This paper investigates the return-volatility relation by taking into account the model specification problem. The market volatility is modeled to have two components, one due to the diffusion risk and the other due to the jump risk. The model indicates that under the absence of leverage...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

This study investigates the nature of relationship between price and trading volume for 50 Indian stocks. Firstly the contemporaneous and asymmetric relation between price and volume are examined. Then we examine the dynamic relation between returns and volume using VAR, Granger causality,...

This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members. Bayesian model averaging (BMA) and regularized linear regression (RLR) are employed to address uncertainties arising from diferent estimation models and...

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

Existing methods of partitioning the market index into bull and bear regimes do not identify market corrections or bear market rallies. In contrast, our probabilistic model of the return distribution allows for rich and heterogeneous intra-regime dynamics. We focus on the characteristics and...

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 develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls below the variable's past 12-month high....

Using well-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between the qualities of the forecasts of the whole density, whereas the Bayesian approach...

COVID-19 pandemic is an extreme event that created a turmoil in stock markets around the world. This unexpected circumstance poses a critical question whether the prevailing models can help predict the plummets of indices, hence the returns. In this study, we model the stock returns using...