Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states

Buczyński, Mateusz; Chlebuś, Marcin

doi:10.21314/jrmv.2020.222

Cited by 4 publications

(6 citation statements)

References 31 publications

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“…While the AVG strategy has been used by Taylor (2020) for both VaR and ES forecasting, the other three strategies have been used only for VaR forecasting (Bayer, 2018; Buczyński & Chlebus, 2019; McAleer et al, 2013a, 2013b). Their applicability for ES forecast combining is assessed here.…”

Section: Forecast Combinationsmentioning

confidence: 99%

A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

Trucíos

Taylor

2022

Journal of Forecasting

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Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long-memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression-based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID-19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

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Section: Forecast Combinationsmentioning

confidence: 99%

A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

Trucíos

Taylor

2022

Journal of Forecasting

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“…Our methodology has significant advantages compared to other approaches presented in the literature (Caporin et al [13], Buczyński and Chlebus [12]). First, IVRVSRI uses systemic risk indication based on two simple and heavily grounded amongst market participants risk measures (IV and RV).…”

Section: Ivrvsri -Implied Volatility Realized Volaitlity Systemic Ris...mentioning

confidence: 95%

“…low vs. high volatility, high vs. moderate skewness). Buczyński and Chlebus [12] checked which of the VaR models should be used depending on the state of the market volatility. They showed that GARCH(1,1) with standardized student's t-distribution is least affected by changes in volatility among analysed models.…”

Section: Literature Reviewmentioning

confidence: 99%

The Systemic Risk Approach Based on Implied and Realized Volatility

2023

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“…However, a potential disadvantage of this approach is the possibility of consistently overestimating VaR, even during calm periods, which is not desirable. Previous studies by McAleer et al (2010) and Buczyński & Chlebus (2019) have successfully applied this method. This model forecasts from all models that were considered as input.…”

Section: The Lowest Varmentioning

confidence: 98%

Combining forecasts? Keep it simple

Lis,

Chlebus

2023

Central European Economic Journal

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This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR) modeling, aiming to enhance accuracy and provide novel insights. Employing daily returns data from 2000 to 2020 for gold, silver, oil, gas, and copper, various combination methods are evaluated using the Model Confidence Set (MCS) procedure. Results show individual models excel in forecasting VaR at a 0.975 confidence level, while combined methods outperform at 0.99 confidence. Especially during high uncertainty, as during COVID-19, combined forecasts prove more effective. Surprisingly, simple methods such as mean or lowest VaR yield optimal results, highlighting their efficacy. This study contributes by offering a broad comparison of forecasting methods, covering a substantial period, and dissecting crisis and prosperity phases. This advances understanding in financial forecasting, benefiting both academia and practitioners.

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Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states

Abstract: Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states. M Ma at te eu us sz z B Bu uc cz zy yń ńs sk ki i* *, , M Ma ar rc ci in n C Ch hl le eb bu us s

Cited by 4 publications

References 31 publications

A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

The Systemic Risk Approach Based on Implied and Realized Volatility

Combining forecasts? Keep it simple

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