Portfolio optimization in hedge funds by OGARCH and Markov Switching Model

Luo, Cuicui; Seco, Luis; Wu, Lin-Liang Bill

doi:10.1016/j.omega.2015.01.021

Cited by 19 publications

(11 citation statements)

References 21 publications

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“…To test the robustness of our findings, we considered three additional financial performance evaluation measures. We used the adaptation proposed by Ferruz and Sarto (FS) [42] regarding the Sharpe ratio used previously by some studies [43,44]. FS [42] noted that the Sharpe ratio assumes positive portfolio excess returns.…”

Section: Robustness Checksmentioning

confidence: 99%

Financial Performance of Government Bond Portfolios Based on Environmental, Social and Governance Criteria

2019

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We evaluated the financial performance of government bond portfolios formed according to socially responsible investment (SRI) criteria. We thus open a discussion on the financial performance of SRI for government bonds. Our sample includes 24 countries over the period of June 2006 to December 2017. Using various financial performance measures, the results suggest that high-rated government bonds, according to environmental, social, and governance (ESG) dimensions, outperform low-ranked bonds under any cut-off, although differences are not statistically significant. These findings suggest that ESG screenings can be used for government bonds without sacrificing financial performance.

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Section: Robustness Checksmentioning

confidence: 99%

Financial Performance of Government Bond Portfolios Based on Environmental, Social and Governance Criteria

2019

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“…Dark [40] noticed robustness when hedging via MS-VECM-FIEGARCH, MS-VECM-FIAPARCH and MS-VECM-FIGARCH as long as such models constantly outperform short memory volatility models, as well as the OLS hedge. Luo et al [41] found that the optimal portfolio selected by an orthogonal GARCH model outperforms the exponentially weighted moving average (EWMA), Markov switching model (MSM) and the Gaussian mixture model (GMM) with regards to the risk and returns through the entire investment period. Yan and Li [42] assessed the performance of hedge ratios out of Chinese stock index future markets by means of ordinary least squares model, diagonal Bekk-Garch, and regime-switching diagonal Bekk-Garch models.…”

Section: Literature Reviewmentioning

confidence: 99%

A Markov Regime Switching Approach towards Assessing Resilience of Romanian Collective Investment Undertakings

et al. 2019

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This paper explores the sensitivity of Romanian collective investment undertakings’ returns to changes in equity, fixed income and foreign exchange market returns. We use a sample of 80 open-end investment funds and pension funds with daily returns between 2016 and 2018. Our methodology consists of measuring changes in the daily conditional volatility for the fund returns (EGARCH) and changes in their conditional correlation with selected market risk factors (DCC MV-GARCH) throughout different volatility regimes identified using a Markov Regime Switching model. We argue that, on average, the level of conditional correlations between funds and market risk factors remained stable and unconcerned by the volatility regimes. In addition, for only less than half of the funds in the sample, their volatility regimes were synchronized with those of the selected market risk factors. We found that, on average, fund returns are more correlated with equity returns and less correlated with changes in local bond yields, while not being significantly influenced by changes in foreign bond yields or changes in foreign exchange. During the period investigated equity returns were the most volatile while the funds returns volatility were, on average, much more reduced. Overall, our results show the resilience of the Romanian collective investment sector to the selected market risk factors, during the investigated period.

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“…The observed time series can be linearly transformed to a set of uncorrelated time series using a principal component analysis. This model has commonly been used in much research to model the conditional covariance of financial time series due to its feasibility in estimating large covariance matrices (Weide, 2002;Luo, Seco, & Wu, 2015). For non-Gaussian data, the independent component analysis (ICA) is used to perform the orthogonal transformation.…”

Section: Generalised Orthogonal Garch (Go-garch)mentioning

confidence: 99%

Evaluation of multivariate GARCH models in an optimal asset allocation framework

Aziz

Vrontos

Hasim

2019

The North American Journal of Economics and Finance

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JEL classification: C58 G11 G15 G17 G32 A B S T R A C TThis paper analyses plethora of advanced multivariate econometric models, which forecast the mean and variance-covariance of the asset returns to create optimal asset allocation models. Most existing studies use a limited number of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. In this study, we provide an in-depth knowledge of large asset modeling and optimization strategies for solving a portfolio selection problem involving the dynamic conditional correlation models (DCC). Specifically, we use symmetric GARCH models and an asymmetric version of it (GJR-GARCH). Several studies have also tried to examine the effectiveness of using parametric copula in estimating portfolio risk measures but their results have been inconclusive. We are interested in evaluating if Copula-GARCH could be an optimal model for assessing the performance of a portfolio. This study, therefore, implemented various Copula-GARCH based models using the static and dynamic estimation of the correlation. By employing different model specifications, we are able to explore the empirical applicability of the multivariate GARCH models when estimating large conditional covariance matrices. In constructing the optimal portfolios, we evaluate the minimum variance, mean-variance, maximising Sharpe ratio, mean-CVaR, and maximization of Sortino ratio. We compare the out-of-sample performance for each of the models based on the risk-adjusted performance for a portfolio with and without short sales, consisting eight stocks and four bond indices of 10 years maturity, in the United States (US), Kong. Our results suggest that the dynamic models are more capable of delivering better performance gains than the static models. These models reduce portfolio risk and improve the realized return in the out-of-sample period. This paper concludes that by adding copula functions to the model, it does not give a better performance model when compared to the dynamic correlation model.

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Portfolio optimization in hedge funds by OGARCH and Markov Switching Model

Cited by 19 publications

References 21 publications

Financial Performance of Government Bond Portfolios Based on Environmental, Social and Governance Criteria

Financial Performance of Government Bond Portfolios Based on Environmental, Social and Governance Criteria

A Markov Regime Switching Approach towards Assessing Resilience of Romanian Collective Investment Undertakings

Evaluation of multivariate GARCH models in an optimal asset allocation framework

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