Generalised autoregressive conditional heteroscedasticity (Garch)
Forecasting extreme tail risk in China’s banking sector: an approach based on a component generalized autoregressive conditional heteroscedasticity and mixed data sampling model and extreme value theory
The authors put forward a means to forecast extreme tail risk in the Chinese banking sector - the component GARCH-MIDAS-EVT-X model.
Forecasting the Volatility Index with a realized measure, volatility components and dynamic jumps
The authors put forward the REGARCH-2C-Jump model to forecast VIX, with results suggesting that this model can outperform other models in VIX forecasting.
Does investors’ sentiment influence stock market volatility? Evidence from India during pre- and post-Covid-19 periods
The authors use data from during the Covid-19 pandemic to investigate the impact of investor sentiment on equity market volatility, finding negative news to have a stronger impact that positive news of the same magnitude.
Alternative margin models for mortgage-backed securities
The authors investigate mortgage-backed securities, applying margin frameworks often used on other asset classes to MBSs which could be uses as a supplemental model framework.
Volatility spillover effects and risk assessment of Indian green stocks: a DCC-GARCH analysis
The authors, focussing on India, employ a DCC-GARCH model to better understand price fluctuations and risks linked to other assets in relation to green investment projects.
On the contagion effect between crude oil and agricultural commodity markets: a dynamic conditional correlation and spectral analysis
The authors present an empirical study concerning the volatility comovements between crude oil and agricultural commodities relative to global economic shocks such as Covid-19 and the Russo-Ukrainian war.
Conditional and unconditional intraday value-at-risk models: an application to high-frequency tick-by-tick exchange-traded fund data
The authors consider conditional and unconditional intraday value-at-risk models for high-frequency exchange-traded funds, providing results useful to practitioners of high-frequency trading.
Time-varying higher moments, economic policy uncertainty and renminbi exchange rate volatility
The authors investigate how time-varying higher moments and economic policy uncertainty may be used for predicting the renminbi exchange rate volatility.
The haves and the ‘have bots’: can AI give vol forecasters an edge?
Firms look to machine learning and natural language processing to gain advantage over peers
Semiparametric GARCH models with long memory applied to value-at-risk and expected shortfall
The authors introduce and apply new semiparametric GARCH models with long memory to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets.
Trading strategies and weekly anomalies in the stock market: Mexico, Indonesia, Nigeria and Turkey
This paper explores the day-of-the-week impact and efficiency of the stock markets in Mexico, Indonesia, Nigeria and Turkey by using closing prices of a major index from each stock market.
As geopolitical risk spikes, a major index gets a revamp
Geovol risk gauge built by Nobel laureate Robert Engle to become Global Covol
Oil value-at-risk forecasts: a filtered semiparametric approach
This paper proposes the GARCH model combined with the Cornish–Fisher expansion for the oil VaR forecast.
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.
Covariance estimation for risk-based portfolio optimization: an integrated approach
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.
Reinvestigating international crude oil market risk spillovers
This paper develops a copula-GARCH-MIDAS model to estimate the joint probability distribution of multivariate variables, and then derives CoVaR-type risk measures.
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.
Correlated idiosyncratic volatility shocks
To capture the commonality in idiosyncratic volatility, the authors propose a novel multivariate generalized autoregressive conditional heteroscedasticity (GARCH) model called dynamic factor correlation (DFC).
The price of Bitcoin: GARCH evidence from high-frequency data
This is the first paper that estimates the price determinants of Bitcoin in a generalized autoregressive conditional heteroscedasticity (GARCH) framework using high-frequency data.
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.
Performance of value-at-risk averaging in the Nordic power futures market
The authors investigate the performance of various value-at-risk (VaR) models in the context of the highly volatile Nordic power futures market, examining whether simple averages of models provide better results than the individual models themselves.