The generalized asymmetric dynamic covariance model

In this paper we extend the ADC model of Kroner and Ng [1998. Review of Financial Studies 11, 817–844] such that it allows for cross-asymmetries in conditional volatility. That is, the model allows for asymmetries in covariances after shocks of opposite signs. We find evidence for significant cross-asymmetries in the conditional volatility in stock and bond markets.     

Macroeconomic announcement effects on the covariance structure of government bond returns

This paper concerns the effects of macroeconomic announcements on the covariance structure of US government bond returns for six different maturities; the study shows that the conditional variances, covariances, and correlation coefficients are significantly greater on announcement days. On non-announcement days, the correlation coefficients are relatively large and are greater the closer the bonds are with respect to the time to maturity. The maturity dependency is substantially dampened on announcement days and, hence, releases of macroeconomic news induce common movement in the government bond market that strengthen the correlations.     

STOCK MARKET REACTION TO GOOD AND BAD INFLATION NEWS

This article shows that differentiating between good and bad inflation news is important to understanding how inflation affects stock market returns. Summing positive and negative inflation shocks as in previous studies tends to wash out or mute the effects of inflation news on stock returns. More specifically, we find that, depending on the economic state, positive and negative inflation shocks can produce a variety of stock market reactions. We conclude that the effect of inflation on stock returns is conditional on whether investors perceive inflation shocks as good or bad news in different economic states.     

Asymmetric Price and Volatility Adjustments in Emerging Asian Stock Markets

This paper tests the hypothesis that stock returns in emerging stock markets adjust asymmetrically to past information. The evidence suggests that both the conditional mean and the conditional variance respond asymmetrically to past information. In agreement with studies dealing with developed stock markets, the conditional variance is an asymmetrical function of past innovations, rising proportionately more during market declines. More importantly, the conditional mean is also an asymmetrical function of past returns. Specifically, positive past returns are more persistent than negative past returns of an equal magnitude. This behaviour is consistent with an asymmetric partial adjustment price model where news suggesting overpricing (negative returns) are incorporated faster into current prices than news suggesting underpricing (positive returns). Furthermore, the asymmetric adjustment of prices to past information could be partially responsible for the asymmetries in the conditional variance if the degree of adjustment and the level of volatility are positively related.     

How do policy and information shocks impact co-movements of China’s T-bond and stock markets?

We investigate the impacts of policy and information shocks on the correlation of China’s T-bond and stock returns, using originally the asymmetric dynamic conditional correlation (DCC) model that allows for the coexistence of opposite-signed asymmetries. The co-movements of China’s capital markets react to large macroeconomic policy shocks as evidenced by structural breaks in the correlation following the drastic 2004 macroeconomic austerity. We show that the T-bond market and the bond–stock correlations bear more of the brunt of the macroeconomic contractions. We also find that the bond–stock correlations respond more strongly to joint negative than joint positive shocks, implying that investors tend to move both the T-bond and stock prices in the same direction when the two asset classes have been hit concurrently by bad news, but tend to shift funds from one asset class to the other when hit concurrently by good news. However, the stock–stock correlation is found to increase for joint positive shocks, indicating that investors tend to herd more for joint bullish than joint bearish stock markets in Shanghai and Shenzhen.     

Macroeconomic announcements and asymmetric volatility in bond returns

This study analyses the impact of macroeconomic news announcements on the conditional volatility of bond returns. Using daily returns on the 1, 3, 5 and 10 year US Treasury bonds, we find that announcement shocks have a strong impact on the dynamics of bond market volatility. Our results provide empirical evidence that the bond market incorporates the implications of macroeconomic announcement news faster than other information. Moreover, after distinguishing between types of macroeconomic announcements, releases of the employment situation and producer price index are especially influential at the intermediate and long end of the yield curve, while monetary policy seem to affect short-term bond volatility.     

Asymmetry in return and volatility spillover between equity and bond markets in Australia

We document asymmetry in return and volatility spillover between equity and bond markets in Australia for daily returns during the period 1992–2006 using a bivariate GARCH modelling approach. Negative bond market returns spillover into lower stock market returns whereas good news originating in the equity market leads to lower bond returns. Bond market volatility spills over into the equity market but the reverse is not true. Transmission of bond volatility into equity volatility depends in a complex way upon the respective signs of the return shocks in each market.     

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.     

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.     

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.