Derivatives and Internal Models

Deutsch, Hans‐Peter; Eller, Roland

doi:10.1007/978-1-349-14979-7

Cited by 12 publications

(6 citation statements)

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“…The normal distribution of the empirical data is observable if these dots t the line; however, most nancial data have an 'S' shape in the QQ plot, suggesting a power-law distribution and fat tails (Clauset el al., 2009). By relying on the de nition of QQ plots proposed by Deutsch (2002), the above separation can be expressed in the following way (11):…”

Section: Methodsmentioning

confidence: 99%

The Euro Crisis and Contagion among Central and Eastern European Currencies: Recommendations for Avoiding Lending in a Safe Haven Currency such as CHF

Kiss¹,

Schuszter²

2015

Prague Economic Papers

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Abstract:This study analyses the Czech, Hungarian, and Polish currencies by examining the statistical characteristics of the Swiss franc as well as the ECB monetary policy in order to indicate shocks in these markets between 2002 and 2013. The abundance of monetary easing decisions can be used as a viable sign of market misbehaviour in addition to the low probability of extreme exchange rate fluctuations. Indeed, the temporal distribution of extreme currency fluctuations provides vital information about the nature of the recent crisis. Contagions can be defined as increased correlations during periods of crisis, while divergence means a significant decrease in this regard. Methodologically, common movements in this study were calculated by using DCC-GARCH modelling. The findings of this study underline the special features of the Swiss franc exchange rate, notably that its extreme fluctuations can be managed by using swap agreements and that it tended towards divergences during the crisis era. These results support the idea of avoiding lending in reserve currencies.

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Section: Methodsmentioning

confidence: 99%

The Euro Crisis and Contagion among Central and Eastern European Currencies: Recommendations for Avoiding Lending in a Safe Haven Currency such as CHF

Kiss¹,

Schuszter²

2015

Prague Economic Papers

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“…If α is the selected level of confidence, the Value-at-Risk corresponds to the 1 − α lower tail level. Extending this concept, the Expected Shortfall (also known as Conditional Value-at-Risk) for a given level of confidence α is defined as the expected loss, given the loss is larger or equal to the Value-atRisk (Deutsch, 2009).…”

Section: Risk Measures and Backtestingmentioning

confidence: 99%

“…To benchmark the simulated Value-at-Risk with a fully deterministic approach we apply historical simulation. The freedom from model assumptions is one of the primary advantages of this method (Deutsch, 2009). Based on the given time series and the target horizon of T = 250 business days we compute 751 daily Value-at-Risk estimates for the confidence level vector α i (α 1 = 95%, α 2 = 97.5%, α 3 = 99%).…”

Section: Risk Measures and Backtestingmentioning

confidence: 99%

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Estimation of risk measures in energy portfolios using modern copula techniques

Jaschke¹

2014

Computational Statistics & Data Analysis

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The present paper analyses the dependence structure between WTI and Brent crude oil spot log-returns using modern copula techniques. In a first step we apply several single equation models to the marginals to account for autocorrelation and volatility clustering. Second, to select both copulas and tail copulas characterising the joint dynamics between the time series we implement and evaluate newly introduced bootstrap-based goodness-of-fit tests. Based on each approach, a comprehensive backtesting is performed by simulating and comparing the risk measures Value-at-Risk and Expected Shortfall with historical values.

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“…Simulation methods were extensively used in assets pricing, such as covariance matrix based approaches, historical simulation approaches and the scenario based approaches (Deutsche, 2002) were usually mentioned. However, one assumption of the covariance matrix based approaches is portfolio value changes in a linear manner with changes in the risk factor.…”

Section: Introductionmentioning

confidence: 99%

Comparisons of non-parametric disturbance simulations and Monte Carlo approach

Keng¹,

Shian²

2011

Afr. J. Bus. Manage.

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This paper utilized the proposed historical simulation, where the effect of GARCH (1,1) model on price path were considered, and the Monte Carlo approach were also used to examine the difference in option payoff values between these simulation approaches and the original path. Furthermore, we showed which simulation model would have smaller root mean squared pricing error by examining the difference of root mean squared pricing error between these approaches. We applied these approaches to simulate option payoff values on the Shenzhen composite index series in China during the period 2005 to 2009, and the common back-testing approach was used. The results showed that the estimated option values were significant and differ from the actual Shenzhen composite index option payoff values for the observed period. Finally, we found that the root mean squared pricing error of the adjusted historical simulation is less than the other two simulation approaches.

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Derivatives and Internal Models

Cited by 12 publications

References 0 publications

The Euro Crisis and Contagion among Central and Eastern European Currencies: Recommendations for Avoiding Lending in a Safe Haven Currency such as CHF

The Euro Crisis and Contagion among Central and Eastern European Currencies: Recommendations for Avoiding Lending in a Safe Haven Currency such as CHF

Estimation of risk measures in energy portfolios using modern copula techniques

Comparisons of non-parametric disturbance simulations and Monte Carlo approach

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