Forecasting
Allocating and forecasting changes in risk
This paper considers time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components.
Wanted: radical ideas for inflation modelling
Hedge funds echo Mervyn King’s calls for a new approach to inflation modelling post-2022 crisis
Degree of influence 2022: in the grip of volatility
Rough volatility, liquidity and trade execution were quants’ top priorities this year
Forecasting the realized volatility of stock markets with financial stress
This paper investigates the impact of financial stress on the predictability of the realized volatility of five stock markets
ECL model forecasts are off-target, researchers find
Anticipated slowdown will be first major test for new generation of expected credit loss models
Forecasting the European Monetary Union equity risk premium with regression trees
The authors use EMU data from the period between 2000 to 2020 to forecast equity risk premium and investigate Classification and Regression Trees.
Application of the moving Lyapunov exponent to the S&P 500 index to predict major declines
The authors suggest an innovative method based in econophysics that provides early warning signs for major declines in the S&P 500 Index
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.
Banks tout CCAR-style stress tests for emergent risks
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.
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
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