Francisca Beer

Emerging Equity Market Volatility: An Empirical Investigation of Markets in Kenya and Nigeria

Abstract This paper considers two emerging markets that are under-researched, Kenya and Nigeria. It offers a comprehensive view of four time properties that emerged from the empirical time series literature on asset returns: (1) the predictability of returns from past observations; (2) the auto-regressive behavior of conditional volatility; (3) the asymmetric response of conditional volatility to innovations; and (4) the conditional variance risk premium. Results of the exponential GARCH (EGARCH) model indicate that asymmetric volatility found in the U.S. and other developed markets also characterized the Nigerian stock exchange. In Kenya, however, the asymmetric volatility coefficient is significant and positive, suggesting that positive shocks increase volatility more than negative shocks of an equal magnitude. The Nairobi Stock Exchange (KSE) returns series report negative but insignificant risk-premium parameters. In Nigeria (NSE), return series exhibit a significant and positive time-varying risk premium. The results also show that expected returns are predictable, that the auto-regressive return parameters (ø 1 ) are significant in both Kenya and Nigeria. Finally, the GARCH parameter (b) is statistically significant, indicating that volatility persistence is present in the two emerging markets studied.

Estimation of risk on the Brussels Stock Exchange: Methodological issues and empirical results

Abstract The primary focus of this paper is to assess models of beta estimates and to appraise their relative ability to forecast the beta parameter in a thin market, the Belgian market. The evidence, using Belgian data, is contrasted with previously reported results using American, U.K. and French data. The adjustment procedures designed by Vasicek (1973), Scholes and Williams (1977) and Dimson (1979) were tested. The results suggest that these adjustment procedures are misspecified and provide biased results. The results also suggest that the simple OLS technique may be the best method to obtain estimates of the systematic risk when dealing with a thin market such as the Brussels Stock Exchange.

Measuring Investor Sentiment in the Stock Market

Recently, the investor sentiment measure has become one of the most widely studied areas in behavioral finance. Various measures have been developed in the literature without being to determine which measure should be used. The purpose of this study is to test their relative performance in predicting stock returns. Using a panel of investor sentiment measures, we develop a new measure of sentiment which combines direct and indirect sentiment measures. Our results show that our composite sentiment index affects the returns of stocks hard to value and difficult to arbitrage consistent with the predictions of noise trader’s models. Finally, we find that our composite index has a better predictive ability than the alternative sentiment measures largely used in the literature.