This paper investigates how geopolitical risks influence the prediction performance on the
US stock market volatility with machine learning models. Further, it compares the predictive …

We demonstrate that the forecasting combination puzzle is a consequence of the
methodology commonly used to produce forecast combinations. By the combination puzzle …

This paper introduces a simple yet effective modification to bootstrap aggregation (bagging)
and boosting techniques, aimed at addressing substantial errors arising from parameter …

The economic value of risk knowledge is formalised and measured within the context of a
simple trading strategy. In contrast to the risk preferences and the return prediction ability of …

This paper conducts a comprehensive investigation of the" forecast combination puzzle" in
the context of the heterogeneous autoregressive (HAR) model for volatility forecasting. The …

This paper introduces a new two-stage approach to daily volatility modelling where we first
model the implied volatility surface (IVS) of the weekly options of the S&P 500 (SPWX) and …

This paper investigates the predictability of shape features of option implied volatility
surfaces (IVS) through a comparative analysis of traditional econometric and machine …

Forecasts of the covariance matrix of returns is a crucial input into portfolio construction. In
recent years multivariate version of the Heterogenous AutoRegressive (HAR) models have …

A forecast combination is produced by taking a weighted average of forecasts from different
sources, such as different statistical models. This thesis proposes a novel method for the …