Recent research shows that emerging markets are distinguished by high returns and low covariances with global market factors. To check whether these results can be attributed to their recent emergence, we simulate a simple, general model of global markets with a realistic survival process. The simulations reveal a number of new effects. We find that pre-emergence returns are systematically lower than post-emergence returns, and that the brevity of a market history is related to the bias in returns as well as to the world beta. These patterns are confirmed by an empirical analysis of emerging and submerged markets.

This study examines the kinds of events that cause large shifts in the volatility of emerging stock markets. We first determine when large changes in the volatility of emerging stock market returns occur and then examine global and local events (social, political, and economic) during the periods of increased volatility. An iterated cumulative sums of squares (ICSS) algorithm is used to identify the points of shocks/sudden changes in the variance of returns in each market and how long the shift lasts. Both increases and decreases in the variance are identified. We then identify events around the time period when shifts in volatility occur. Most events tend to be local and include the Mexican peso crisis, periods of hyperinflation in Latin America, the Marcos-Aquino conflict in the Philippines, and the stock market scandal in India. The October 1987 crash is the only global event during the period 1985–1995 that caused a significant jump in the volatility of several emerging stock markets.

In the context of dual trading restrictions, we examine whether aggregate liquidity measures are appropriate indicators of trader welfare in multiple dealer markets. Consistent with our theoretical results, we show empirically that dual trading restrictions did not affect market liquidity significantly, but dual traders of above average skills may have quit brokerage and switched to trading exclusively for their own accounts following restrictions. Further, customers of these dual traders had lower trading costs in the period before restrictions relative to the trading costs of all customers after restrictions.

This paper provides a theoretical rebalancing benchmark for trading volume that delivers a connection between trading activity in individual stocks and market-wide volume. This model supports the empirical use of an adjustment for market-wide trading activity when filtering out normal trading volume. Data on a sample of large NYSE/AMEX firms support the usefulness of the benchmark. While 20% of the sample firms exhibit trading behavior that is consistent with the cross-sectional prediction of the rebalancing benchmark, systematic deviations exist. An analysis of deviations from the benchmark allows a characterization of anomalous trading activity. I find that average excess turnover vs. the benchmark is positively related to option availability and institutional ownership and negatively related to firm size. The data do not yield a uniform conclusion on the effect of S&P 500 inclusion. S&P 500 inclusion does not significantly increase the trading of firms that are already trading above benchmark levels, but does result in additional trading for firms that undertrade the benchmark prior to inclusion. An investigation of individual firm market model regressions indicates that this is a useful methodology for filtering out the anomalous trading documented here.

We present a subclass of Langetieg's (1980).linear Gaussian models of the term structure. The bond price is derived in terms of a finite set of state variables with correlated innovations. The subclass contains a reformulation of the double-decay model of Beaglehole and Tenney (1991), enabling us to clarify interpretation of their parameters. We apply Kalman filtering to a state space formulation of the model, allowing measurement errors in the data. One-, two-, and three-factor models are estimated on U.S. data from 1987–1996 and the results indicate the subclass of models can fit the U.S. term structure.

The objective of this paper is twofold. First, the paper develops a class of models of the term structure of interest rates, in the Heath, Jarrow, and Morton (1992) framework, with dynamics characterized by the evolution of a small set of state variables. Second, the paper exploits this characterization of the dynamics of the term structure in an estimation exercise that makes use of both the time series and cross-section of bond prices. In this way, our class of models bridges the gap between traditional models, such as Cox, Ingersoll, and Ross (1985) and Vasicek (1977), that emphasize the dynamics of interest rates and the models of Heath, Jarrow, and Morton (1992) that stress fitting the cross-section of bond prices.