Empirical analysis of jump dynamics, heavy-tails and skewness on value-at-risk estimation

Su, Jung-Bin; Hung, Jui‐Cheng

doi:10.1016/j.econmod.2010.11.016

Cited by 43 publications

(17 citation statements)

References 31 publications

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“…Moreover, many authors have also emphasized the importance of efficiency of VaR estimates (Hendricks, 1996;Sarma et al, 2003;Marcucci, 2005;Cheng and Hung, 2011;Hung et al, 2008;Sajjad et al, 2008;Su and Hung, 2011). The majority of these studies implement the two-stage evaluation procedure proposed by Sarma et al (2003).…”

Section: Var Evaluation Measuresmentioning

confidence: 99%

The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting

Louzis

Xanthopoulos-Sisinis

Refenes

2013

Journal of Forecasting

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In this paper, we assess the informational content of daily range, realized variance, realized bipower variation, two time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out-of-sample Value-at-Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed student distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi- Jel Classification: C13; C53; C58; G17; G21; G32

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Section: Var Evaluation Measuresmentioning

confidence: 99%

The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting

Louzis

Xanthopoulos-Sisinis

Refenes

2013

Journal of Forecasting

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“…The unconditional coverage test (LR uc ), proposed by Kupiec (1995), examines whether the unconditional coverage rate is statistically consistent with the confidence level prescribed for the VaR model. The null hypothesis is defined as the failure probability of each trial equals the specified probability of this model (α VaR In a risk management framework, it is of paramount importance that VaR exceptions be uncorrelated over time (Su & Hung, 2011). Thus, the conditional coverage test (LR cc ), as proposed by Christoffersen (1998), is addressed.…”

Section: 17mentioning

confidence: 99%

Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

Maciel

Ballini

2017

Rev. contab. finanç.

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This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

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“…Tavares, Curto and Tavare (2007) model the heavy tails and asymmetric effect on stocks returns volatility into the GARCH framework, and showed the Student's t and the stable Paretian with (α < 2) distribution clearly outperform the Gaussian distribution in fitting S&P 500 returns and FTSE returns. Su and Hung (2011) provides a comprehensive analysis of the possible influences of jump dynamics, heavy-tails, and skewness with regard to Value at Risk (VaR) estimates through the assessment of both accuracy and efficiency. Su and Hung consider a range of stock indices across international stock markets during the period of the U.S. Subprime mortgage crisis, and show that the GARCH model with normal, generalized error distribution (GED) and skewed normal distributions provide accurate VaR estimates.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Class of Heavy-Tailed Distribution and the Stock Market Returns in Germany

Oden¹,

Hurt²,

Gentry³

2017

RBM

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During the past decade, as the fourth largest economy over the world Germany's financial sector plays a key role in the global economy. As one of the most important components of the financial sector, the equity market played a more and more important role. Thus, risk management of its stock market is crucial for welfare of its market participants. To account for the two stylized facts, volatility clustering and conditional heavy tails, we take advantage of the framework in Guo (2017a) and consider empirical performance of the GARCH model with normal reciprocal inverse Gaussian distribution in fitting the German stock return series. Our results indicate the NRIG distribution has superior performance in fitting the stock market returns.

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Empirical analysis of jump dynamics, heavy-tails and skewness on value-at-risk estimation

Cited by 43 publications

References 31 publications

The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting

The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting

Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

A New Class of Heavy-Tailed Distribution and the Stock Market Returns in Germany

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