A VaR assuming Student t distribution not significantly different from a VaR assuming normal distribution

Xu, Su

doi:10.1057/s41283-017-0017-9

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Since volatility estimation is a key input to VaR (value-at-risk) models, the selection of the appropriate volatility model is one of the most important factors in determining the accuracy of VaR. According to the distribution of errors, when the mean return is zero, the 1-day horizon VaR for an individual asset with probability p% can be calculated as follows (Xu, 2017):…”

Section: Value-at-risk Estimationmentioning

confidence: 99%

GARCH-X(1; 1) model allowing a non-linear function of the variance to follow an AR(1) process

Nugroho

Wicaksono

Larwuy

2023

Communications for Statistical Applications and Methods

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GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCH-X(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

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Section: Value-at-risk Estimationmentioning

confidence: 99%

GARCH-X(1; 1) model allowing a non-linear function of the variance to follow an AR(1) process

Nugroho

Wicaksono

Larwuy

2023

Communications for Statistical Applications and Methods

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Equity Price Risk of Commercial Banks in India

Das

Rout

2022

Arthaniti: Journal of Economic Theory and Practice

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This study is directed at gauging the equity price risk of the Indian commercial banks for the period 2003–2020. Parametric value-at-risk (VaR) is employed to estimate the downside risk. Further, the univariate exponential generalized auto regressive conditional heteroskedasticity (EGARCH) model is also used to find out the existence of stylised aspects of volatility. The outcomes point towards the existence of volatility clustering, persistence and asymmetry, but differ from bank to bank. Furthermore, the parametric VaR model that assumes normal distribution and student’s t-distribution is not found to be an accurate model for all the banks. Tail risk is also found to be significant, and thus, justifies the Basel Committee’s decision to shift towards an expected shortfall. However, these conventional VaR models should be supplemented by internal models, taking into consideration, bank-specific characteristics. JEL: G01, G15, I15, G17, G28

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A VaR assuming Student t distribution not significantly different from a VaR assuming normal distribution

Cited by 2 publications

References 14 publications

GARCH-X(1; 1) model allowing a non-linear function of the variance to follow an AR(1) process

GARCH-X(1; 1) model allowing a non-linear function of the variance to follow an AR(1) process

Equity Price Risk of Commercial Banks in India

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