Forecasting
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.
Regularization effect on model calibration
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…
New model simplifies loan-loss forecasts. Some say it’s too simple
Modelling approach devised by Commerzbank quant promises to ease computational burden, but may not suit complex portfolios
Podcast: Man Group’s Zohren on forecasting prices with DeepLOB
Deep learning model can project prices around 100 ticks into the future
Forecasting volatility and market returns using the CBOE Volatility Index and its options
This paper examines the CBOE VIX, the VIX options’ implied volatility and the smirks associated with these options.
Multi-horizon forecasting for limit order books
A multi-step path is forecast using deep learning and parallel computing
Forecasting natural gas price trends using random forest and support vector machine classifiers
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.
Neural networks show fewer false positives on bad loans – study
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers
Using equity, index and commodity options to obtain forward-looking measures of equity and commodity betas and idiosyncratic variance
This paper presents a means to extract forward-looking measures of equity and commodity betas, and idiosyncratic variance.
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.
Forecasting consumer credit recovery failure: classification approaches
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.
‘It’s the economy’: forecasting an op risk climate change spike
History of op risk suggests economic impacts of climate change could exacerbate losses, writes op risk head
How algos are helping inflation-wary investors
Buy-siders look to machine learning for clues on the effect of rising prices on portfolios
Zurich’s Scott: don’t levy climate risk capital charges
Imposing set-asides based on stress tests “does not make any sense”, sustainability chief warns watchdogs
Canada’s top banks cut loan-loss provisions by $1.2bn
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.
Fake data can help backtesters, up to a point
Synthetic data made with machine learning will struggle to capture the caprice of financial markets
Modeling realized volatility with implied volatility for the EUR/GBP exchange rate
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.
The selection of predictive variables in aggregate hydroelectric generation models
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.
Neural network middle-term probabilistic forecasting of daily power consumption
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.
Forecasting Bitcoin returns: is there a role for the US–China trade war?
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.
Buffer stops? Why banks haven’t used Covid capital relief
Amid weak credit demand, banks haven’t availed themselves of capital buffers, but they still might