Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In ...

Many researchers seek factors that predict the cross-section of stock returns. The standard methodology sorts stocks according to their factor scores into quantiles and forms a corresponding long-short portfolio. Such a course of action ignores any information on the covariance matrix of stock returns. Historically, it has been difficult to estimate the covariance matrix for a large universe of...

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model Engle et al. (2019) is able overcome this via nonlinear shrinkage estimation unconditional correlation matrix. In paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead simply daily returns. A key innovati...

This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. We consider 10 assets from the NYSE and compare 125 model based one, five and twenty-day ahead conditional variance forecasts over a period of 10 years using the Model Confidence Set (MCS) and the Superior Pr...

This paper assesses the relative economic value of volatility and correlation timing in the context of asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing a set of multivariate models based on Dynamic Conditional Correlation (DCC) model of Engle (2002). Our analysis takes a Bayesian approach in both estimation and asset ...

Abstract This paper investigates the linkage of returns and volatilities between United States Chinese stock markets from January 2010 to March 2020. We use dynamic conditional correlation (DCC) asymmetric Baba–Engle–Kraft–Kroner (BEKK) GARCH models calculate time-varying correlations these two examine return volatility spillover effects markets. The empirical results show that there are only u...

Forecasting Value-at-Risk (VaR) for financial portfolios is a staggering task in financial risk management. The turmoil in financial markets as observed since September 2008 called for more complex VaR models, as ”standard” VaR approaches failed to anticipate the collective market movements faced during the financial crisis. Hence, recent research on portfolio management mainly focussed on mode...

During the last decades, the financial markets volatility concept attracted the attention of the theorists and the experts in the field of finance, especially for the internationally diversified wallets. In this article, we used an asymmetric dynamic conditional correlation (DCC-GARCH (1.1)) model following the approach of Engle (2002), to test if the volatility of individual market or their re...

Resumo Neste artigo utilizou as abordagens da Correlação Condicional Dinâmica — DCC proposto por Engle (2002), a abordagem do Índice de Spillover volatilidade abordado (Diebold e Yilmaz 2009, 2012, 2014, 2015) o Hedge Maghyereh et al. (2017), para estudar mecanismo transmissão choque, contágio diversificação carteira no setor petrolífero entre variações preços petróleo dos das ações empresas em...

This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and onemonth forward prices for the period 2 June 1992 to 16 January 2004, using recently developed multivariate conditional volatility models, namely the Constant Conditional Correlation Multivariate GARCH (CCCMGARCH) model of Bollerslev [1990], Vector Autoregressive Moving Average – GARCH (VARMAGARCH) m...