We study potential drivers for a large cross-section of commodity futures. Unlike previous studies, we examine the effect of monthly drivers on daily returns using mixed-frequency Granger causality tests. We find real economic activity as a main driver on a monthly basis, whereas financial...

The transition from fossil fuels toward cleaner energy necessitates technological innovation, suggesting not only an association between clean energy and technology stocks but also a potential impact of climate risk on this association. In this paper, we examine the impact of climate risk on the...

This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find...

Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more different. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as...

We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important...

This paper contributes to the large debate regarding the impact of oil price changes on U.S. GDP growth. Firstly, it replicates empirical findings of prominent studies and finds that the proposed oil price measures have a dissipating effect with recent data up to 2016Q4. Secondly, it re-examines...

This paper presents a thorough replication of Hamilton (2003) which in turn replicates and extends the findings of four seminal papers regarding the oil price-GDP growth relationship. Firstly, we replicate the empirical results obtained with the oil price measures of Hamilton (1983), Mork...

Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning generated forecasts provide better forecasting quality and that portfolios which are constructed with these forecasts outperform their...

We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets,...