The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX...

Efficient estimation of the equity cost of operating public corporations is essential for a rational investment policy. Traditional OLS beta estimates of a single stock are known to suffer from violations of normality due to outliers – extreme returns caused by large, unpredictable...

The distributions of stock returns and capital asset pricing model (CAPM) regression residuals are typically characterized by skewness and kurtosis. We apply four flexible probability density functions (pdfs) to model possible skewness and kurtosis in estimating the parameters of the CAPM and...

Higher initial margin requirements are associated with lower subsequent stock market volatility during normal and bull periods, but show no relationship during bear periods. Higher margins are also negatively related to the conditional mean of stock returns, apparently because they reduce...

Outlines previous research on business failure prediction models and investigates the impact of serial correlation and non‐stationarity in financial variables on models based on linear discriminant analysis, logit and cumulative sums using 1974‐1991 data from a sample of failed and...

Several authors have raised the issue of non‐stationarity of security returns in empirical tests of the Arbitrage Pricing Theory (APT). This paper tests for one form of non‐stationarity, namely, conditional heteroskedasticity, in the empirical APT with observed factors. Using monthly stock...

This study presents various GARCH models for predicting movements (returns) and volatility patterns in major national stock market indices. These models depict that future returns in the national stock markets of Australia, Belgium, Canada, France, Italy and Switzerland are predictable from past...