Deep learning model can project prices around 100 ticks into the future
This paper examines the CBOE VIX, the VIX options’ implied volatility and the smirks associated with these options.
A multi-step path is forecast using deep learning and parallel computing
In this paper, different machine learning approaches are applied to forecasting future yearly price trends in the natural gas Title Transfer Facility market in the Netherlands.
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers
Forecasting stock market volatility: an asymmetric conditional autoregressive range mixed data sampling (ACARR-MIDAS) model
This paper proposes an extension of the classical CARR model, the ACARR-MIDAS model, to model volatility and capture the volatility asymmetry as well as volatility persistence.
This study proposes an advanced credit evaluation method for nonperforming consumer loans, which may serve as a new investment opportunity in the post-pandemic era.
A fractional Brownian–Hawkes model for the Italian electricity spot market: estimation and forecasting
This paper proposes a new model for the description and forecast of gross prices of electricity in the liberalized Italian energy market via an additive two-factor model.
Zone-wide prediction of generating unit-specific power outputs for electricity grid congestion forecasts
This paper explores various statistical and statistical learning methods, with the goal of adequately predicting the on/off status and power output levels of all power plants within a control zone.
Synthetic data made with machine learning will struggle to capture the caprice of financial markets
This paper provides a method to identify the best predictive variables and the appropriate predictive indexes for an aggregate hydropower storage forecasting model. To this end, we use an entropy-based approach.
In this paper, the authors extend the related literature by examining whether the information on the US–China trade war can be used to forecast the future path of Bitcoin returns, controlling for various explanatory variables.
In this paper, the authors investigate how data aggregation and risk attributes affect the development and performance of stress testing models by studying residential mortgage loan defaults.
Crowd-sourced election scenarios show sharp falls and correlation breaks if Trump challenges results
Energy market expert investigates ways to forecast future power prices and capture rates in order to value renewables PPAs
Duality’s CEO discusses key to machine learning success, and the influence of Renaissance’s Jim Simons
Non-parametric VAR models perform well in calm markets, but miss the mark in volatile periods
As reserves for bad loans balloon, banks grapple with measuring Covid-era credit risk
The analysis in this paper reveals that additional fundamental risk gets transferred along supply chains, and that suppliers are exposed to additional fundamental risk that is not captured by their market beta. Suppliers are therefore exposed to…
Range-based volatility forecasting: a multiplicative component conditional autoregressive range model
This paper proposes a multiplicative component CARR (MCCARR) model to capture the "long-memory" effect in volatility.
Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states
The authors present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory…
Quant fund pioneer plans to build an economic super-simulator on a global scale