Wessel Marquering

Modeling the conditional covariance between stock and bond returns : A multivariate GARCH approach

To analyze the intertemporal interaction between the stock and bond market returns, we allow the conditional covariance matrix to vary over time according to a multivariate GARCH model similar to Bollerslev, Engle and Wooldridge (1988). We extend the model such that it allows for asymmetric effects on conditional variances and covariances. Using weekly U.S. stock and bond market data, we find strong evidence of conditional heteroskedasticity in the covariance between stock and bond market returns. The results indicate that not only variances, but also covariances respond asymmetrically to return shocks. Regardless of the bond market shocks, bad news in the stock market is typically followed by a higher conditional covariance than good news. We find that volatility timing strategies for dynamic asset allocation significantly outperform passive strategies. Even when short-sale restrictions are present and transaction costs are high, the economic value of dynamic trading strategies is larger than that of a passive strategy. Moreover, the symmetric volatility timing strategy is outperformed by its asymmetric counterpart.

Disappearing anomalies: a dynamic analysis of the persistence of anomalies

This study examines several well-known stock market anomalies before and after they were published. If the anomalies are a result of data snooping or data crunching, these are expected to disappear in the data soon after they have been reported. Moreover, increased awareness of anomalies among investors will diminish possible profits as more investors will trade based on these anomalies. Employing dynamic analyses, strong evidence is found that the weekend effect, the holiday effect, the time-of-the-month effect and the January effect have disappeared after these anomalies have been published. The turn-of-the-month effect seems still present, whereas the small firm effect has recently resurrected. The timing of disappearing or reappearing anomalies typically often coincides with the timing of academic publications.

Modeling the Conditional Covariance Between Stock and Bond Returns: A Multivariate Garch Approach

To analyze the intertemporal interaction between the stock and bond market returns, we assume that the conditional covariance matrix follows a multivariate GARCH process. We allow for asymmetric effects in conditional variances and covariances. Using daily data, we find strong evidence of conditional heteroskedasticity in the covariance between stock and bond market returns. The results indicate that not only variances, but also covariances respond asymmetrically to return shocks. Bad news in the stock and bond market is typically followed by a higher conditional covariance than good news. Cross asymmetries, that is, asymmetries followed from shocks of opposite signs, appear to be important as well. Covariances between stock and bond returns tend to be relatively low after bad news in the stock market and good news in the bond market. A financial application of our model shows that optimal portfolio shares can be substantially affected by asymmetries in covariances. Moreover, our results show sizable gains due to asymmetric volatility timing. Copyright 2004, Oxford University Press.

Do Macroeconomic Announcements Cause Asymmetric Volatility

In this paper we study the impact of macroeconomic news announcements on the conditional volatility of stock and bond returns. Using daily returns on the S&P 500 index, the NASDAQ index, and the 1 and 10 year U.S. Treasury bonds, for January 1982 - August 2001, some interesting results emerge. Announcement shocks appear to have a strong impact on the (dynamics of) bond and stock market volatility. Our results provide empirical evidence thatasymmetric volatility in the Treasury bond market can be largely explained by these macroeconomic announcement shocks. This suggests that the asymmetric volatility found in government bond markets are likely due to misspecification of the volatility model. After including macroeconomic announcements into the model, the asymmetry disappears. Becausefirm-specific news is the most important source of information in the stock market, the asymmetries in stock volatility do not disappear after incorporating macroeconomic announcements into the volatility model.

A Multivariate Nonparametric Test for Return and Volatility Timing

This paper develops a novel approach to simultaneously test for market timing in stock index returns and volatility. The tests are based on the estimation of a system of regression equations with indicator variables and provide detailed information about the statistical significance of alternative market timing components. JEL classification: C14; C22; C52; C53; G12; G14.

Do consumption-based asset pricing models explain return predictability?

This paper studies whether time series predictability is consistent with risk-based asset pricing models. Whereas earlier papers – e.g. Kirby (1998), Cecchetti, et al. (2000) and Avramov (2004) – show that returns are too predictable to be explained by rational asset pricing, we find that the predictability typically found in linear forecasting models is not necessarily larger than can be expected from rational asset pricing. More specifically, when preferences are summarized by habit persistence with habit and risk aversion parameters equal to 0.97 and 4.8 (or greater) respectively, a degree of predictability is obtained that is compatible with the predictability found in the recent literature.