Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange
This paper investigates how input window length and forecast horizon affect the predictive performance of a trading signal prediction system.
Extreme-but-plausible scenario planning is being applied to geopolitical events such as Ukraine conflict
The importance of window size: a study on the required window size for optimal-quality market risk models
In this paper the authors study different moving-window lengths for value-at-risk evaluation, and also address subjectivity in choosing the window size by testing change point detection algorithms.
This paper compares two methods to calibrate two popular models that are widely used for stochastic volatility modeling (ie, the SABR and Heston models) with the time series of options written on the Nasdaq 100 index to examine the regularization effect…
Modelling approach devised by Commerzbank quant promises to ease computational burden, but may not suit complex portfolios
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.
History of op risk suggests economic impacts of climate change could exacerbate losses, writes op risk head
Buy-siders look to machine learning for clues on the effect of rising prices on portfolios
Imposing set-asides based on stress tests “does not make any sense”, sustainability chief warns watchdogs
The decrease in set-asides represents a 92% fall quarter on quarter
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 concerns the application of implied volatility in modeling realized volatility in the daily, weekly and monthly horizon using high-frequency data for the EUR/GBP exchange rate.
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.
The authors propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature.