Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

In this study, the relationship between oil price movements and Turkish stock market is investigated. Given the fact that Turkey is an emerging and oil dependent country, we analyze how the stock market behaves together with the fluctuations in oil prices. The study focuses on extreme...

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned...

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

The promising predictive power of machine learning (ML) models is encouraging a wide range of applications, but the weak interpretability of their black-box feature is a major obstacle to their application to finance. The large scale of limit order book (LOB) data provides fertile ground for ML...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...