The long-memory and nonlinearity coexist in realised volatility. This paper incorporates the linear AR and HAR models with regime-switching models, including the smooth transition and Markov-switching approaches, to assess the forecasting performance of realized volatility. In-sample results...

Accurate prediction of Bitcoin prices is a purported boon for risky investors, more so, if the forecasts are largely unconditional. This paper introduces a class of autoregressive fractionally integrated moving average model with asymmetric exponential generalized autoregressive score errors to...

Using Thailand stock market data, we find that prospect theory has strong predictive power for returns in the Thailand stock market. This predictive power is strengthened during crises and bear and bull markets. The loss aversion component is the main contributor to the increased predictive...

It is well known that volatility is time-varying and clustered. However, few studies have explored the information content of volatility clustering and its implications for investors’ risk aversion. This information is particularly important in turbulent periods, such as financial crisis. We...

In a novel take on the gradual information diffusion hypothesis of Hong et al. (2007), we examine the predictive role of industries over aggregate stock market volatility. Using high frequency data for U.S. industry indexes and various heterogeneous autoregressive (HAR) type and machine learning...

This study estimates the probability of a defaulted company being cured on the basis of data from a large international sample of defaulted companies. In particular, this study examines which of the characteristics of a defaulted company and its loan might help predict whether that company is...

We study the impacts of business cycles on machine learning (ML) predictions. Using the S&P 500 index, we find that ML models perform worse during most recessions, and the inclusion of recession history or the risk-free rate does not necessarily improve their performance. Investigating...

We demonstrate how a mixture of two SEP3 densities (skewed exponential power distribution of Fernández et al., 1995) can model the conditional forecasting of VaR and CVaR to efficiently cover market risk at regulatory levels of 1% and 2.5%, as well as at the additional 5% level. Our data...

This paper examines the ability of the oil market variance risk premium (VRP) to predict both financial and key macroeconomic series. Interest in understanding the movement of such variables increasingly involves considers measures of investor risk, for which the VRP that incorporates both...

With rising inflation top-of-mind for Canadians, a major question is: Can we predict its future? This Commentary shows that growth in the money supply is a useful predictor of inflation, and examines why and when.Since the early 1990s, the Bank of Canada has pursued a successful...