This paper investigates the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis. Using a broad sample of stock options traded at the American Stock Exchange and the Chicago Board Options Exchange (CBOE)...

I have used four measures that have had considerable success in predicting stock market declines of ten percent or more and average twenty-five percent. Other declines of 5-15% seem to be hard to predict ex ante, while some can be explained ex post. In this paper, I focus on six of the latter...

In this study, we apply a rolling window approach to wavelet-filtered (denoised) S&P500 returns (2000–2020) to obtain time varying Hurst exponents. We analyse the dynamics of the Hurst exponents by applying statistical tests (e.g., for stationarity, Gaussianity and self-similarity), a...

This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape (CARS) model with beta density to predict the direction of stock returns. The CARS model is continuously valued, which makes it different from binary classification...

This paper is concerned with empirical and theoretical basis of the Efficient Market Hypothesis (EMH). The paper begins with an overview of the statistical properties of asset returns at different frequencies (daily, weekly and monthly), and considers the evidence on return predictability, risk...

This paper is concerned with empirical and theoretical basis of the Efficient Market Hypothesis (EMH). The paper begins with an overview of the statistical properties of asset returns at different frequencies (daily, weekly and monthly), and considers the evidence on return predictability, risk...

We propose a new approach to imposing economic constraints on forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two...

We compare several representative sophisticated model averaging and variable selection techniques of forecasting stock returns. When estimated traditionally, our results confirm that the simple combination of individual predictors is superior. However, sophisticated models improve dramatically...