Quants are using the theory of rough paths to distil the essence of financial datasets
Signatures can provide the synthetic data to train deep hedging strategies
This study investigates the systematic error that is made if the exposure pool underlying a default time series is assumed to be homogeneous when in reality it is not.
This paper models natural gas returns explicitly, allowing for market participants to learn over time and to react differently to present changes in economic variables. This learning and adaptation, and the attendant parameter uncertainty, constitutes…
In this paper, the authors present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture.
Generating probability-weighted oil price scenarios from traded derivatives prices can help risk managers in the industry
In this paper, the authors use information theory quantifiers to analyze the graphs generated by the VG method as applied to the return rate time series of stock markets from different countries.
This paper investigates the application of the empirical likelihood method in the study of option pricing.
In this paper, a novel simulation-based methodology is proposed to test the validity of a set of marginal time series models.
This paper analyzes the case of commodity-dependent industries by testing in the case of the oil industry and analyzing whether oil exposure relates to the cross-section of returns.
In this paper, the authors investigate the four most commonly used risk measures – return volatility, beta, value-at-risk and stressed value-at-risk – of a TSM trading strategy.
This paper focuses on medium-term probabilistic forecasting for risk management purposes.
Hybrid correlation matrices