Generalised autoregressive conditional heteroscedasticity (Garch)
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
On the contagion effect between crude oil and agricultural commodity markets: a dynamic conditional correlation and spectral analysis
Conditional and unconditional intraday value-at-risk models: an application to high-frequency tick-by-tick exchange-traded fund data
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.
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
Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states
The authors present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory…
CCPs failing to set effective margin limits – study
Clearers must strike balance between countercyclicality and sensitivity to risk
Procyclicality mitigation for initial margin models with asymmetric volatility
In this paper, we explore the procyclicality of initial margin requirements based on VaR volatility models.We suggest procyclicality can be reduced using a three-regime model rather than using ad hoc tools.
Empirical analysis of oil risk-minimizing portfolios: the DCC–GARCH–MODWT approach
This paper strives to analyze hedging strategies between Brent oil and six other het- erogeneous assets – American ten-year bonds, US dollars, gold, natural gas futures, corn futures, and Europe, Australasia and Far East exchange-traded funds (EAFE- ETFs…
Engle: rise in geopolitical risks may be overestimated
Risk USA: Nobel laureate’s new ‘Geovol’ model suggests geopolitical risk no higher than during past 20 years