Modelling, Estimation and Visualization of Multivariate Dependence for High-frequency Data

Brodin, Erik; Klüppelberg, Claudia

doi:10.1007/978-3-7908-2413-1_15

Cited by 5 publications

(9 citation statements)

References 13 publications

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“…Hence, each of the series has a total length of 6 391 data points. This is part of a data set, which was analysed in Brodin and Klüppelberg (2006) with respect to the extreme dependence structure of the three stocks.…”

Section: Real Data Analysismentioning

confidence: 99%

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Method of moment estimation in the COGARCH(1,1) model

Haug

Klüppelberg

Lindner

et al. 2007

The Econometrics Journal

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We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on equally spaced observations. Using the fact that the increments of the COGARCH(1,1) process are strongly mixing with exponential rate, we show that the resulting estimators are consistent and asymptotically normal. We investigate the empirical quality of our estimators in a simulation study based on the variance gamma driven COGARCH(1,1) model. The estimated volatility with corresponding residual analysis is also presented. Finally, we fit the model to high-frequency data. Copyright Royal Economic Society 2007

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Section: Real Data Analysismentioning

confidence: 99%

“…The effect of seasonality is common in high‐frequency data and also appears in the raw data. Therefore, the data was deseasonalized by a median filter, which is explained in Section 4.2 in Brodin and Klüppelberg (2006). The resulting time series are shown in Figure 3.…”

Section: Real Data Analysismentioning

confidence: 99%

Method of moment estimation in the COGARCH(1,1) model

Haug

Klüppelberg

Lindner

et al. 2007

The Econometrics Journal

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“…[26,27]. The tail dependence function is also invoked in [8], which includes an in depth analysis of multivariate high-frequency equity data.…”

Section: Multivariate Issuesmentioning

confidence: 99%

Extreme Value Theory in Finance

Brodin¹,

Klüppelberg

2014

Wiley StatsRef: Statistics Reference Online

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“…In the last decade, a considerable number of studies have concentrated on models that could account for asymmetry and non-normality of returns in financial markets, and therefore demonstrate the weaknesses of traditional parametric VaR methods that assume a normal distribution of returns. Brodin and Klüppelberg (2010), e.g. criticised the theoretical properties and robustness of standard VaR in statistical estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Rootzèn and Klüppelberg (1999) proposed a coherent risk measure closely related to VaR, known as the CVaR or the expected shortfall (ES). Brodin and Klüppelberg (2010) argued that although ES is a more informative risk measure than VaR, it suffers from an even higher variability as it uses information very far out in the tail. As noted by Bensalah (2000), investors and risk managers have become more concerned with events occurring under extreme market conditions.…”

Section: Introductionmentioning

confidence: 99%

Extreme Value at Risk: A Scenario for Risk Management

Kabundi

Muteba

2011

South African J Economics

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This paper uses the generalised extreme value (GEV) distribution to model the extreme losses that are likely to occur during market crashes, in the case of an investor who has long positions in stocks and currencies. The null hypothesis -which tests for normality of asset returns -is rejected due to asymmetry of these returns. We assume that the asymmetric behaviour and volatility of the returns are captured by the shape and scale parameters, respectively, of a GEV distribution. The data set includes stock indices for the United States, Japan, the United Kingdom, Germany, France and South Africa, and the South African rand exchange rates against the US dollar observed from 3 . We compared the estimates of value at risk (VaR) using an extreme value theory (EVT) model, with the estimates derived from the traditional variance-covariance method and found that during the crisis the 99% extreme VaR estimates are more reliable as they lie within the Basel II green zone. These results suggest that, at higher quintiles, the VaR estimates based on EVT are reliable and more accurate than estimates from the traditional method. JEL Classification: C22, G10

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Modelling, Estimation and Visualization of Multivariate Dependence for High-frequency Data

Cited by 5 publications

References 13 publications

Method of moment estimation in the COGARCH(1,1) model

Method of moment estimation in the COGARCH(1,1) model

Extreme Value Theory in Finance

Extreme Value at Risk: A Scenario for Risk Management

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