Evaluating the performance of the skewed distributions to forecast value-at-risk in the global financial crisis

Romero, Pilar Abad; Muela, Sonia Benito; Granero, Miguel Ángel Sánchez; López, Carmen María López

doi:10.21314/j0r.2016.332

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…Hajihasani et al (2021) demonstrated the superior performance of q-Gaussian distribution VaR models over traditional normality-based VaR models. In line with this, Abad et al (2016) found that skewed distribution models outperform both normal and Student T distributions during financial distress. Miletic and Miletic (2015) further corroborated the superiority of GARCH-type VaR models over traditional variants.…”

Section: Value-at-risk Concepts and Limitationssupporting

confidence: 67%

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

Likitratcharoen,

Suwannamalik

2024

Risks

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The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising capital reserve strategies. Nonetheless, the predictive precision of VaR models frequently faces scrutiny, particularly during crises and heightened uncertainty phases. Phenomena like volatility clustering impinge on the accuracy of these models. To mitigate such constraints, conditional volatility models are integrated to augment the robustness and adaptability of VaR approaches. This study critically evaluates the efficacy of GARCH-type VaR models within the transportation sector amidst the Thai stock market’s volatility during the COVID-19 pandemic. The dataset encompasses daily price fluctuations in the Transportation Sector index (TRANS), the Service Industry index (SERVICE), and 17 pertinent stocks within the Stock Exchange of Thailand, spanning from 28 December 2018 to 28 December 2023, thereby encapsulating the pandemic era. The employed GARCH-type VaR models include GARCH (1,1) VaR, ARMA (1,1)—GARCH (1,1) VaR, GARCH (1,1)—M VaR, IGARCH (1,1) VaR, EWMA VaR, and csGARCH (1,1) VaR. These are juxtaposed with more traditional, less computationally intensive models like the Historical Simulation VaR and Delta Normal VaR. The backtesting methodologies encompass Kupiec’s POF test, the Independence Test, and Christoffersen’s Interval Forecast test. Intriguingly, the findings reveal that the Historical Simulation VaR model surpasses GARCH-type VaR models in failure rate accuracy. Within the GARCH-type category, the EWMA VaR model exhibited superior failure rate accuracy. The csGARCH (1,1) VaR and EWMA VaR models emerged as notably robust. These findings bear significant implications for managerial decision-making in financial risk management.

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Section: Value-at-risk Concepts and Limitationssupporting

confidence: 67%

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

Likitratcharoen,

Suwannamalik

2024

Risks

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“…We use M1 and M2 as a general notation to refer to any pair of models under comparison. 2 Definition 1. Let β be some specified threshold between 0 and 1, let R 1 and R 2 be the number of VaR validation tests in which the models M1 and M2 are rejected at a chosen significance level p, and let r 12 be the number of common rejections for both models.…”

Section: Dominance Among Var Modelsmentioning

confidence: 99%

“…A key limitation of such tests is that they do not distinguish between returns that are below but far from the VaR and those that are below but close to the VaR. A standard solution has consisted in considering some loss function defined on excess returns over and above the VaR (see, for example, Abad et al, 2016;;Gerlach et al, 2011; Lee and Su, 2015;Louzis et al, 2014;Ozun et al, 2010). Various heuristic criteria have been proposed in the literature to support specific loss functions, usually penalizing models that produce exceedances with a large deviation from VaR (see, for example, Lopez, 1998Lopez, , 1999Sarma et al, 2003; Giacomini and Komunjer, 2005;Caporin, 2008).…”

Section: Introductionmentioning

confidence: 99%

A dominance approach for comparing the performance of VaR forecasting models

Garcia‐Jorcano

Novales

2020

Comput Stat

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The Influences of Book-to-Price Ratio and Stock Capitalization on Value-at-Risk Estimation in Taiwan Stock Market

Huang

Wang

et al. 2018

Emerging Markets Finance and Trade

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Evaluating the performance of the skewed distributions to forecast value-at-risk in the global financial crisis

Abstract: Executive summary:This paper evaluates the performance of several skewed and symmetric distributions in modeling the tail behavior of daily returns and forecasting Value at Risk (VaR). First, we used

Cited by 7 publications

References 36 publications

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

A dominance approach for comparing the performance of VaR forecasting models

The Influences of Book-to-Price Ratio and Stock Capitalization on Value-at-Risk Estimation in Taiwan Stock Market

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