The impact of interest rate and exchange rate volatility on banks' stock returns and volatility: Evidence from Turkey

This paper investigates the effects of interest rate and foreign exchange rate changes on Turkish banks' stock returns using the OLS and GARCH estimation models. The results suggest that interest rate and exchange rate changes have a negative and significant impact on the conditional bank stock return. Also, bank stock return sensitivities are found to be stronger for market return than interest rates and exchange rates, implying that market return plays an important role in determining the dynamics of conditional return of bank stocks. The results further indicate that interest rate and exchange rate volatility are the major determinants of the conditional bank stock return volatility.     

DYNAMIC CORRELATION BETWEEN STOCK RETURNS AND EXCHANGE RATE AND ITS DEPENDENCE ON THE CONDITIONAL VOLATILITIES – THE CASE OF SEVERAL EASTERN EUROPEAN COUNTRIES

The objective of the paper is to determine whether the linkage between stock returns and exchange rates in several Eastern European countries was in accordance with the flow oriented model or the portfolio‐balance approach. The dynamic interdependence between exchange rate and stock returns is determined using the Dynamic Conditional Correlation (DCC) framework. The results pointed to a negative dynamic correlation which is in line with portfolio‐balance approach. Rolling regression revealed that conditional correlation was affected primarily by conditional volatility of currency, while the impact of stock returns volatility was negligible.     

Asset prices and economic fluctuations: The implications of stochastic volatility

This paper investigates whether the multi-factor stochastic volatility of stock returns is related to economic fluctuations and affects asset prices. We address these issues in a dynamic Fama-French three-factor volatility model framework. Consistent with the ICAPM with stochastic volatility (Campbell et al., 2017), we find that the conditional volatility of the size and value factors is significantly related to economic uncertainty. These volatilities are also significant pricing factors. The out-of-sample forecasting analysis further reveals that the conditional volatility can predict stock returns and deliver economic gain in asset allocation. Our analysis sharpens the understanding on the link between the stock market and economic fundamentals.     

Asymmetric volatility of stock returns during the Asian crisis: Evidence from Indonesia

This paper investigates the conditional volatility in stock returns in Indonesia over the period covered by the Asian crisis. Rolling regression parameter estimates from three asymmetric volatility models suggested that all parameters, including those capturing asymmetric response, were time-varying. The precise pattern of adjustment was sensitive to model selection. Nevertheless, increases in asymmetric response patterns appear to coincide with the very large exchange rate devaluations in the rupiah over this period and were followed by more general symmetric short-term volatility in the post crisis period. Estimates from a smooth transition volatility model indicated both sign and size asymmetries during the crisis period.     

Volatility Spillovers and Contagion from Mature to Emerging Stock Markets

This paper models volatility spillovers from mature to emerging stock markets, tests for changes in the transmission mechanism during turbulences in mature markets, and examines the implications for conditional correlations between mature and emerging market returns. Tri‐variate GARCH–BEKK models of returns in mature, regional emerging, and local emerging markets are estimated for 41 emerging market economies (EMEs). Wald tests suggest that mature market volatility affects conditional variances in many emerging markets. Moreover, spillover parameters change during turbulent episodes. In the majority of the sample EMEs, conditional correlations between local and mature markets increase during these episodes. While conditional variances in local markets rise as well, volatility in mature markets rises more, and this shift is the main factor behind the increase in conditional correlations. With few exceptions, conditional beta coefficients between mature and emerging markets tend to be unchanged or lower during turbulences.     

Determinants of stock market comovements among US and emerging economies during the US financial crisis

By analyzing the dynamic conditional correlations (DCC) of the daily stock returns of 10 emerging economies in comparison with those of the US for the period of 2006–2010, we find different patterns of crisis spillover among 10 emerging economies. While a group of countries has three distinctive phases of crisis spillover (contagion, herding, and post-crisis adjustment), other groups show different phases of crisis spillover. It is also shown that increases in CDS spread and TED spread decrease conditional correlations while increases in foreign institutional investment, exchange market volatility, and the VIX index of the S&P 500 increase conditional correlations.     

Asymmetric effect of advertising on the Chinese stock market

In this paper, we investigate whether investor attention to advertising has an asymmetric effect on Chinese stock returns by using a multivariate Markov switching model with time-varying regime transition probabilities. Using the Chinese stock market as a setting, we obtain lagged conditional volatility from generalized autoregressive conditional heteroskedasticity (GARCH) for modelling the time-varying transition probabilities of the regime-switching process to capture changes in the market regime. Our evidence documents that the high advertising portfolio does earn higher abnormal return than the low advertising portfolio in low-volatility periods. In high-volatility periods, however, the abnormal return is insignificant when the firm increases advertising spending. Our results support the behavioural model argument that in high-volatility period, advertising information diffuses slowly due to cognitive dissonance. Thus, the effect of advertising on stock returns is asymmetric, and it shows statistical significance in low-volatility periods.     

FINANCIAL CRISES AND INTERNATIONAL STOCK MARKET VOLATILITY TRANSMISSION

This paper examines the interplay between stock market returns and their volatility, focusing on the Asian and global financial crises of 1997–98 and 2008–09 for Australia, Singapore, the UK, and the US. We use a multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model and weekly data (January 1992–June 2009). Based on the results obtained from the mean return equations, we could not find any significant impact on returns arising from the Asian crisis and more recent global financial crises across these four markets. However, both crises significantly increased the stock return volatilities across all of the four markets. Not surprisingly, it is also found that the US stock market is the most crucial market impacting on the volatilities of smaller economies such as Australia. Our results provide evidence of own and cross ARCH and GARCH effects among all four markets, suggesting the existence of significant volatility and cross volatility spillovers across all four markets. A high degree of time‐varying co‐volatility among these markets indicates that investors will be highly unlikely to benefit from diversifying their financial portfolio by acquiring stocks within these four countries only.     

An empirical analysis of margin debt

Despite the extensive literature on margin requirements and stock market volatility, few articles consider the determinants of margin borrowing. Our trivariate autoregressive model of margin debt, stock returns, and the broker call rate shows that margin debt responds positively to stock returns and negatively to interest rate changes over the period 1951–2001. We also document an asymmetry, with margin debt responding quickly to stock market downturns and more gradually to market upswings.     

The Effect of Annual Earnings Announcements on the Chinese Stock Markets

This paper examines the annual earnings announcement effect of the stock markets in China. The investigation is based on events analysis and carried out by modeling the daily changes of stock returns using the M-EGARCH approach, by testing the news effects of annual earnings announcement on the conditional mean of abnormal return and the variance of the returns. It is found that a higher than expected earnings announcement leads to a rise in the conditional mean of stock returns on days before the news announcement and a fall afterwards. The conditional volatility of the changes are significantly reduced by bigger absolute values of reported earnings before the news announcement and increased afterwards, supporting the rejection of semi-strong-form efficiency.     

A two-regime threshold model with conditional skewed Student t distributions for stock returns

This paper proposes a two-regime threshold model for the conditional distribution of stock returns in which returns follow a distinct skewed Student t distribution within each regime: the model allows capturing time variation in the conditional distribution of returns, as well as higher order moments. An application of the model to daily U.S. stock returns illustrates the advantages of the proposed model in comparison to alternative specifications: the model performs well in terms of in-sample fit; it more accurately estimates the conditional volatility; and it produces useful risk assessment as measured by the term structure of value at risk.     

Exchange Rate Exposure of Chinese Firms at the Industry and Firm Level

This study investigates the exchange rate exposure of Chinese firms at the industry and firm level based on the conventional capital asset pricing model (CAPM) framework. At the industry level, the dynamic conditional correlation MGARCH (DCC MGARCH) estimates demonstrate that the market model and three‐factor model are appropriate for exposure measurements, and industry returns are more likely to be exposed to unanticipated changes in the real exchange rate and the trade‐weighted effective exchange rate, particularly for manufacturing industries. At the firm level, although the seemingly unrelated regression (SUR) estimates vary across markets, it is apparent that there is a relationship between firm size and exposure effects, which also show that lagged exchange rate changes have significant exposure effects on firm returns. This study finally suggests that non‐financial firms should set up special commissions to hedge currency risks of their future cash flows.     

Modeling the Taiwan Stock Market and International Linkages

This paper analyzes the Taiwan stock market and examines its price and volatility linkages with those of the United States. In particular, it tests the hypothesis that the short-term volatility and price changes spill over from the developed markets, mainly the United States, to the emerging Taiwan stock market. The model and the test are built upon Engle's ARCH (autoregressive conditional heteroskedasticity) and Engle and Kroner's M-GARCH (multivariate generalized ARCH) models. The paper differs from previous studies on the Taiwan stock market in three respects. First, instead of using daily closing prices, it uses close-to-open and open-to-close returns to avoid the problem of overlapping samples. It carefully models the day-of-the-week effect in daily data to avoid misspecification of the model. Second, to circumvent the generated regressor problem arising from the two-step estimation procedure, it also employs the M-GARCH model where all parameters are estimated simultaneously. Third, the misspecification test is carried out on various kinds of asymmetric ARCH factors. A substantial volatility spillover effect is found from the US stock market to the Taiwan stock market, especially for the model using close-to-open returns. There is also evidence supporting a spillover effect in price changes. The findings can be explained by the recent gradual opening of the Taiwan stock market to foreign investors.     

Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model

The predictability of stock return dynamics is a topic discussed most frequently in empirical studies; however, no unanimous conclusion has yet been reached due to the ignorance of structural changes in stock price dynamics. This study applies various regime switching GJR-GARCH models to analyze the effects of macroeconomic variables (interest rate, dividend yield, and default premium) on stock return movements (including conditional mean, conditional variance, and transition probabilities) in the U.S. stock market, so as to clearly compare the predictive validity of stable and volatile states, as well as compare the in-sample and out-of-sample portfolio performance of regime switching models. The empirical results show that macro factors can affect the stock return dynamics through two different channels, and that the magnitude of their influences on returns and volatility is not constant. The effects of the three economic variables on returns are not time-invariant, but are closely related to stock market fluctuations, and the strength of predictability in a volatile regime is far greater than that in a stable regime. It is found that interest rate and dividend yield seem to play an important role in predicting conditional variance, and out-of-sample performance is largely eroded when the effects of these two factors on volatility are ignored. In addition, the three macro factors do not play any role in predicting transition probabilities.     

Exchange rate volatility and export performance: a cointegrated VAR approach

During the last decades Norwegian exporters have–despite various forms of exchange rate targeting–faced a rather volatile exchange rate which may have influenced their behaviour. Recently, the shift to inflation targeting and a freely floating exchange rate has brought about an even more volatile exchange rate. We examine the causal link between export performance and exchange rate volatility across different monetary policy regimes within the cointegrated Vector Autoregression (VAR) framework using the implied conditional variance from a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model as a measure of volatility. Although treating the volatility measure as either a stationary or a nonstationary variable in the VAR, we are not able to find any evidence suggesting that export performance has been significantly affected by exchange rate uncertainty. We find, however, that volatility changes proxied by blip dummies related to the monetary policy change from a fixed to a managed floating exchange rate and the Asian financial crises during the 1990s enter significantly in a dynamic model for export growth–in which the level of relative prices and world market demand together with the level of exports constitute a significant cointegration relationship. A forecasting exercise on the dynamic model rejects the hypothesis that increased exchange rate volatility in the wake of inflation targeting in the monetary policy has had a significant impact on export performance.     

From Returns to Tweets and Back: An Investigation of the Stocks in the Dow Jones Industrial Average

A sizeable percentage of investors are using social media to obtain information about companies (Cogent Research [2008]). As a consequence, social media content about firms may have an impact on stock prices (Hachman [2011]). Various studies utilize social media content to forecast stock market-related factors such as returns, volatility, or trading volume. The objective of this article is to investigate whether a bidirectional intraday relationship between stock returns and volatility and tweets exists. The study analyzed 150,180 minute-by-minute stock price and tweet data for the 30 stocks in the Dow Jones Industrial Average over a random 13-day interval from June 2 to June 18, 2014 using a BEKK-MVGARCH methodology. Findings indicate that 87% of stock returns are influenced by lagged innovations of the tweets data, but there is little evidence to support that the direction is reciprocal, with only 7% of tweets being influenced by lagged innovations of the stock returns. Results further show that the lagged innovations from 40 percent of stock returns affect the current conditional volatility of the tweets, while 73 percent of tweets affect the current conditional volatility of stock returns. Moreover, there is strong evidence to suggest that the volatility originating from the returns to the tweets persists for 33 percent of stocks; the volatility originating from the tweets to the returns persists for 73 percent of stocks. Last, 53 percent of stocks exhibit both immediate and persistent impacts from returns to tweets, while 90 percent of stocks exhibit both immediate and persistent impacts from tweets to returns. These results may help traders achieve superior returns by buying and selling individual stocks or options. Also, asset and mutual fund managers may benefit by developing a social media strategy.     

PREDICTING STOCK RETURNS AND VOLATILITY WITH INVESTOR SENTIMENT INDICES: A RECONSIDERATION USING A NONPARAMETRIC CAUSALITY‐IN‐QUANTILES TEST

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S^BW and S^PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only S^PLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality‐in‐quantiles model of Balcilar et al., (forthcoming), in fact, both S^BW and S^PLS can predict stock returns and its volatility, with S^PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S^BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.     

Nonlinearities in emerging stock markets: evidence from Europe's two largest emerging markets

Recent developments in time series analysis allow proper modelling of nonlinearities in economic and financial variables. A growing body of research was dedicated to investigation of potential nonlinearities in conditional mean of many economic and financial variables, mainly concentrating in developed economies. However, nonlinearities in financial variables in developing economies have not been fully examined yet. In this article we investigate potential nonlinearity and cyclical behaviour of stock returns in Europe's two largest emerging stock markets, mainly in the Greek and Turkish stock markets. Specifically, we use STAR family models, which allow to model nonlinearities in the conditional mean, for modelling monthly returns on stock exchange indices of the Athens Stock Exchange and Istanbul Stock Exchange. Although we find no nonlinearity in conditional variance, we do find strong evidence in favour of nonlinear adjustment of stock returns. It is found that allowing for nonlinearity in conditional mean results in a superior model and provides good out-of-sample forecasts, which contradicts to efficient market hypothesis.     

Modelling conditional moments and correlation with the continuous hidden-threshold-skew-normal distribution

A key issue in modelling conditional densities of returns of financial assets is the time-variation of conditional volatility. The classic econometric approach models volatility of returns with the generalized autoregressive conditional heteroscedasticity (GARCH) models where the conditional mean and the conditional volatility depend only on historical prices. We propose a new family of distributions in which the conditional distribution depends on a latent continuous factor with a continuum of states. The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. The distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. We show empirically that this distribution outperforms its main competitor, the mixed normal conditional distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.     

Spillover effects of the 2008 financial crisis on NIE stock markets

This article estimates dynamic conditional correlations of stock returns across countries by using DCC–GARCH model and analyse spillover effects of the 2008 financial crisis on the NIE’s stock markets. The results show that there is no regime shift in mean equation of the correlation coefficient during the financial crisis. It may imply there are no mean spillover effects of the US financial crisis on the NIE’s stock markets. However, there are volatility spillover effects of the financial crisis sparked in 2008 from the US to the NIE’s markets.