Knowledge that Transforms

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.

We present a new methodology for estimating time-varying conditional skewness. Our model allows for changing means and variances, uses a maximum likelihood framework with instruments, and assumes a non-central t distribution. We apply this method to daily, weekly, and monthly stock returns, and find that conditional skewness is important. In particular, we show that the evidence of asymmetric variance is consistent with conditional skewness. Inclusion of conditional skewness also impacts the persistence in conditional variance.