Granger-causality in quantiles between financial markets: Using copula approach

This paper considers the Granger-causality in conditional quantile and examines the potential of improving conditional quantile forecasting by accounting for such a causal relationship between financial markets. We consider Granger-causality in distributions by testing whether the copula function of a pair of two financial markets is the independent copula. Among returns on stock markets in the US, Japan and U.K., we find significant Granger-causality in distribution. For a pair of the financial markets where the dependent (conditional) copula is found, we invert the conditional copula to obtain the conditional quantiles. Dependence between returns of two financial markets is modeled using a parametric copula. Different copula functions are compared to test for Granger-causality in distribution and in quantiles. We find significant Granger-causality in the different quantiles of the conditional distributions between foreign stock markets and the US stock market. Granger-causality from foreign stock markets to the US stock market is more significant from UK than from Japan, while causality from the US stock market to UK and Japan stock markets is almost equally significant.     

A Conditional Assessment of the Relationships between the Major World Bond Markets

This paper uses a bivariate GARCH framework to examine the lead‐lag relations and the conditional correlations between 10‐year US government bond returns and their counterparts from the UK, Germany, and Japan. We find that while mean and volatility spillovers exist between the major international bond markets, they are much weaker than those between equity markets. The results also indicate that the correlations between the US and other major bond market returns are time varying and are driven by changing macroeconomic and market conditions. However, in contrast to the finding that the benefits of international diversification in equity markets evaporate during high‐stress periods, we find that the benefits of diversification across major government bond markets do not decrease during periods of extremely high bond market volatility or following extremely negative US and foreign bond returns.     

Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach

We propose a method for estimating Value at Risk (VaR) and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudo-maximum-likelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT) for estimating the tail of the innovation distribution of the GARCH model. We use our method to estimate conditional quantiles (VaR) and conditional expected shortfalls (the expected size of a return exceeding VaR), this being an alternative measure of tail risk with better theoretical properties than the quantile. Using backtesting of historical daily return series we show that our procedure gives better 1-day estimates than methods which ignore the heavy tails of the innovations or the stochastic nature of the volatility. With the help of our fitted models we adopt a Monte Carlo approach to estimating the conditional quantiles of returns over multiple-day horizons and find that this outperforms the simple square-root-of-time scaling method.     

Quantile regression analysis of hedge fund strategies

Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors changes across the distribution of conditional returns. We propose a Bayesian approach to model comparison which provides posterior probabilities for different risk factor models that can be used for model averaging. The most relevant risk factors are identified for different quantiles and compared with those obtained for the conditional expectation model. We find differences in factor effects across quantiles of returns, which suggest that the standard conditional mean regression method may not be adequate for uncovering the risk-return characteristics of hedge funds. We explore potential economic impacts of our approach by analysing hedge fund single strategy return series and by constructing style portfolios.     

Determinants of returns and volatility of Chinese ADRs at NYSE

This paper examines the determinants of returns and of volatility of the Chinese ADRs as listed at NYSE. Using an autoregressive conditional heteroskedasticity (ARCH) model and data from 16 April 1998 through 30 September 2004, we find that Hong Kong stock market (underlying market), US stock market (host market), and local (Shanghai A and B) markets all are important determinants of returns of the Chinese ADRs. However, the underlying Hong Kong market has the most significant impact on mean returns of the ADRs. In terms of the determinants of the conditional volatility of the ADRs returns, only shocks to the underlying markets are significant. These results are consistent with [Kim, M., Szakmary, A.C., Mathur, I., 2000. Price transmission dynamics between ADRs and their underlying foreign securities. Journal of Banking and Finance 24, 1359–1382] who find that the most influential factor in pricing the ADRs in Japan, UK, Sweden, The Netherlands and Australia is their underlying shares. Implications of the results for investors are discussed.     

The role of time‐varying rare disaster risks in predicting bond returns and volatility

This paper aims to provide empirical evidence to the theoretical claim that rare disaster risks affect government bond market movements. Using a nonparametric quantiles‐based methodology, we show that rare disaster‐risks affect only volatility, but not returns, of 10‐year government bond of the United States over the monthly period of 1918:01 to 2013:12. In addition, the predictability of volatility holds for the majority of the conditional distribution of the volatility, with the exception of the extreme ends. Moreover, in general, similar results are also obtained for long‐term government bonds of an alternative developed country (UK) and an emerging market (South Africa).     

Trading volume,time-varying conditional volatility,and asymmetric volatility spillover in the Saudi stock market

Despite the well known importance of volatility–volume relationship, there is a paucity of research on this topic in emerging markets. We attempt to partially fill this gap by investigating volatility–volume relationship in the most important exchange market in the Middle East. We test the effect of trading volume on the persistence of the time-varying conditional volatility of returns in the Saudi stock market. Overall our results support the mixture of distribution hypothesis at the firm level. We also use two different proxies for information arrival, intra-day volatility, and overnight indicators. We find that these are good proxies for information and are important as contemporaneous volume in explaining conditional volatility. We also test for the volatility spillover direction between large- and small-cap portfolios. Our results show that the spillover effect is larger and statistically significant from large to small companies.     

Stock and bond market interactions with level and asymmetry dynamics: An out-of-sample application

We model the dynamic interaction between stock and bond returns using a multivariate model with level effects and asymmetries in conditional volatility. We examine the out-of-sample performance using daily returns on the S&P 500 index and 10 year Treasury bond. We find evidence for significant (cross-) asymmetries in the conditional volatility and level effects in bond returns. The out-of-sample covariance matrix forecasts of the model imply that an investor is willing to pay between 129 and 820 basis points per year for using a dynamic trading strategy instead of a passive strategy.     

Do external political pressures affect the Renminbi exchange rate?

This paper investigates whether external political pressure for faster Renminbi appreciation affects both the daily returns and the conditional volatility of the Renminbi central parity rate. We construct several political pressure indicators pertaining to the Renminbi exchange rate, with a special emphasis on the US pressure, to test the hypothesis. After controlling for Chinese macroeconomic surprise news, we find that US and non-US political pressure does not have a significant influence on Renminbi's daily returns. However, evidence suggests that political pressures, and especially those from the US, have statistically significant impacts on the conditional volatility of the Renminbi. Furthermore, we conduct the same exercise on the 12-month Renminbi non-deliverable forward rate. We find that the non-deliverable forward market is highly responsive to macroeconomic surprise news and there is some evidence that Sino-US bilateral meetings affect the conditional volatility of the Renminbi non-deliverable forward rate.     

Stock returns and volatility following the September 11 attacks: Evidence from 53 equity markets

September 11 attacks matter, and why not? Given that globalization has integrated financial markets, the magnitudes of the effect of the September 11 attacks on global markets are expected to be pervasive. We used data from 53 equity markets to investigate the short term impact of the September 11 attacks on markets' returns and volatility. Our empirical findings indicate that the impact of the attacks resulted in significant increases in volatility across regions and over the study period. However, stock returns experienced significant negative returns in the short-run but recovered quickly afterwards. Nevertheless, we find that the impact of the attacks on financial markets varied across regions. The implication here is that the less integrated regions (e.g., Middle East and North Africa) are with the international economy, the less exposed they are to shocks.     

Causality in quantiles and dynamic stock return–volume relations

This paper investigates the causal relations between stock return and volume based on quantile regressions. We first define Granger non-causality in all quantiles and propose testing non-causality by a sup-Wald test. Such a test is consistent against any deviation from non-causality in distribution, as opposed to the existing tests that check only non-causality in certain moment. This test is readily extended to test non-causality in different quantile ranges. In the empirical studies of three major stock market indices, we find that the causal effects of volume on return are usually heterogeneous across quantiles and those of return on volume are more stable. In particular, the quantile causal effects of volume on return exhibit a spectrum of (symmetric) V-shape relations so that the dispersion of return distribution increases with lagged volume. This is an alternative evidence that volume has a positive effect on return volatility. Moreover, the inclusion of the squares of lagged returns in the model may weaken the quantile causal effects of volume on return but does not affect the causality per se.     

Noise trading and stock market volatility

We investigate the relative effects of fundamental and noise trading on the formation of conditional volatility. We find significant positive (negative) effects of investor sentiments on stock returns (volatilities) for both individual and institutional investors. There are greater positive effects of rational sentiments on stock returns than irrational sentiments. Conversely, there are significant (insignificant) negative effects of irrational (rational) sentiments on volatility. Also, we find asymmetric (symmetric) spillover effects of irrational (rational) bullish and bearish sentiments on the stock market. Evidence in favor of irrational sentiments is consistent with the view that investor error is a significant determinant of stock volatilities.     

Stock returns,quantile autocorrelation,and volatility forecasting

We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast stock return volatility. Our empirical results show that the strength of the autoregression varies across the quantiles of the returns' distribution in terms of both magnitude and persistence. Specifically, the autoregression order and magnitude of the coefficients is lower in the left tail in comparison with the right tail. Additionally, we show that the quantile autoregressive (QAR) framework proposed in this study improves out-of-sample volatility forecasting performance compared to the generalised autoregressive conditional heteroscedasticity (GARCH)-type models and other quantile-based models. We also observe greater outperformance in QAR estimates during periods of financial turmoil. Moreover, the QAR method also explains the stylized ‘leverage effect’ associated with asset returns in the presence of volatility asymmetry.     

Time-varying risk The case of the American computer industry

A bivariate GARCH-in-mean model for individual stock returns and the market portfolio is designed to model volatility and to test the conditional Capital Asset Pricing Model versus the conditional Residual Risk Model. We find that a univariate model of volatility for individual stock returns is misspecified. A joint modelling of the market return and the individual stock return shows that a major force driving the conditional variances of individual stocks is the history contained in the market return variance. We find that a conditional residual risk model, where the variance of the individual stock return is used to explain expected returns, is preferred to a conditional CAPM. We propose a partial ordering of securities according to their market risk using first and second order dominance criteria.     

The Effect of Bond Rating Changes and New Ratings on UK Stock Returns

This is the first study to use daily data from a major capital market outside of the US to examine the role of corporate bond and commercial paper rating changes on common stock returns. Using data published by Standard and Poors' credit rating agency between 1984 and 1992, we examine the impact of new credit ratings, credit rating changes and Credit Watch announcements on UK common stock returns. We find significant negative excess returns around the date of a downgrade and positive returns close to the date of a positive CreditWatch announcement. Hence, the financial markets would appear to place some importance on rating agency pronouncements in the UK. New ratings, whether short or long-term, have no significant impact on returns. We also attempt to quantify the impact of a new credit rating upon firm cost of capital through measures of conditional volatility and systematic risk. However, we find only weak evidence to suggest that a stock's cost of capital is reduced after a long-term credit rating is awarded for the first time.     

A simple two-component model for the distribution of intraday returns

We model the conditional distribution of high-frequency financial returns by means of a two-component quantile regression model. Using three years of 30 minute returns, we show that the conditional distribution depends on past returns and on the time of the day. Two practical applications illustrate the usefulness of the model. First, we provide quantile-based measures of conditional volatility, asymmetry and kurtosis that do not depend on the existence of moments. We find seasonal patterns and time dependencies beyond volatility. Second, we estimate and forecast intraday Value at Risk. The two-component model is able to provide good-risk assessments and to outperform GARCH-based Value at Risk evaluations.     

On the estimation of stock-market reaction to corporate layoff announcements

This study investigates the stock-market reaction to layoff announcements where more than 1000 workers are affected. We employ a dummy variable regression (DVR) version of the market model and compare the results obtained using ordinary least squares (OLS) versus exponential GARCH (EGARCH), and value-weighted (VW) versus equally weighted (EW) market index. We find that the stock market responds negatively to layoffs attributed to low demand. We also find that contrary to prior research, the market reacts positively to restructuring-related layoffs on the announcement date. This pattern of market reaction is observed regardless of the market index used or the parameter estimation methods employed, although the empirical results indicate that using EGARCH/VW market index tends to generate fewer statistically significant test results and smaller (in the absolute size of the cumulative) abnormal returns (ARs). Taken together, our study provides additional support for the claim that studies of stock-market reaction to corporate events must account for the time variation in return volatility. Ignoring these could result in erroneous inferences.     

The effects of quantitative easing on the integration of UK capital markets

We examine the effects of quantitative easing (QE) on the volatility of and correlation between stocks, short-term bonds and long-term bonds in the UK. Using a multivariate dynamic conditional correlation generalised autoregressive conditional heteroscedasticity model, we find that volatility in each of the markets experiences a significant increase during the financial crisis that is reversed during the first phase of QE. We find limited effects of the specific occurrence or intensity of QE activity on either the volatility or correlations for these asset classes, but some evidence that volatility persistence experienced temporary shifts during the sample period. We find short-term variability in the correlations between the markets during the crisis and QE periods, but cannot reject the hypothesis that correlations were constant throughout the sample period.     

Linkages between Indian and US financial markets: impact of global financial crisis and Eurozone debt crisis

This paper examines inter-linkages between Indian and US equity, foreign exchange and money markets using the vector autoregressive-multivariate GARCH-BEKK framework. We investigate the impact of global financial crisis (GFC) and Eurozone debt crisis (EZDC) on the conditional volatility and conditional correlation estimates derived from the multivariate GARCH model for Indian and US financial markets. Our results indicate that there is significant bidirectional causality-in-mean between the Indian stock market returns and the Rs./USD market returns, and significant unidirectional causality-in-mean from the US stock market returns to the Indian stock market returns. As regards volatility spillovers, we find that volatility in the Indian stock market rises in response to domestic as well as US financial market shocks but Indian financial market shocks do not impact the US markets. Further, impact of the recent crisis episodes on the covariance matrix is found to be significant. We find that volatility in the Indian and US financial markets significantly amplified during GFC. The conditional correlations across asset markets were significantly accentuated in the wake of the two crisis episodes. The impact of GFC on cross-market conditional correlations is higher for majority of the asset market pairs in comparison to the EZDC.