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Simonato, Jean-Guy - In: Quantitative Finance 11 (2011) 8, pp. 1165-1176

of European and American options in Merton's jump diffusion framework and Duan's GARCH framework are examined. …

Persistent link: https://www.econbiz.de/10009214979

The robustness of modified unit root tests in the presence of GARCH

Cook, Steven - In: Quantitative Finance 6 (2006) 4, pp. 359-363

Dickey-Fuller unit root tests in the presence of generalized autoregressive conditional heteroskedasticity (GARCH). Using … Monte Carlo simulation, the properties of the tests are examined for a range of GARCH processes over alternative sample … persistence, of the underlying GARCH process. While the original Dickey-Fuller test is found to exhibit greater size distortion …

Persistent link: https://www.econbiz.de/10005462655

Conditional tail behaviour and Value at Risk

Bellini, Fabio; Figa-talamanca, Gianna - In: Quantitative Finance 7 (2007) 6, pp. 599-607

calculation methods and with GARCH VaR by means of several back-testing techniques that take into account not only the number of …

Persistent link: https://www.econbiz.de/10005462657

Higher order and recurrent neural architectures for trading the EUR/USD exchange rate

Dunis, Christian; Laws, Jason; Sermpinis, Georgios - In: Quantitative Finance 11 (2010) 4, pp. 615-629

to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate. This is done by benchmarking three …

Persistent link: https://www.econbiz.de/10009214943

Volatility forecasts and at-the-money implied volatility: a multi-component ARCH approach and its relation to market models

Zumbach, Gilles - In: Quantitative Finance 11 (2011) 1, pp. 101-113

forecast provided by an I-GARCH(1) process (one time scale) does not capture correctly the dynamics of the realized volatility …. An I-GARCH(2) process (two time scales, similar to GARCH(1,1)) is better, while a long-memory LM-ARCH process (multiple …

Persistent link: https://www.econbiz.de/10009208332

Hierarchies of Archimedean copulas

Savu, Cornelia; Trede, Mark - In: Quantitative Finance 10 (2010) 3, pp. 295-304

We present a flexible class of hierarchical copulas capable of modelling multidimensional joint distributions of asset returns with a richer rank correlation structure than existing models. We derive estimators and simulation techniques. The methods are applied to an illustrative portfolio...

Persistent link: https://www.econbiz.de/10008503056

Embracing change: financial informatics and risk analytics

Flood, Mark - In: Quantitative Finance 9 (2009) 3, pp. 243-256

We present an enterprise design pattern for managing metadata in support of financial analytics packages. The complexity of financial modelling typically requires deployment of multiple financial analytics packages, drawing data from multiple source systems. Business domain experts are typically...

Persistent link: https://www.econbiz.de/10004982261

Stress testing for VaR and CVaR

Dupacova, Jitka; PolIvka, Jan - In: Quantitative Finance 7 (2007) 4, pp. 411-421

The practical use of the contamination technique in stress testing for risk measures Value at Risk (VaR) and Conditional Value at Risk (CVaR) and for optimization problems with these risk criteria is discussed. Whereas for CVaR its application is straightforward, the presence of the simple...

Persistent link: https://www.econbiz.de/10005462661

On the super-replicating approach when trading a derivative is limited

Isaenko, Sergei - In: Quantitative Finance 8 (2008) 3, pp. 285-297

We extend the theory of super-replicating a European option by relaxing its two main assumptions: we take into account the constraints on trading the option and allow it to be traded inter-temporally. The first extension has a dramatic effect on the price of a portfolio hedging the option, while...

Persistent link: https://www.econbiz.de/10005462684

MaxVaR for non-normal and heteroskedastic returns

Bhattacharyya, Malay; Misra, Nityanand; Kodase, Bharat - In: Quantitative Finance 9 (2009) 8, pp. 925-935

In this work we propose Monte Carlo simulation models for dynamically computing MaxVaR for a financial return series. This dynamic MaxVaR takes into account the time-varying volatility as well as non-normality of returns or innovations. We apply this methodology to five stock market indices. To...

Persistent link: https://www.econbiz.de/10008609623