WITHDRAWN: The role of the foreign exchange risk in economic growth: Evidence from German equity markets

    The role of the foreign exchange risk in economic growth: Evidence from German equity markets Nicholas Apergis, Panagiotis G. Artikis PII: DOI: Reference:

S1044-0283(16)30054-0 doi: 10.1016/j.gfj.2016.05.005 GLOFIN 346

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Global Finance Journal

Received date: Revised date: Accepted date:

26 May 2014 13 May 2015 13 May 2016

Please cite this article as: Apergis, N. & Artikis, P.G., The role of the foreign exchange risk in economic growth: Evidence from German equity markets, Global Finance Journal (2016), doi: 10.1016/j.gfj.2016.05.005

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The role of the foreign exchange risk in economic growth: Evidence from German equity markets

Nicholas Apergis (Corresponding author)

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Business School, Northumbria University [email protected]

Panagiotis G. Artikis

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Department of Business Administration, University of Piraeus

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[email protected]

The authors need to express their profound gratitude to a reviewer of this journal whose comments and suggestions substantially enhanced the merit of this study. Needless to say, the usual disclaimer applies.

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The role of the foreign exchange risk in economic growth: Evidence from German equity markets

ABSTRACT

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This paper examines the information content of the exchange risk factor as a potential leading indicator that can predict future economic growth. The analysis incorporates a new foreign exchange risk factor that provides important insights in the relation between risk factors and the business cycle. The results display that the foreign exchange risk factor contains strong and significant information concerning future economic growth. The results survive a robustness check as well as a forecasting exercise, while they have substantial implications for portfolio choice decisions as well as for monetary and fiscal policy makers.

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Keywords: Factor equity mοdel; economic growth; foreign exchange risk; Germany

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1. Introduction

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JEL classification: E44; G11; G12; G17

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The literature in macroeconomics has developed modelling approaches to forecast economic growth with the use of variables from capital asset markets, i.e. stock prices, bond prices, credit spreads and exchange rates (Fama 1990; Schwert and Ng 1990; Cheung 1988; Aylward and Glen 2000; Binswanger 2000a, 2000b, 2004; Wongbangpo and Sharma 2002; Mauro 2003; Chaudhuri and Smiles 2004; Henry et al. 2004; Mao and Wu 2007). The rationale of such a relationship is based on the forward looking characteristic of capital markets, given that asset prices discount future expected cash flows. Peltonen at al. (2012) also empirically model macroeconomic investment in emerging economies and find that equity prices play a relevant role in the dynamics of investment, while Aaastveit and Trovik (2012) document that asset prices play a significant role as a leading indicator to improve forecasting estimates of economic growth. The literature mainly deals with relevant issues, such as the identification of the variables which proxy for common risk factors, the theoretical foundation of the relationship between potential risk factors and stock returns, and the modelling of the systematic risk using econometric methods. Although the Capital Asset Pricing Model (CAPM) has been the dominant model, empirical studies document patterns in 1

ACCEPTED MANUSCRIPT average stock returns that cannot be explained by the CAPM, shedding doubt to its validity and leading to the development of alternative asset pricing models, such as the Fama and French (1992, 1993, 1995, 1996, 1998) three factor model (3FM) and its extension the Carhart (1997) four factor model (4FM).

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In the literature that examines the information content of risk factors in terms of their capacity to predict future macroeconomic growth there have been only two attempts to investigate this relationship, the first by Liew and Vassalou (2000), and the second by Hanhardt and Ansotegui (2008). Both use as explanatory variables the returns of the size risk factor (SMB-small minus big), which is a long-short portfolio based on the market capitalization of the sample stocks, the value risk factor (HMLhigh minus low), and the momentum risk factor (WML-winners minus losers), in a long-short portfolio based on the past year’s average return of the sample stocks. Their empirical findings highlight that only the value and size risk factors contain substantial information about future GDP growth, while their results are not supportive for the momentum risk factor. However, both papers focus only on the four traditional risk factors, while ignoring other potential sources of cross-sectional return variability that might contain information on future macroeconomic growth.

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This study builds on the proposition put forward by Liew and Vassalou (2000) and Hanhardt and Ansotegui (2008) that further research should be directed towards regressions with additional explanatory variables, to examine whether introducing explicitly an exchange risk factor may contain additional information above the market risk premium in predicting future GDP growth. Therefore, given that there are certain studies in the financial economics literature providing evidence that the foreign exchange risk is a priced risk factor (Doukas et al. 1999, 2003; Vassalou 2000; Muller and Verschoo 2006, 2007; Kolari et al. 2008; Du 2009; Apergis et al. 2011) which captures a significant portion of the variability of stock returns, this paper will examine, for the first time, the information content of a new foreign exchange risk factor in predicting future macroeconomic growth. Although we do acknowledge that the foreign exchange risk is priced, while stock prices can act as leading indicators for GDP growth, we still consider that the exchange rate plays its own idiosyncratic role in predicting GDP growth. In particular, unexpected movements of the exchange rate and, mainly, persistent deviations from equilibrium levels could have significant implications for the economy. Kaminsky et al. (1998) underline that an overvaluation of the currencies is often the sign of the inconsistency of the decisions of macroeconomic policies that may lead to an unsustainable current account deficit, increasing external debt and the risk of possible speculative attacks. By contrast, it is expect that an undervaluation may drive the exchange rate to a level that encourages exports and promote growth. In addition, changes in exchange rates can easily lead to exchange rate volatility levels that disrupt the economic performance of a country. De Grauwe’s (1988) and Barkoulas et al. (2002) argue in favour of the idea of risk transfer from highly volatile investments to less risky ones 2

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by risk-averse investors, implying that there exists a negative effect of exchange rates on the volume of trade. Moreover, Kandil (2004) argues that the relationship between exchange rate changes and GDP cannot be determined a priori, because its effect can be either positive or negative due to the impact of exchange rate depreciation on the domestic economy’s interest rate. Thus, depreciation is theoretically expected to have positive effect on export since it makes domestic goods cheaper to foreign consumers. It is expected that depreciation would reduce import as a result of the higher relative price of imported goods, thus increasing net export and income, where the Marshall– Lerner condition is satisfied. Where this condition holds, domestic output would increase with depreciation through the goods market. Exchange rate can also affect domestic money supply and through it domestic income. Depreciation is theoretically expected to be accompanied by increase in money supply, leading to a reduction in interest rates and an improvement in investments. Increase in investment would lead to increase in national income and output, given the national income identity. The negative relationship between the exchange rate and GDP can be through interest rate effects of exchange rate changes. With depreciation and the consequent reduction in interest rates due to its expansionary effect on money supply, domestic interest rates turn lower relative to international interest rates. This is expected to lead to capital flights and, consequently, to reduce domestic output growth.

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The novelty of the paper is that it incorporates a foreign exchange risk factor that can provide further and important insights in the relationship between risk factors and the business cycle. Moreover, this study builds on the findings of studies showing equity returns to be a leading indicator for economic growth. In addition to the inclusion of a new risk factor in the analysis, further novelties signify that the study does not rely only on the step-wise regression analysis followed by Liew and Vassalou (2000) and Hanhardt and Ansotegui (2008), but it also performs a number of additional empirical tests, shedding more light in the relationship between equity risk and the business cycle. Specifically, it examines the direction of the relationship between equity risk and macroeconomic growth through the implementation of Granger causality tests, while it tests the predictive power of the recommended empirical model through out-of-sample dynamic forecasting. The analysis of the capacity of foreign exchange risk to forecast economic growth is expected to shed further light on the debate going on in the academic literature as well as in financial markets. Despite the perceived centrality of the role of the exchange rate risk factor to long-run growth and economic stability, the current theoretical and empirical literatures on this association offer little guidance on this subject. In particular, increased risk associated with exchange rate volatility could induce risk-averse agents to direct their resources to riskless economic activities, since such variability generates uncertainty which increases the level of riskiness, not only of trading activities, but also of financial markets activities, especially in cases that hedging may be imperfect as well as very costly as a basis for averting exchange risk. 3

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By contrast, higher risk associated with fluctuations in exchange rates could present greater opportunities for profits and thus should also increase trading and financial transactions and growth. At the same time, Reinhart and Rogoff (2004) argue that the growth effects of exchange rate risk vary with the level of financial development. Therefore, the empirical analysis will take place by explicitly taking into account the role of equity (portfolio) markets. Vassalou (2000) finds that both the common and the residual components of the foreign exchange risk are usually priced in securities. Muller and Verschoo (2006; 2007) suggest that a depreciating (appreciating) euro against foreign currencies has a net negative (positive) impact on European stock returns, while Kolari et al. (2008) find that stocks most sensitive to foreign exchange risk have lower returns and they document that the foreign exchange risk is priced in the cross-section of US stocks. Du (2009) finds evidence that the premium of the exchange rate risk is positive, while high book-to-market ratio firms have more positive loadings on the foreign exchange factor. Finally, Apergis et al. (2011) document that the foreign exchange risk factor is priced in the cross section of stock returns.

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The setting of the study is the German stock exchange (Deutsche Börse), the largest in terms of number of firms listed and total market capitalization for the entire period under investigation. It should also be noted that the time period of the present research, 1999-2012, is quite unique since it involves the recent eurozone era, while it covers ‘bull’ and ‘bear’ equity markets, three periods of economic expansion (19992002, 2004-2008, 2010-2011) and two periods of economic downturn (2002-2004, 2008-1020). Moreover, the second period of recession was quite severe, following the burst of the real estate bubble in the U.S. which triggered the recent global financial crisis. The empirical models in this paper are tested across different economic and stock market backgrounds and the implications of the results may be of particular interest for academics, investors/market participants, and policy makers. Furthermore, the number of firms used in our study, that is 373 firms on average per year, is considerable larger than the number of firms used in both Liew and Vassalou (2000) and Hanhardt and Ansotegui (2008). Section 2 describes the data used, while Section 3 presents the methodology employed. Section 4 reports the results, while Section 5 implements robustness tests. Section 6 performs a dynamic out-of-sample forecasting analysis, and, finally, Section 7 concludes the paper.

2. Data The sample used in the empirical tests consists of all firms listed on the ‘Deutsche Börse’, spanning the period 1999 to 2010. Stock prices, index market prices, market capitalization, accounting data of the sample firms and the risk-free 4

ACCEPTED MANUSCRIPT rate of return are sourced from Bloomberg, while GDP data for Germany are obtained from Datastream.

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The market risk premium (MRP) is calculated as the return on a market portfolio in excess of the risk–free rate. As a market proxy, the DAX German stock index is used, which is consisting of the 30 major German firms trading on the Frankfurt Stock Exchange. The 12-month German Treasury-Bill is used as the risk-free rate of return.

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All firms listed in 2010 are included in the initial sample. Firms that have changed name under the selected period are identified and treated as a single unit, while firms that either merged or are acquired over the study period are treated as a new unit following the event. In that sense, a selection bias towards historically successful firms is significantly limited. Listed firms, which have been under suspension for more than 50% at year t, are excluded from the final sample. Moreover, firms with no available financial information for book or market equity for at least twelve months in a row are not included in the sample either. Following Fama and French (1992), we also exclude firms with negative BE/ME ratios at 12/31 at year t–1. Finally, stock prices are adjusted for dividends and stock splits. The number of firms that pass the above criteria range from 373 in 1999 to 573 in 2010.

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Quarterly data on private investment expenses (defined as gross fixed capital formation), FDI inflows, openness (defined as the ratio of exports plus imports to GDP), and the yield curve were obtained from Datastream and spanning the same time period. In the case of the yield curve, the term spread was estimated between the 5-year and the 3-months zero-coupon interest rates.

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3. Methodological issues 3.1. The regression model We start by assessing the predictive in-sample ability of the equity risk factors through a step–wise regression analysis of future macroeconomic growth, as proxied by GDP growth, against the lagged returns of the equity risk factors. Apart from the traditional risk factors, (MRP, SMB, HML, and WML) we add the risk factor that captures the foreign exchange risk as well as other potential factors that are priced in stock prices while they are capable of explaining economic growth. First, we construct a foreign exchange risk factor (SFXI) in such a manner as to obtain the difference in the behaviour between foreign exchange sensitive and foreign exchange insensitive stocks. Next, we introduce other factors that could also provide explanatory power to economic growth. In particular:

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ACCEPTED MANUSCRIPT The liquidity shock hypothesis argues that sudden drops in asset markets liquidity cause equity prices to fall and the price of liquid assets to rise (Kiyotaki and Moore, 2008). Moreover, in a world where firms have to cope with financing constraints on their investments, this fall in equity prices reduces the funds for investments a firm can raise by issuing equity and/or using equity as collateral in borrowing. As a result, investments fall, output follows and a recession starts. The liquidity shock hypothesis has received wide attention because of its immediate policy implications. Investment is also a fundamental determinant of economic growth identified by both neoclassical and endogenous growth theories. The importance attached to investment has led to an enormous amount of empirical studies examining the relationship between investment and economic growth (De Long and Summers 1991; Mankiw et al. 1992; Barro and Sala-i-Martin 1995; Podrecca and Carmeci 2001). Foreign Direct Investment (FDI) has also played a crucial role of internationalising economic activity and it is a primary source of technology transfer and economic growth. This major role is stressed in several models of endogenous growth theories. The empirical literature examining the impact of FDI on growth has provided relatively consistent findings in favour of a significant positive link between the two (Hermes and Lensink 2000; Lensink and Morrissey 2006). Openness to trade is another significant determinant of growth performance. There are sound theoretical grounds for arguing that there is a strong and positive link between openness and economic growth. Openness enables the exploitation of comparative advantage, technology transfer and diffusion of knowledge, increasing scale economies and exposure to competition. A large number of studies have confirmed such a positive relation (Edwards 1998; Dollar and Kraay 2000; Vamvakidis 2002). The yield curve (the difference between the yields on long- and short-term Treasury securities) has been found useful for forecasting such variables as output growth, inflation, industrial production, consumption, and recessions. The mechanism that dictates this association is based on the expectations hypothesis of the term structure, which is the foundation of many explanations of the yield curve’s usefulness in forecasting output growth and recessions. The expectations hypothesis holds that long-term interest rates equal the sum of current and expected future short-term interest rates plus a term premium. The term premium explains why the yield curve usually slopes upward, i.e. why the yields on long-term securities usually exceed those on short-term securities (Estrella, 2005; Estrella and Trubin, 2006).

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ACCEPTED MANUSCRIPT The model specification involves a set of multivariate regressions, providing further insights about the marginal benefit of adding new risk factors: GDPgrowth(t,t+4) = α1 + b1 MRPt-4,t + b2 SMBt-4,t + b3 HMLt-4,t + b4 WMLt-4,t +

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b5 LIQt-4,t + b6 INVt-4,t + b7 FDIt-4,t + b8 OPENt-4,t + b9 YIELDt,t-4 + (1)

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GDPgrowth(t,t+4) = α2 + c1 MRPt-4,t + c2 SMBt-4,t + c3 HMLt-4,t + c4 WMLt-4,t + c10 SFXIt,t-4 + ηt,t+4

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c5 LIQt-4,t + c6 INVt-4,t + c7 FDIt-4,t + c8 OPENt-4,t + c9 YIELDt-4,t + (2)

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where, GDPgrowth(t,t+4) is the continuously compounded GDP growth, MRP is the market risk premium, SMB is the size mimicking portfolio, HML is the value mimicking portfolio, WML is the momentum mimicking portfolio, SFXI is the foreign exchange risk factor, LIQ is the liquidity proxy, INV denotes the private investment expenses as a percentage of GDP, FDI is FDI inflows as a percentage of GDP, OPEN describes openness and YIELD is the yield curve. Finally ε and η describe the errors terms. In other words, in both specifications of the research model, future GDP growth rate is the dependent factor, while both regressions the independent drivers are the four factors of the Fama and French model plus those proposed by Carhart (1997). Both regressions include the same number of control variables (i.e., LIQ, INV, FDI, OPEN and YIELD), while their difference is that only equation (2) includes the exchange rate risk factor.

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3.2. Independent variables To construct the SMB, ΗΜL and WML risk factors at the end of June of each year, sample stocks are independently sorted: a) by size (capitalization), measured at the end of June, and allocated into two size bisects, b) by their book equity to market equity ratio (BE/ME), measured at the end of December of the previous year, and allocated into three value trisects, and c) by their average monthly return over a 11month period beginning in June of the prior year and ending in May of the current year, and allocated into three momentum trisects. At the intersection of the two sizes, three values and three momentum portfolios, 18 portfolios are formed and are used for the construction of the: a) SMB (small minus big) factor, a portfolio that is long on small sized stocks and short on big sized stocks, b) HML (high minus low) factor, a portfolio that is long on high BE/ME stocks and short on low BE/ME stocks and c) WML (winners minus losers) factor, a portfolio that is long on winner stocks and short on loser stocks. 7

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To construct the SFXI risk factor, the sensitivity of all sample stocks to exchange rate movements over time is first estimated. The sensitivity of each stock to foreign exchange movements is defined as the correlation between stock returns and contemporaneous changes in the value of the Euro. Specifically, this is achieved by regressing each stock’s excess return (ER) on the foreign exchange return series (FX), which captures the return on the Euro per currency basket and simultaneously controlling for size, value and momentum effects: ERt = (Ri-Rf)t = ai + bi MRPt + si SMBt + hi HMLt + wi WMLt + fxi FXt + εi

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where,

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ai = the intercept term

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Ri = returns of stock i Rf = the risk free asset

FXt = returns of the Euro per currency basket ei = the error term

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We measure the return of the foreign exchange series (FXt) using the effective exchange rate of the Euro. It is based on the weighted average of bilateral Euro exchange rates against 21 major trading partners in the Euro area. The weights capture third-market effects and are based on trade in manufactured goods with the main trading partners of Eurozone countries. If the Euro effective exchange index goes up, more foreign currency can be obtained, on average, for €1 and the Euro appreciates vis-à-vis the other currencies.

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Equation (3) is estimated using daily data and one-year rolling periods beginning in July each year. For example, we first estimate Equation (4) for each firm during July 1999 to June 2000 and obtain firm-specific values of the fi coefficients for 30/6/2000. We repeat the procedure for the period from July 2000 to June 2001 to obtain firm-specific fi coefficients for 30/6/2001, and thereafter we continue the process until 30/6/2011. After obtaining annual measures of firm-specific foreign exchange exposure, fxi, from Equation (3), we rank firms based on the value of these coefficients into 10 portfolios. Portfolios 1 and 10 consist of stocks with the highest absolute (positive or negative) foreign exchange exposure. In line with the work by Kolari et. al. (2008), we construct a foreign exchange risk factor (SFXI) in such a manner as to obtain a monotonic relationship between risk and expected returns. We do this by creating a zero-investment portfolio that takes long positions in stocks that have extreme negative or positive sensitivity to the foreign exchange risk (portfolios ranked 1 and 10) and short positions in all other stocks (portfolios ranked 2 through 9). 8

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Having constructed the factor mimicking portfolios at the end of June in each year, we then calculate their equally-weighted monthly returns for the following year from July through June in the next year. In June of the next year we reform the portfolios and calculate returns for the following year. The annual rebalancing has been adopted because the book value of the sample firms is available once a year (i.e., at the end of each calendar year), thus, making the HML factor inconsistent if intraannual balancing is followed. We form portfolios from June 2000 through June 2011 and the monthly returns of the risk factors are from July 2000 through June 2012. Finally, the 120 monthly returns of the risk factors are adjusted to 40 quarterly returns, in order to meet the quarterly frequency of GDP data.

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R i,t

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ILR i,T  1 D T 

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To proxy liquidity, we make use of the Amihud (2002) illiquidity ratio. This liquidity metric is the ratio of absolute stock returns to monetary volume on a daily basis, displaying how much prices move for each monetary unit of trades. The cost associated with larger trades is more accurately captured in the price impact of a trade. Hasbrouck (2009) shows that the ratio is the best available price-impact proxy constructed from daily data. The ILR captures the sensitivity of prices to trading volumes, since it is a measure of the elasticity dimension of liquidity. The ILR is calculated as: (4)

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where DT is the number of observations within a time window T, |Ri,t| is the absolute return at day t for stock i, and VOLi,t is the trading volume in monetary values at day t for stock i. The ILR essentially provides an illiquidity measure, since a high value indicates low liquidity (i.e., a high price impact of trades). Moreover, a high price impact suggests that the market depth is low and a smaller volume is needed to move that price.

4. Empirical results 4.1. Descriptive statistics The findings from the descriptive statistics of the research variables are reported in Table 1. GDP, MRP, SMB, INV, FDI, OPEN and YIELD are normally distributed, whereas for the HML, WML and SFXI risk factors we reject the null hypothesis of normally distributed data when considering the Jarque-Bera statistic (Cochrane, 2005). The SMB, HML and WML factors have all a positive average return, documenting a size, value and momentum effect (Fama and French, 1992, 1993, 1995, 1996, 1998; Jegadeesh and Titman, 1993; Rouwenhorst, 1998; Liew and Vassalou, 2000; Hanhardt and Ansotegui, 2008). Small cap, high BE/ME ratio and winner firms outperform large cap, low BE/ME ratio and looser firms. The most 9

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interesting finding, however, is that the SFXI risk factor has the largest average return in absolute values as compared to the other risk factors, which is statistical significant at the 1% level. Foreign exchange insensitive firms outperform foreign exchange sensitive firms by 20.18% on average each year. Thus, from an investor’s perspective, the strategy that is based on the foreign exchange sensitivity of the sample firms produces the highest returns as compared to the ones based on the traditional risk factors (i.e., SMB, HML and WML).

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[Insert Table 1 about here]

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4.2. Regression models

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The empirical results of the regressions models (i.e., equations (1) and (2)) are reported in Table 2. As GDP growth rates are observed at quarterly frequencies, successive annual growth rates have three overlapping quarters. This may cause autocorrelation among the residuals. We correct for the presence of autocorrelation and heteroskedasticity of the error terms by using the Newey and West (1987) estimator, setting the lags equal to four.

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Across both models, the coefficients of the independent factors remain quite stable in terms of sign and statistical significance with only small variations in magnitude. The loading of the market risk premium turns out to be, as expected, positive and highly statistically significant in line with the findings of Aylward and Geln (1995), Liew and Vassalou (2000) and Hanhardt and Ansotegui (2008). More specifically, the size, value and momentum factors appear to have a negative and statistically significant impact on future GDP growth, in line with the findings of Hanhardt and Ansotegui (2008). This means that small capitalization stocks, firms with high BE/ME equity ratio and winner stocks have higher returns when a slowdown in the economy is anticipated. By contrast, when the expectations are for an expansion in the economy, then it is highly likely that large capitalization stocks, firms with low BE/ME equity ratio and looser stocks prosper. Moreover, we can clearly see that private fixed capital formation, FDI inflows, openness and the yield curve have a positive effect on future output growth, while the liquidity metric has a statistically significant negative link with GDP. This behaviour of the illiquidity proxy has been expected, since its increase indicates a fall in market liquidity and, consequently, it should be negatively related with economic growth. Focusing on the variable of the primary interest in this study, the foreign exchange factor exhibits a strong positive and statistical significant relationship with future macroeconomic growth. Firms sensitive to foreign exchange movements, prosper when an economic upturn is anticipated and vice versa. This might be explained by the fact that firms sensitive to foreign exchange risk are those that do not hedge effectively a large part of their foreign exchange exposure, assuming higher 10

ACCEPTED MANUSCRIPT levels of risk. Investors choose not to hold stocks that have higher levels of foreign exchange risk when they believe that economic growth is sluggish or even negative and they choose to invest in foreign exchange insensitive stocks.

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The SFXI risk factor has the largest coefficient in absolute terms vis-à-vis the other risk factors, indicating that it captures a large portion of the variation in future GDP growth. The results also highlight the substantial increase of the adjusted R 2 following the introduction of the exchange rate risk factor, i.e. from 0.42 to 0.54, indicating that this particular risk factor has incremental explanatory (and potentially forecasting) power relative to the remaining market risk premia in the prediction of the future state of the economy, and thus, it should not be omitted. Finally, diagnostics regarding serial correlation and model specification support the absence of model misspecifications in both regressions models. [Insert Table 2 about here]

5. Robustness tests

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To test the robustness of our results we also measure the returns of the risk factors (SMB, HML, WML and SFXI) across different states of the economy. This set of the robustness test involves the calculation of the returns of the four equity risk factors at different states of the economy. In doing so, we associate the annual GDP growth rate of next year with the past year annual return per trading strategy based on each of the risk factors and short by the GDP growth rate every quarter. The highest 33.33% of future GDP growth rate is characterized as ‘good states’ of the economy, the lowest 33.33% of future GDP growth rate is characterized as ‘bad states’ of the economy and the remaining 33.33% is characterized as ‘mid-state’. Finally, we calculate the difference in return for each trading strategy between ‘good’ and ‘bad’ states of the economy. The empirical findings are reported in Table 3. The market risk premium has a statistically significant positive difference between ‘good’ and ‘bad’ states of the economy, implying that it positively related to future macroeconomic growth, which further validates the results in the previous section. The size factor shows a negative (and statistically significant) average return in both states of the economy, indicating that large sized firms have a better performance than small sized firms, while the correlation with future macroeconomic growth is negative, also validating the results in the previous section. Furthermore, the difference between the ‘good’ and ‘bad’ sate is negative, showing that small sized firms have a better performance prior to an economic downturn, since the returns of ‘bad’ states are higher on average than those of ‘good’ states; these findings are in line with those provided by Hanhardt and Ansotegui (2008). According to the authors, one potential explanation lies in the argument that small sized firms may react faster to anticipated market changes vis-à11

ACCEPTED MANUSCRIPT vis large sized firms. It may be more difficult for large sized firms to adapt in a changing macroeconomic environment and they may follow more the general market trend.

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The value factor has a negative difference in returns between ‘good’ and ‘bad’ states, showing a negative correlation with future economic activity and in line with the findings in the previous section. Investors choose high BE/ME ratio stocks when an economic downturn is anticipated, resulting in higher returns for the value factor in ‘bad’ states of the economy. The results validate those from the regression models, since, they show that ‘winner’ stocks have a better performance than ‘loser’ stocks when a downturn in the economy is anticipated. Finally, stocks sensitive to foreign exchange movements over perform those that are insensitive to foreign exchange movements prior to an economic recovery, while the difference in returns has the highest magnitude across all risk factors. [Insert Table 3 about here]

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6. Out-of-sample forecasting accuracy

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In this part of the paper we are interested in the forecasting power of the modelling approach. Consider the prediction error at time t; this error is obtained using a fit obtained including observations realized after time t, and in this sense utilizes information that would not be available to a forecaster at time t. In that sense, we can clearly identify the forecasting power of the regression specification by conducting an out-of-sample forecasting exercise. Observations from all the independent variables are used to predict future growth with truncated data. The parameter estimates from the recursive regression are used to generate a series of fitted values. We make use of the regression equations (1) and (2) and focus on forecast horizons, h, of 1, 4 and 12 periods ahead. To generate those forecasting periods we estimate a horizon-specific model that can provide direct multiperiod-ahead forecasts based on the models described by equations (1) and (2). Out-of sample forecasts are generated recursively. The forecasting performance of the models is assessed by calculating the ratio of the Mean Squared Forecasting Error (MSFE) of model (2) over the MSFE of the benchmark (equation (1)) model. A ratio less than one recommends superiority of the candidate model over the benchmark model and indicates that the candidate variable(s) is (are) a useful predictor for the variable of interest (i.e., output growth). To this empirical end, we use the F-statistic proposed by McCracken (2004) to compare the forecasting performance of model (2): 12

ACCEPTED MANUSCRIPT p p  OOS - F   ε1,2 t  ε 22,t  P 1  ε 22,t t 1  t 1 

(5)

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where εit, i=1,2 are the forecast errors of the benchmark and the alternative model, respectively and P is the number of out-of-sample forecasts. Under the null hypothesis, the two models have equal MSFE, while under the alternative hypothesis the MSFE of the alternative model is less than that of the benchmark. The statistic OOS-F compares the relative forecasting performance between the model of equation (2) and that which contains fewer predictors (i.e., equation (1)).

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The estimation period is 1999 to 2006:4, while the out-of-sample forecast period is 2007:01 to 2010:04. In each step, we re-estimate the model by adding one observation at a time. The h−step ahead forecasts are generated for 1, 4 and 12 quarters ahead and the corresponding MSFE is calculated. The results are reported in Table 4. Specifically, the second row reports the MSFE of the benchmark model (1), while row 3 tabulates the ratio of the MSFE of equations (2) over that of the benchmark model (equation (1)). A value lower than one suggests that the additional variable (i.e., in our case the SFXI factor) improves the forecast accuracy of future output growth.

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The second row in Table 4 reveals that the benchmark model (equation (1)) generates large MSFEs (larger than one) in both the 4-quarter and 12- quarter forecasting horizons, implying that for longer than one quarter time span the benchmark model has low success in predicting future output growth. Row 3 presents the ratio of the MSFE of the augmented model to the MSFE of the benchmark model. Improvements in forecast accuracy are observed across all three forecasting horizons under study. Admittedly, these reductions in the MFSEs are not small and may make a substantial difference. The model described by equation (2) does well vis-à-vis its competitor for the output growth forecasts. The last row in Table 4 reports the OOS-F statistic. Under the null hypothesis, the MSFE of the benchmark model equals that of the alternative (augmented) model. The findings clearly lend more support to the adoption of the model described by equation (2) as the best predictor of output growth across all h-forecasting horizons. [Insert Table 4 about here]

7. Conclusions The objective of this paper was to examine the impact of the foreign exchange risk factor and macroeconomic growth in Germany. Using data from the ‘Deutsche Börse’ market spanning the period 1999 to 2012, the empirical findings documented that the foreign exchange risk factor generated the highest returns vis-a-vis to the 13

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traditional risk factors. In particular, the results showed a positive and robust relationship between the foreign exchange risk factor and macroeconomic growth. Firms sensitive to the foreign exchange risk thrive when an economic upturn is anticipated and firms insensitive to the foreign exchange risk have larger returns when an economic downturn is coming up. The importance of the foreign exchange risk factor in predicting future macroeconomic growth was further validated by a forecasting exercise in a sense that the model that included the foreign exchange risk factor turned out to be the best predictor of growth in the out-of-sample forecasting analysis.

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Our analysis establishes the importance of currency risk as a pricing factor, even after accounting for the other dimensions of risk, for stock markets as well as for economic growth. In other words, currency risk represents a significant portion of total premium. The results also suggest that the use of an asset pricing model without exchange risk may be misspecified, because the significance of the other risk factors may be overestimated since it may subsume the missing exchange risk factor. Thus, taking into consideration all available risks is clearly important for investment and risk management decisions coming from portfolio investors and managers or from firms interested in foreign direct investment activities. Nevertheless, in terms of trade transactions the exposure of individual member economies to movements in the exchange rate depends on how much they trade with non-euro area countries, the geographical orientation of such trade, and the currency's movements against the respective trade partners' currencies. The exchange rate risk factor can substantially affect investors who are exposed to exchange rate risk on their asset positions as well. In additions, its presence may cause large currency mismatches in corporate, banking or household sector balance sheets, leading to increased default risks, while a greater exchange rate risk can reduce liquidity in foreign exchange and other asset markets, thus, exerting a further negative impact on future economic activity. The findings could have serious implications for cross-border capital flows as well and not necessarily just for Germany. Such capital flows may respond more to changes in exchange rate changes/volatility and their uncertainty when capital account openness is greater. Although this is not prevalent for Eurozone or other developed economies, foreign holdings controls could be put into effect, with further negative spillovers to consumption and investment decisions and, thus, to future output. While the exchange risk factor could also influence the degree of pass through of exchange rate factors to domestic asset yields, there are also substantial implications for hedging activities and their effect on future output. McCauley et al. (2014) argue that such hedging activities conducted in forward markets, could render negative costs onto domestic local currency asset yields and, therefore, onto economic activity. In particular, when the exchange rate risk factor is hedged less, the impact of exchange rate risk on local currency asset yields could be larger.

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Given that Germany is part of the Eurozone, the results recommend a number of policy steps needed to prevent future crises related wither to the country or to the Eurozone or to a global crisis event, such as improving regulations of excessive surges of portfolio movements, more counter-cyclical macro-economic policies, and more strengthening of domestic financial systems. Although within the Eurozone area the foreign exchange risk factor does not exist due to the workings of the common currency, this risk factor remains in effect for trade and financial transactions of German firms and investors vis-à-vis those on a global basis. Therefore, given that the standard response to higher exchange rate risks includes sharp changes in interest rates and significant fiscal tightening, the outcome should equally generate some negative and painful effects on future economic activity. In particular, sharp changes in interest rates, through monetary policy changes, have not been quite effective across all cases and only when other measures (e.g. fiscal) are taken simultaneously. If interest rates changes persist, they may have very damaging and undesirable effects due to their recessionary or inflationary impact on the real economy, on changes in current account competitiveness, and on disruptive portfolio capital movements that deemphasize domestic monetary policies. At the same time, fiscal austerity measures can further exacerbate growth and unemployment issues in the new Eurozone economy. The recent sovereign debt crisis across a number of Eurozone countries just lends support to these arguments.

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Therefore, the new financial and monetary architecture requires coordination actions across countries, given the context of a single financial market. To this end, efforts to strengthen the financial architecture of the European and the global economy should be seen as complementary to a more active use of national fiscal policies.

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ACCEPTED MANUSCRIPT Table 1 Descriptive statistics – variables of the research models

ΜRP

SMB

Mean

0.0185

-0.0259

0.0061

Median

0.0162

0.1067

Std. Dev.

0.0237

Skewness

SFXI

LIQ

0.0236

0.0218

-0.2018

0.6749

-0.0067

0.0110

0.01501

-0.1501

0.4656

0.3141

0.0375

0.0304

0.0353

0.2919

0.0463

-0.6912

-0.7485

-0.0192

1.7275

1.4744

-0.9471

-0.2575

Kurtosis

3.3281

2.5515

2.4885

5.9460

5.3731

3.3068

3.1096

JarqueBera

3.3619

p-value

0.19

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WML

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GDP

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The table reports the annualized summary statistics for all factors of the research model. GDP is calculated as the continuously compounded growth rate and is seasonally adjusted. MRP is the market premium. SMB is the realized return on a portfolio that is long on small sized firms and short on big sized firms. HML is the realized return on the portfolio that is long on high BE/ME equity stocks and short on low BE/ME equity stocks. WML is the realized return on the portfolio that is long on winner stocks and short on loser stocks. SXFI is the realized return on the portfolio that is long on foreign exchange sensitive stocks and short on foreign exchange insensitive. LIQ is the stock market liquidity measure. INV is the compounded fixed capital formation rate as a percentage of GDP. FDI is FDI inflows as a percentage of GDP. *, ** denote significance at the 1% and 5% level of significance, respectively.

HML

2.2936

0.4468

34.3616*

23.8849*

6.1427**

0.13

0.80

0.00

0.00

0.04

0.77

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4.0782

INV

FDI

OPEN

YIELD

0.269

0.0526

0.8174

5.5893

Median

0.248

0.0497

0.7763

5.3285

Std. Dev.

1.2760

0.8952

2.5057

2.7846

Skewness

0.0289

0.0536

0.0328

0.7529

Kurtosis

2.6256

2.8471

2.8742

3.2371

JarqueBera

1.3152

1.2109

0.8195

3.6329

p-value

0.36

0.41

0.59

0.28

Mean

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2

Baseline results: Empirical models GDPgrowth(t,t+4) = α1 + b1 MRPt-4,t + b2 SMBt-4,t + b3 HMLt-4,t + b4 WMLt-4,t +

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b5 LIQt-4,t + b6 INVt-4,t + (1)

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GDPgrowth(t,t+4) = α2 + c1 MRPt-4,t + c2 SMBt-4,t + c3 HMLt-4,t + c4 WMLt-4,t + c5 LIQt-4,t + c6 INVt-4,t + c7 FDIt-4,t + c8 OPENt-4,t + c9 YIELDt-4,t + c10 SFXIt,t-4 + ηt,t+4

(2)

SMB

HML

(4.56)

(4.41)

0.0473*

0.0449*

(5.38)

(4.68)

-0.0821*

-0.0763*

(-5.46)

(-5.18)

-0.2197*

-0.1895*

(-4.60) -0.2184* (-5.74)

-0.2008* (-5.27) 0.0974*

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LIQ

(-4.36)

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WML

0.0252*

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GDP, MRP, SMB, HML, WML, SFXI, LIQ, INV, FDI, OPEN and YIELD as defined in Table 1. LM is the serial correlation diagnostic test, while RESET is the model specification test. Figures in parentheses denote t-stats and those in brackets p-values. * denotes statistical significance at 1%.

(5.58)

-3.5482*

-3.2988*

(-4.86)

(-4.47)

1.2495*

1.1952*

(5.28)

(4.93)

0.9814*

0.9246*

(4.96)

(5.11)

1.4582*

1.3857*

(5.04)

(4.71)

1.418*

1.328*

(4.96)

(4.58)

INV

FDI

OPEN

YIELD

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R2-adj

0.42

0.54

LM

1.37

1.32

[0.16]

[0.20]

1.22

1.11

[0.28]

[0.36]

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RESET

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ACCEPTED MANUSCRIPT Table 3 Performance of risk factors at different states of the economy

significance at 1%. Good State

Bad State

Difference

MRP

12.39%

-15.08%

27.47%*

SMB

-5.86%

-4.51%

SC

(3.95)

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-10.37%*

-2.41%

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(-3.82)

HML

1.75%

-0.66% (-1.20)

3.78%

8.44%

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WML

1.28%

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SFXI

-34.52%

-4.66%* (-3.58) -33.24%* (4.95)

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Figures in parentheses denote t-statistics. * indicates statistical

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Best model refers to the model with the minimum MSFE. * denotes the rejection of the null hypothesis that the benchmark model equals the enhanced model.

1-quarter

4-quarter

12-quarter

MSFE-Equation (1)

1.386

2.548

4.762

relative to

0.729

MSFE-Equation (1)

0.319*

SC

0.831

0.858

0.185*

0.252*

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OOS-F test (2) vs (1)

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Horizon:

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Output growth/out-of-sample forecasts

1