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Support vector regression based GARCH model with application to forecasting volatility of financial returns
Chen, Shiyi (contributor); Jeong, Kiho (contributor);  … - 2008
In recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving...
Persistent link: https://www.econbiz.de/10003636113
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Recurrent support vector regression for a nonlinear ARMA model with applications to forecasting financial returns
Chen, Shiyi (contributor); Jeong, Kiho (contributor);  … - 2008
Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns Abstract: Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data...
Persistent link: https://www.econbiz.de/10003770766
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Forecasting volatility with support vector machine-based GARCH model
Shiyi, Chen; Härdle, Wolfgang; Jeong, Kiho - In: Journal of forecasting 29 (2010) 4, pp. 406-433
Persistent link: https://www.econbiz.de/10003989791
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Forex exchange rate forecasting using deep recurrent neural networks
Dautel, Alexander Jakob; Härdle, Wolfgang; Lessmann, Stefan - In: Digital finance : smart data analytics, investment … 2 (2020) 1/2, pp. 69-96
Persistent link: https://www.econbiz.de/10012284950
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Uni- and multivariate extensions of the sinh-arcsinh normal distribution applied to distributional regression
Feng, Yuanhua; Härdle, Wolfgang - 2021
Persistent link: https://www.econbiz.de/10012625953
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Predicting Bankruptcy with Support Vector Machines
Härdle, Wolfgang; Moro, Rouslan A.; Schäfer, Dorothea - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2005
The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to...
Persistent link: https://www.econbiz.de/10005489966
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Statistics of Risk Aversion
Giacomini, Enzo; Härdle, Wolfgang - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007
theory enforces that the utility function is concave or equivalently, that investors are risk averse. Equilibrium and non … by the pricing kernel (PK). In this paper we investigate pricing kernels from DAX and ODAX data in a time varying … approach and consider their term structure. In order to approximate and analyse the complex dynamic structure from pricing …
Persistent link: https://www.econbiz.de/10005861030
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From Animal Baits to Investors´ Preference: Estimating and Demixingof the Weight Function in Semiparametric Models for Biased Samples
Ritov, Ya’acov; Härdle, Wolfgang - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007
We consider two semiparametric models for the weight function in a biased sample model. The object of our interest parametrizes the weight function, and it is either Euclidean or non Euclidean. One of the models discussed in this paper is motivated by the estimation the mixing distribution of...
Persistent link: https://www.econbiz.de/10005861031
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Empirical Pricing Kernels and Investor Preferences
Detlefsen, Kai; Härdle, Wolfgang; Moro, Rouslan - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007
This paper analyzes empirical market utility functions and pricing kernelsderived from the DAX and DAX option data for …
Persistent link: https://www.econbiz.de/10005861046
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A dynamic programming approach for pricing weather derivatives under issuer default risk
Härdle, Wolfgang; Osipenko, Maria - In: International Journal of Financial Studies : open … 5 (2017) 4, pp. 1-18
risk can transfer it to financial markets via weather derivatives. We develop a utility-based model for pricing baskets of …
Persistent link: https://www.econbiz.de/10011760235