http://repub.eur.nl/res/aut/3842/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/20955/ 2009-11-01T00:00:00Z REITs exhibit a strong and prevalent momentum effect that is not captured by conventional factor models. This REIT momentum anomaly hampers proper judgments about the performance of actively managed REIT portfolios. In contrast, a REIT momentum factor adds incremental explanatory power to performance attribution models for REIT portfolios. Using this factor, this study finds that REIT momentum explains a great deal of the abnormal returns that actively managed REIT mutual funds earn in aggregate. Accounting for exposure to REIT momentum also materially influences cross-sectional comparisons of the performances of REIT mutual funds. This study has important implications for performance evaluation, alpha--beta separation, and manager selection and compensation. http://repub.eur.nl/res/pub/14489/ 2009-03-01T00:00:00Z 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. http://repub.eur.nl/res/pub/13586/ 2008-04-01T00:00:00Z We show that results in the recent strand of the literature, which tries to explain stock returns by weather induced mood shifts of investors, might be data-driven inference. More specifically, we consider two recent studies [Kamstra, Mark J., Kramer, Lisa A., Levi, Maurice D., 2003a. Winter blues: A SAD stock market cycle. American Economic Review 93(1), 324–343; Cao, Melanie, Wei, Jason, 2005. Stock market returns: A note on temperature anomaly. Journal of Banking and Finance 29(6), 1559–1573] that claim that a seasonal anomaly in stock returns is caused by mood changes of investors due to lack of daylight and temperature variations, respectively. While we confirm earlier results in the literature that there is indeed a strong seasonal effect in stock returns in many countries: stock market returns tend to be significantly lower during summer and fall months than during winter and spring months as documented by Bouman and Jacobsen [Bouman, Sven, Jacobsen, Ben, 2002. The Halloween indicator, Sell in May and go away: Another puzzle. American Economic Review, 92(5), 1618–1635], there is little evidence in favor of a SAD or temperature explanation. In fact, we find that a simple winter/summer dummy best describes this seasonality. Our results suggest that without any further evidence the correlation between weather-related variables and stock returns might be spurious and the conclusion that weather affects stock returns through mood changes of investors is premature. http://repub.eur.nl/res/pub/1816/ 2004-12-03T00:00:00Z We show that results in the recent strand of the literature that tries to explain stock returns by weather induced mood shifts of investors might be data-driven inference. More specifically, we consider two recent studies (Kamstra, Kramer and Levi, 2003a and Cao and Wei, 2004) that claim that a seasonal anomaly in stock returns is caused by mood changes of investors due to lack of daylight and temperature variations, respectively. We confirm earlier results in the literature that there is indeed a strong seasonal effect in stock returns in many countries: stock market returns tend to be significantly lower during summer and fall months than during winter and spring months. However, we also show that at best, these two studies offer two of many possible explanations for the observed seasonal effect. As an illustration we link ice cream production and airline travel to the stock market seasonality using similar reasoning. Our results suggest that without any further evidence the correlation between weather variables and stock returns might be spurious and the conclusion that weather affects stock returns through mood changes of investors is premature. http://repub.eur.nl/res/pub/12628/ 2004-12-01T00:00:00Z 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. http://repub.eur.nl/res/pub/12675/ 2004-07-05T00:00:00Z 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. http://repub.eur.nl/res/pub/12629/ 2004-01-01T00:00:00Z In this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the economic value of a number of alternative trading strategies over the period 1970-2001. It appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. Most of the profitability of the dynamic strategies, however, is located in the first half of our sample period http://repub.eur.nl/res/pub/254/ 2002-11-19T00:00:00Z 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. http://repub.eur.nl/res/pub/159/ 2002-01-24T00:00:00Z 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. http://repub.eur.nl/res/pub/133/ 2001-12-10T00:00:00Z In this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954 to 2001, we test the statistical significance of return and volatility predictability and examine the economic value of a number of alternative trading strategies. While we find strong evidence for market timing in both returns and volatility, the success of market timing and volatility timing varies considerably over the sample period. Further, it appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. http://repub.eur.nl/res/pub/12641/ 1999-01-01T00:00:00Z In intertemporal asset pricing models, transaction costs are usually neglected. In this paper we explicitly incorporate transaction costs in these models and analyze to what extent this extension is helpful in explaining the cross-section of expected returns. An empirical analysis using CRSP data on size-based portfolios examines the role of the transaction costs and shows that incorporating such costs in the consumption-based model with power utility does not yield very satisfactory results. However, the introduction of habit persistence substantially improves the model. We find rather strong evidence of habit persistence in monthly consumption data. The plots of the models' pricing errors indicate that an intertemporal asset pricing model with transaction costs and habit persistence explains the cross-sectional variation in the portfolio returns quite accurately.