Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios

Janabi, Mazin A. M. Al; Hernández, José Arreola; Berger, Theo; Nguyen, Duc Khuong

doi:10.1016/j.ejor.2016.11.019

Cited by 49 publications

(62 citation statements)

References 54 publications

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“…In addition, the effect of liquidity risks on VaR also needs to be accounted for, because of the illiquid scenarios under market turmoil and crises. Several works have developed liquidity‐adjusted VaR models (e.g., Al Janabi, Hernandez, Berger, & Nguyen, ; Berkowitz, ; Weiß & Supper, ). Al Janabi et al (), for instance, propose a model to optimize structured portfolios with liquidity‐adjusted VaR, while taking into account the nonlinear dependence structure among portfolio assets.…”

Section: Resultsmentioning

confidence: 99%

“…Several works have developed liquidity‐adjusted VaR models (e.g., Al Janabi, Hernandez, Berger, & Nguyen, ; Berkowitz, ; Weiß & Supper, ). Al Janabi et al (), for instance, propose a model to optimize structured portfolios with liquidity‐adjusted VaR, while taking into account the nonlinear dependence structure among portfolio assets. They show evidence of the superiority of their models over the competing portfolio strategies including the minimum variance, risk parity and equally weighted portfolio allocations.…”

Section: Resultsmentioning

confidence: 99%

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Value‐at‐risk under market shifts through highly flexible models

BenSaïda

Boubaker

Nguyen

et al. 2018

Journal of Forecasting

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Managing market risk under unknown future shocks is a critical issue for policymakers, investors, and professional risk managers. Despite important developments in market risk modeling and forecasting over recent years, market participants are still skeptical about the ability of existing econometric designs to accurately predict potential losses, particularly in the presence of hidden structural changes. In this paper, we introduce Markov‐switching APARCH models under the skewed generalized t and the generalized hyperbolic distributions to fully capture the fuzzy dynamics and stylized features of financial market returns and to generate value‐at‐risk (VaR) forecasts. Our empirical analysis of six major stock market indexes shows the superiority of the proposed models in detecting and forecasting unobservable shocks on market volatility, and in calculating daily capital charges based on VaR forecasts.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Value‐at‐risk under market shifts through highly flexible models

BenSaïda

Boubaker

Nguyen

et al. 2018

Journal of Forecasting

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“…These empirical findings have important implications to portfolio management practices and may appeal to risk managers, portfolio managers and regulatory agencies in wake of the repercussions of the 2007-2009 GFC. Portfolio managers are increasingly interested in managing liquidity risk, specifically the downside risk associated with liquidity exposure (e.g., Al Janabi et al, 2017).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Pricing of time-varying illiquidity within the Eurozone: Evidence using a Markov switching liquidity-adjusted capital asset pricing model

Grillini

Özkan

Sharma

et al. 2019

International Review of Financial Analysis

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This paper investigates time-varying characteristics of illiquidity and the pricing of its risk using a liquidity-adjusted capital asset pricing model (L-CAPM). Collecting data from a pool of Eurozone countries between 1990 and 2018, we employ Markov switching models to assess the degree of persistence of illiquidity shocks. Contrary to prior research, which largely makes use of autoregressive (AR) processes, we provide strong evidence that illiquidity is time-varying and the persistence of shocks determines two distinct regimes characterised by high and low illiquidity. We assess pricing of illiquidity risk by developing and empirically testing a conditional L-CAPM model, where different regimes constitute priced risk factors for the cross-section of stock returns. We extend previous unconditional versions of L-CAPM models and we show that the various channels through which illiquidity affects asset returns and price of risks are time-varying. We find strong support for our conditional L-CAPM and our results are robust to alternative specifications and estimation techniques. These findings have important implications for portfolio management practices and are relevant to portfolio and risk managers and regulatory institutions.

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“…Recent papers in quantitative and mathematical finance have shown that copula models are flexible enough for modelling nonlinear dependence and tail risk (e.g. Al Janabi, Arreola Hernandez, Berger, & Nguyen, 2017; Grundke & Polle, 2012; Irresberger, Wei, & Gabrysch, 2018). When fitting copula functions, we particularly suggest a special forcing variable to better model the dependence parameter of the 90° rotated Clayton and 270° rotated Clayton copulas.…”

Section: Introductionmentioning

confidence: 99%

Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates

Bing-yue

Nguyen

et al. 2020

Int. J Fin Econ

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This article aims at investigating the dynamic dependence and extreme risk comovement of oil price and exchange rates in seven oil‐importing and seven oil‐exporting countries. For this purpose, we use six representative time‐varying copula models and four kinds of tail dependences to assess the downside and upside conditional value‐at‐risk measures (CoVaRs). Our findings indicate that the dependence of crude oil returns and exchange rates is negative for most pairs, that is, the rise (fall) in oil prices was accompanied by the appreciation (depreciation) of foreign currency against the US dollar. The oil price–exchange rate dependences in oil exporters is slightly larger than in oil importers, even though the dependence is weak in general. More interestingly, we find strong evidence of significant risk comovement between crude oil returns and exchange rates through the analysis of lower–upper and upper–lower tail dependence as well as downside and upside CoVaRs. This comovement particularly showed asymmetric effects. Finally, we sum up some implications for investors, policymakers and exchange rate regulators from a holistic perspective of empirical findings.

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Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios

Cited by 49 publications

References 54 publications

Value‐at‐risk under market shifts through highly flexible models

Value‐at‐risk under market shifts through highly flexible models

Pricing of time-varying illiquidity within the Eurozone: Evidence using a Markov switching liquidity-adjusted capital asset pricing model

Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates

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