Time-series analysis
Synthetic data enters its Cubist phase
Quants are using the theory of rough paths to distil the essence of financial datasets
Dilated convolutional neural networks for time series forecasting
In this paper, the authors present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture.
Using derivatives to forecast oil scenarios
Generating probability-weighted oil price scenarios from traded derivatives prices can help risk managers in the industry
Visibility graph combined with information theory: an estimator of stock market efficiency
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.
On empirical likelihood option pricing
This paper investigates the application of the empirical likelihood method in the study of option pricing.
A new bootstrap test for multiple assets joint risk testing
In this paper, a novel simulation-based methodology is proposed to test the validity of a set of marginal time series models.
Do investors price industry risk? Evidence from the cross-section of the oil industry
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.
Risk reduction in a time series momentum trading strategy
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.
Probabilistic forecasting of medium-term electricity demand: a comparison of time series models
This paper focuses on medium-term probabilistic forecasting for risk management purposes.
Hybrid correlation matrices
Hybrid correlation matrices