A Coronavirus Asset Pricing Model: The Role of Skewness

We study an equilibrium risk and return model to explore the effects of the coronavirus crisis and associated skewness. We derive the moment and equilibrium equations, specifying skew-ness price of risk as an additive component of the effect of variance on mean expected return. We estimate our model using the flexible skewed generalized error distribution, for which we derive the distribution of returns and the likelihood function. Using S&P 500 Index returns from January 1990 to mid-May 2020, our results show that the coronavirus crisis generated the most negative reaction in the skewness price of risk, more negative even than the subprime crisis

Consequences of Outlier Returns for Event Studies: A Methodological Investigation and Treatment

Stock returns are decomposed into their regular and outlier components using a maximum likelihood outlier-resistant estimation method. Analytical results depicting the impact of outliers on the ordinary least square (OLS) estimated models and cumulative abnormal return (CAR) statistics are derived and validated using Monte Carlo simulations. The implications of outliers for past event studies are investigated using samples drawn randomly from the universe of stocks in the CRSP database. The OLS-CAR statistics fail to forecast about 37% of the negative-impact and 43% of the positive-impact events. These results raise serious concerns about the validity of conclusions of past event studies, especially those that rejected the hypothesis of significant-impact events

Financial data and the skewed generalized t distribution

This paper develops a skewed extension of the generalized t (GT) distribution, introduced by McDonald and Newey (1988). In particular, the paper derives the mathematical moments and other properties of the distribution and assesses its ability to fit the empirical distribution of several financial series characterized by skewness and excess kurtosis. In all cases the skewed GT provides an excellent fit to the empirical distribution of dat

Predicting shifts in the mean of a multivariate time series process: An application in predicting business failures

A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm’s slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model

Truncated skewed type III generalized logistic distribution: risk measurement applications

This article derives the moment functions of the truncated skewed type III generalized logistic (SGL). These are then applied in finance for the development of value at risk (VaR), expected shortfall (ES), and downside risk measures for investment returns and values. The SGL distribution provides and good fit to the empirical distribution of a representative set of long series of financial data. Moreover, the SGL generates accurate VaR measures

Skewed type III generalized logistic distribution

This paper develops a skewed extension of the type III generalized logistic distribution and presents the analytical equations for the computation of its moments, cumulative probabilities and quantile values. It is demonstrated through an example that the distribution provides an excellent fit to data characterized by skewness and excess kurtosis