A long return history is useftil in estimating the current equity premium even if the historical distribution has experienced structural breaks. The long series helps not only if the timing of breaks is uncertain but also if one believes that large shifts in the premium are unlikely or that the premium is associated, in part, with volatility. Our framework incorporates these features along with a belief that prices are likely to move opposite to contemporaneous shifts in the premium. The estimated premium since 1834 fluctuates between four and six percent and exhibits its sharpest drop in the last decade.

This paper takes a new look at the tradeoff between risk and return in the stock market. We find a significant positive relation between average stock variance and the return on the market. There is, therefore, a tradeoff between risk and return in the stock market, except that risk is measured as total risk, including idiosyncratic risk, rather than only systematic risk. Further, we find that the variance of the market by itself has no forecasting power for the market return. These relations persist after we control for macroeconomic variables known to forecast the stock market. We show that idiosyncratic risk explains most of the variation of average stock risk through time and it is idiosyncratic risk that drives the forecastability of the stock market.

informs doi 10.1287/mnsc.1060.0520

We reexamine the time-series relation between the conditional mean and variance of stock market returns. To proxy for the conditional mean return, we use the implied cost of capital, computed using analyst forecasts. The usefulness of this proxy is shown in simulations. In empirical analysis, we construct the time series of the implied cost of capital for the G-7 countries. We find strong support for a positive intertemporal mean-variance relation at both the country level and the world market level. Some of our evidence is consistent with international integration of the G-7 financial markets.

Please do not quote without permission * Chidambaran is visiting at NYU, on leave from Tulane. Lee holds joint appointments at Tulane and HKUST. Trigueros is at Tulane. We are grateful for the comments from participants at seminars at Tulane

We investigate the impact of scheduled government announcements relating to six different macroeconomic variables on the risk and return of three major US financial markets. Our results suggest that these markets do not respond in any meaningful way, to the act of releasing information by the government. Rather, it is the ‘news ’ content of these announcements which cause the market to react. For the three markets tested, unexpected balance of trade news was found to have the greatest impact on the mean return in the foreign exchange market. In the bond market, news related to the internal economy was found to be important. For the US stock market, consumer and producer price information was found to be important. Finally, financial market volatility was found to have increased in response to some classes of announcement and fallen for others. In part, this result can be

Financial risk management typically deals with low probability events in the tails of asset price distributions. In order to capture the behavior of these tails, one should therefore rely on models that explicitly focus on the tails. Extreme value theory (EVT) based models do exactly that, and in this paper we apply both unconditional and conditional EVT models to the management of extreme market risks in stock markets. We find conditional EVT models to give particularly accurate Value-at-Risk measures, and a comparison with traditional (GARCH) approaches to calculate Value-at-Risk demonstrates EVT as being the superior approach both for standard and more extreme Value-at-Risk quantiles. JEL Classification Codes: C22; C53; G19. Keywords: Value-at-Risk; conditional extreme value theory; GARCH; backtesting.

A common approach to estimating the conditional volatility of short horizon asset returns is to use an exponentially weighted moving average (EWMA) of squared past returns. The EWMA estimator is based on the maximum likelihood estimator of the variance of the normal distribution, and is thus optimal when returns are conditionally normal. However, there is ample evidence that the conditional distribution of short horizon financial asset returns is leptokurtic, and so the EWMA estimator will generally be inefficient in the sense that it will attach too much weight to extreme returns. In this paper, we propose an alternative EWMA estimator that is robust to leptokurtosis in the conditional distribution of portfolio returns. The estimator is based on the maximum likelihood estimator of the standard deviation of the Laplace distribution, and is a function of an exponentially weighted moving average of the absolute value of past returns, rather than their squares. We employ the robust EWMA e...

This paper measures the extent to which persistence in expected returns moves asset prices. Over the period 1871 to 1997 persistence is found to vary greatly. For example, 1 unit of news about expected returns can have an impact of between 2 and 30% on asset prices depending on the time period. Variations in persistence appear to be correlated with variations in the riskiness of the economy. That is, at times of high risk, news effects asset prices more than at times of low risk. These results have implications for the estimation of asset pricing models, tests of market efficiency and tests of present value models in general. Interestingly, we find that with the exception of the period surrounding the great depression and the second world war, volatility in stock prices is fairly constant and the time variation in expected returns is driven by variations in the price of risk.

This article proposes a flexible but parsimonious specification of the joint dynamics of market risk and return to produce forecasts of a time-varying market equity premium. Our parsimonious volatility model allows components to decay at different rates, generates mean-reverting forecasts, and allows variance targeting. These features contribute to realistic equity premium forecasts for the U.S. market over the 1840–2006 period. For example, the premium forecast was low in the mid-1990s but has recently increased. Although the market’s total conditional variance has a positive effect on returns, the smooth long-run component of volatility is more important for capturing the dynamics of the premium. This result is robust to univariate specifications that condition on either levels or logs of past realized volatility (RV), as well as to a new bivariate model of returns and RV.