Forecasting
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
The impact of data aggregation and risk attributes on stress testing models of mortgage default
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.
US election scenarios: meltdown fears if poll contested
Crowdsourced election scenarios show sharp falls and correlation breaks if Trump challenges results
Back to school: BlackRock uses quant quake lessons on Covid
Pandemic prompts a switch in approach from strategic to tactical
Dutch banks seek quantum edge for stress tests
ABN, ING and Rabobank working together; US quantum developer seeks patent for CCAR
Power surge: the value of investing in renewables
Energy market expert investigates ways to forecast future power prices and capture rates in order to value renewables PPAs
Podcast: Dario Villani on managing money with ML
Duality’s CEO discusses key to machine learning success, and the influence of Renaissance’s Jim Simons
Study suggests banks may be better off with simpler VAR models
Non-parametric VAR models perform well in calm markets, but miss the mark in volatile periods