scholarly journals Multivariate Volatility Regulated Kelly Strategy: A Superior Choice in Low Correlated Portfolios

2017 ◽  
Vol 07 (05) ◽  
pp. 1453-1472
Author(s):  
Ruanmin Cao ◽  
Zhenya Liu ◽  
Shixuan Wang ◽  
Weifeng Zhou
Keyword(s):  
Multivariate Volatility

scholarly journals COVID-19 pandemic and the crude oil market risk: hedging options with non-energy financial innovations

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Afees A. Salisu ◽  
Kingsley Obiora
Keyword(s):  
Crude Oil ◽  
Structural Breaks ◽  
Exchange Traded Funds ◽  
Modeling Framework ◽  
Risk Hedging ◽  
Financial Innovations ◽  
Oil Market ◽  
Price Series ◽  
Hedging Effectiveness ◽  
Multivariate Volatility

AbstractThis study examines the hedging effectiveness of financial innovations against crude oil investment risks, both before and during the COVID-19 pandemic. We focus on the non-energy exchange traded funds (ETFs) as proxies for financial innovations given the potential positive correlation between energy variants and crude oil proxies. We employ a multivariate volatility modeling framework that accounts for important statistical features of the non-energy ETFs and oil price series in the computation of optimal weights and optimal hedging ratios. Results show evidence of hedging effectiveness for the financial innovations against oil market risks, with higher hedging performance observed during the pandemic. Overall, we show that sectoral financial innovations provide resilient investment options. Therefore, we propose that including the ETFs in an investment portfolio containing oil could improve risk-adjusted returns, especially in similar financial crisis as witnessed during the pandemic. In essence, our results are useful for investors in the global oil market seeking to maximize risk-adjusted returns when making investment decisions. Moreover, by exploring the role of structural breaks in the multivariate volatility framework, our attempts at establishing robustness for the results reveal that ignoring the same may lead to wrong conclusions about the hedging effectiveness.

A multivariate volatility vine copula model

2016 ◽  
Vol 37 (4) ◽  
pp. 281-308 ◽  
Author(s):  
E. C. Brechmann ◽  
M. Heiden ◽  
Y. Okhrin
Keyword(s):  
Copula Model ◽  
Vine Copula ◽  
Multivariate Volatility

scholarly journals MULTIVARIATE VOLATILITY MODELING OF ELECTRICITY FUTURES

2012 ◽  
Vol 28 (5) ◽  
pp. 743-761 ◽  
Author(s):  
Luc Bauwens ◽  
Christian M. Hafner ◽  
Diane Pierret
Keyword(s):  
Volatility Modeling ◽  
Multivariate Volatility

scholarly journals Multivariate Volatility Models

2002 ◽  
pp. 221-236
Author(s):  
Matthias R. Fengler ◽  
Helmut Herwartz
Keyword(s):  
Volatility Models ◽  
Multivariate Volatility

scholarly journals Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors

2011 ◽  
Vol 231 (1) ◽  
Author(s):  
Roxana Halbleib ◽  
Valeri Voev
Keyword(s):  
Evaluation Criteria ◽  
Forecast Accuracy ◽  
Realized Volatility ◽  
Covariance Matrices ◽  
Risk Averse ◽  
Return Distribution ◽  
Volatility Model ◽  
Realized Covariance ◽  
Dominance Tests ◽  
Multivariate Volatility

SummaryThis paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. Bymodelling the Cholesky factors of the covariance matrices, the model generates positive definite, but biased covariance forecasts. In this paper, we provide empirical evidence that parsimonious versions of the model generate the best covariance forecasts in the absence of bias correction. Moreover, we show by means of stochastic dominance tests that any risk averse investor, regardless of the type of utility function or return distribution, would be better-off from using this model than from using some standard approaches.

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