic”, ‘‘Uncertainty/Uncertain”, ‘‘Congress”, ‘‘Deficit”, ‘‘Federal Reserve”, or ‘‘White House”. Baker et al. (2016) observed that index spikes followed by presidential elections over the years. The study takes into account the daily EPU index values from 1st January 2016 to 31st January 2017 and rescaled into 100. Moreover, daily equity market policy uncertainty index also considered. Following the work of Pástor and Veronesi (2012, 2013) assets prices remain more volatile at the time of higher policy uncertainty, and EPU index spikes around the election year. To test this relationship, the following specifications have been employed:

RV t ¼ k0 þ k1 EPUIt þ k2 RV t
1 þ et 1

http://www.policyuncertainty.com/us_daily.html.

ð7Þ

560

I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

Table 8 Political Uncertainty and implied volatility index (VIX). Panel A Political Uncertainty and Change in VIX(Level) Election Year 2016


5

Market

Underlying

estimate

t-stat

estimate

t-stat


1 estimate

t-stat

estimate

t-stat

estimate

t-stat

USA USA Canadian UK India Japan Hon Kong Australia German

VXN VXD VIXC VFTSE NVIX VXJ VHSI VASX VDAX

0.0892
0.0397 0.2088
0.1582
0.0173
0.1508
0.1264
0.1230
0.0788

0.46
0.19 0.94
1.18
0.23
0.59
0.79
0.84
0.50

0.0539 0.2588 0.6787 1.1646 0.3447 1.0722 1.0766 1.0293 0.7872

0.06 0.28 1.75 2.83 1.45 1.57 2.06 1.46 1.52

0.2416
0.4332
1.2375
0.7327 0.2209
1.0519
0.0158
0.2906 0.7735

0.88
1.37
12.44
11.51 4.83 0.17
0.17
2.06 11.35


1.2055
1.3427
0.8437
1.0911
0.0328 1.9781 0.1264 1.1120
1.7380


31.28
45.83
14.71
34.75
1.57 0.07 4.01 44.29
32.26

0.1403
0.2031 0.2862
0.0407
0.0845
0.8102
0.3827
0.7771
0.0461

0.67
2.51 1.19
0.69
0.58 0.37
1.28
2.30
0.68

0

+5

Panel B Political Uncertainty and VIX Volatility Election Year 2016


5

Market

Underlying

estimate

t-stat

estimate

t-stat


1 estimate

t-stat

estimate

t-stat

estimate

t-stat

USA USA Canadian UK India Japan Hon Kong Australia German

VXN VXD VIXC VFTSE NVIX VXJ VHSI VASX VDAX

7.66E
04 5.17E
04 3.79E
03 1.50E
04
3.64E
05 3.05E
04 7.68E
06
2.11E
05
1.66E
04

1.10 0.88 2.85 1.69
0.33 1.67 0.24
0.13
2.12


1.61E
04 3.69E
04
1.74E
03 4.56E
05 5.54E
05 1.37E
03 2.62E
04 1.09E
03 3.57E
04


0.59 1.13
0.48 0.25 0.49 1.39 2.53 3.05 2.66

2.42E
03 1.78E
03
2.56E
03
3.48E
04
1.32E
04
5.11E
04
1.20E
04
5.47E
05
4.72E
06

20.69 13.90
1.83
8.56
2.70
1.64
5.19
0.45
0.21


3.63E
04
3.85E
04
8.84E
05
1.33E
04
3.46E
05
3.30E
04
2.01E
05
2.43E
04 4.98E
05


4.54
4.94
0.13
6.82
1.42
3.14
1.97
4.78 5.17

3.68E
04 1.86E
04
1.96E
04 4.57E
05 3.09E
04 9.45E
04
5.48E
05 5.56E
04
4.27E
04

1.22 0.65
0.16 0.35 0.73 1.09
0.86 1.34
1.83

0

+5

X
6 X
1 [Table reports the estimates for the regression equation RV t ¼ k0 þ k1 EPUIt þ k2 RV t
1 þ et and RV t ¼ k0 þ k Di EPUIt þ k Di EPUIt þ k3 D03 EPUIt þ i¼
2 1 1 i¼
1 2 2 Xþ5 k Di EPUIt þ k5 RV t
1 þ et for the VIX market. Where, RV stands for either change in VIX level or VIX volatility. The first regression specification i¼þ1 4 4 measures the change in VIX level and volatility under political uncertainty followed by presidential election 2016. The second equation explains the change in VIX and VIX volatility behavior around the election window i.e.
5,
1, 0, +5. The regression specifications are expressed in the form of dummies and interaction dummy variable. The t -statistics are consistent of HAC of Newey and West standard errors. The Bold numbers indicate significant either at a1%, or b5%, or c10% level]

RV t ¼ k0 þ


6 X

k1 Di1 EPUIt þ

i¼
2

Drimpt ¼ k0 þ


5 X j¼
1


1 X

k2 Di2 EPUIt þ k3 D03 EPUIt þ

i¼
1

j

þ5 X

k4 Di4 EPUIt þ k5 RV t
1 þ et

ð8Þ

i¼þ1

0

k1 DPED EPUIt þ k2 DPED EPUIt þ 1 2

þ5 X

k3 DPED EPUIt þ k4 Drimpt
1 þ et 3 j

ð9Þ

j¼þ1

where, RV stands for either Returns or Volatility, Drimpt is the change in the expected stock market volatility. The first regression specification measures the global Stock, FX and VIX markets under political uncertainty followed by presidential election 2016. The second specification explains the returns and volatility behavior around the election window i.e.
5,
1, 0, +5. The regression specifications are expressed in the form of dummies and interaction dummy variable. The third specification takes into account the presidential election debates2 gauged into EPU index and implied volatility index. The interaction dummy (PED) variables are considered in a window of
5, 0, +5, zero signifies debate day. In each regression specification one period lag of dependent variable is added in order to control for autocorrelation. The t-statistics are consistent of HAC of Newey and West standard errors. The volatility of Stock, FX and VIX market has been forecasted using the GJR-GARCH model. Fig. 3 visually explains the volatility of stock and FX market, and level of expected stock market volatility during the presidential election year 2016. The first graph exhibits the Global Dow index volatility with political uncertainty index. It is clearly visible that higher the political uncertainty higher the stock market volatility. The second graph also speaks the same story, the WSJ dollar index volatility remains highly volatile during the election year. The third graph shows the implied volatility level expressed as VIX, VXN and VXD, it is seen that expected stock market volatility appears to be very volatile following the policy uncertainty of future government. Table 6 report the empirical evidences on political uncertainty and stock market volatility. The U.S.A. election year 2016 and global stock markets’ returns appears to be negative. The political uncertainty index (EPU index) has shown negative effects on the 9 developed and emerging markets. The EPU index spikes around the election period and impacts negatively on the stock market. Panel A, election window shows that in the pre election period majority of the global equity markets have experienced negative returns. On the days of announcement of president elect India, Australia, Mexico has suffered 2

Three debates, took place on 26/Sept/16, 09/Oct/16 and 19/Oct/16.

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I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563 Table 9 Political Uncertainty, Presidential Election Debate(PED) and implied volatility. Panel A Political Uncertainty and implied volatility level(VIX) under PED VIX

Days -5 PED1 Days + 5 Days
5 PED2 Days + 5 Days
5 PED3 Days + 5

VXN

VXD

estimate

t-stat

estimate

t-stat

estimate

t-stat


0.5598
1.9333 0.5062
0.1581 0.0877 2.1497
1.9947
1.4871 0.5912


0.77
4.51 1.34
0.50 0.12 5.85
6.86
5.43 1.85


0.0874
1.8974 0.7858
0.2219
0.7714 1.4619
1.2482
1.6083 0.6947


0.13
4.21 3.45
1.68
1.37 5.04
5.44
8.19 3.03


0.1675
1.7362 0.6872
0.1526
3.1176 1.4516
1.2961
1.1413 0.5296


0.24
4.26 2.03
0.69
5.92 6.61
7.29
4.74 2.57

Panel B Political Uncertainty (equity market) and implied volatility level(VIX) under PED

Days
5 PED1 Days + 5 Days
5 PED2 Days + 5 Days
5 PED3 Days + 5

VIX estimate

t-stat

VXN estimate

t-stat

VXD estimate

t-stat

2.3857
14.7526
1.1684 1.3541 0.0847 18.2353
18.1095
13.9818 2.1145

0.47
4.54
1.81 2.14 0.15 5.44
5.46
5.45 1.46

5.0844
14.3940 0.2727 0.2957
0.5613 12.2267
12.0293
15.2118 2.2445

1.03
4.21 0.55 0.62
1.34 4.25
4.22
8.30 1.98

4.3638
13.2024
0.6901 1.1195
2.3475 12.0579
11.8981
10.7249 2.0182

0.87
4.28
1.12 1.95
5.97 5.52
5.51
4.76 2.31

0 P P PED j PED j [Table reports the estimates for the regression equation Drimpt = k0 +
5 EPUIt + k2 DPED EPUIt + þ5 EPUIt + k4 Drimpt
1 + et , for the VIX 2 j¼
1 k1 D1 j¼þ1 k3 D3 market. Where, Drimpt is the change in the expected stock market volatility. The regression specification takes into account the presidential election debates gauged into EPU index and implied volatility index. The interaction dummy (PED) variables are considered in a window of
5, 0, +5, zero signifies debate day. The t -statistics are consistent of HAC of Newey and West standard errors. The Bold numbers indicate significant either at a1%, or b5%, or c10% level]

greater amount of loss in the equity market. The Panel B presents the volatility of each markets under political uncertainty. The U.S. policy uncertainty caused significantly to the Mexican stock market during the election year and election period as well. The rest of the equity markets have shown mixed considerable movements following the U.S. political uncertainty. Table 7 shows the estimates on political uncertainty and global FX markets. Panel A of Table 7 clearly show that policy uncertainty of the U.S. economy has caused U.S. dollar to be weaker against the other global currencies. Similar kind of evidence are seen for the other markets in response to the dollar. The election window evidence that Republican president elect has affected adversely on seven out of nine currencies. The Mexican peso was the main loser followed by U.S. presidential election 2016. Panel B report the FX market volatility under U.S. political uncertainty. The FX market volatility stood positive and significant for the Mexican and Japanese currencies. The election window signifies that Australia, Canada, Mexico, India and Japan based FX markets’ volatility increased significantly after the announcement of president elect. Table 8 explains the change in the expected stock market volatility followed by U.S. policy uncertainty. The results reported in Panel A and B inarguably speaks that investors’ sentiment was remain bullish or bearish during the election year. However, during the election period there was significant movement observed in the implied volatility level. The ex -ante stock market volatility was on the higher side for the Asian markets while U.S. has experienced significant decline in the future stock market volatility. Table 9 presents the behavior of investors’ sentiment gauged into VIX, VXN and VXD during the presidential election debates (PED) based on the U.S. EPU index and equity market specific political uncertainty index. Panel A clearly show that first and third presidential election debates have created positive investor sentiment gauged into lower level of implied volatility. It seems that on an average VIX level was down by -1.50 point basis. Panel B also confirms the similar outcome based on equity market specific EPU index. The VIX behavior around the presidential election debates have also shown significant movement. It is seen that third presidential election debate contained most important market specific information to explain the future investment strategy. 6. Summary and conclusion There is no reason to believe that the stock market performance and political uncertainty are closely associated. Keeping in mind the above notion the aim of the work is to analyze the global Equity, FX and VIX market followed by the U.S. presidential election 2016. The U.S. election and equity market consist of nine globally recognized markets such as Nikkei 225, HSI, NIFTY50, FTSE100, DJIA, EuroStoxx50, S&PASX200, IPC and Top40 stock indices. Additionally, the study also regards most popular globally circulated nine currencies namely AUD, CAD, EUR, GBP, HKD, MXN, ZAR, INR and JPY in terms of FX market trading against the US dollar. Moreover, effects of U.S. presidential election 2016 on the investors’ sentiment index

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I. Shaikh / North American Journal of Economics and Finance 42 (2017) 546–563

have been analyzed by taking into account globally recognized volatility indices such as VXN, VXD, VIXC, VFTSE, NVIX, VXJ, VHSI, AXVI and VDAX. The novel aspect of the work is that this is the first attempt that examines the global Equity, FX and VIX market under the U.S. election uncertainty 2016. Moreover, the robustness check has also been performed by taking into account EPU index and political debates. The election stock market analyses have clearly shown that U.S. presidential election 2016 contained important market related information to explain the investors trading strategy. It is apparent from the results during election period global equity market has entered into turbulence phase and on the day of election poll announcement almost all markets have responded significantly. The equity markets such as India, Australia and Mexico lost higher among other markets. The significant negative (positive) returns implies that stock market responded abnormally and also signals possibilities of making profit following the election stock market uncertainty. This is a violation of assumption of market efficiency and indicates that in short-run markets are inefficient and abnormal gain opportunity exists. The election period and FX market also reported important findings. Out of nine there are eight FX markets their current purchasing power against the US dollar has declined. The empirical outcome shows that Mexico has lost more before the USD with fear that Mexican U.S. based outsourced market will go down under the Republican president. The Australia and European counterpart has gained in their domestic currency against USD. The empirical results on the investors’ sentiment index clearly show that due to election uncertainty VIX level across the global equity market increased significantly. As the president elect announced it goes to the normal level. Among nine markets, German markets’ volatility has shown higher degree of expected stock market volatility. The VIX level for the entire nine equity market remained under control in the Post election period. In the Post election period most of the VIX indices traded in the normal volatility zone. The below 15% level indicate the ‘bearish-run market’ across the global equity markets. The practical implications of the study are of two folds: (i) the Stock and VIX index analysis show that investors’ can make profit by taking the advantage of short-run stock market inefficiency. (ii) the FX market analysis gives an insight to the investors in the currency area, election based FX market investment can lead to minimization of loss in the currency investment. Appendix A A. Pre Election period (PREP) Event Window PREP1 PREP2

From

To

Trading Days

01-01-16 01-07-16

30-06-16 30-09-16

130 66

From

To

Trading Days

03-10-16 01-11-16 08-11-16 09-11-16 10-11-16

29-10-16 07-11-16 08-11-16 09-11-16 15-11-16

20 5 1 1 5

From

To

Trading Days

14-11-16 02-01-17

30-12-16 31-01-17

35 22

B. Election period (EP) Event Window EP1 EP2 EP3 EP4 EP5 C. Post Election period (PSEP) Event Window PSEP1 PSEP2

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Shaikh received his post graduation degree in commerce from the Veer Narmad South Gujarat University Surat; his Ph.D from Indian Institute of Technology Bombay (IIT-Bombay). Shaikh has been awarded with ‘Ph.D Theses Excellence Award’ from IIT-Bombay. He has published several papers in national and international journals of repute.