Similar Search Results

Support vector regression based GARCH model with application to forecasting volatility of financial returns

Chen, Shiyi; Jeong, Kiho; Härdle, Wolfgang Karl - 2008

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

Persistent link: https://www.econbiz.de/10010274143

Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

Härdle, Wolfgang Karl; Hautsch, Nikolaus; Mihoci, Andrija - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2012

modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM …

Persistent link: https://www.econbiz.de/10010544325

Adaptive Order Flow Forecasting with Multiplicative Error Models

Härdle, Wolfgang Karl; Mihoci, Andrija; Ting, … - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2014

A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading...

Persistent link: https://www.econbiz.de/10010895342

Local adaptive multiplicative error models for high-frequency forecasts

Härdle, Wolfgang Karl; Hautsch, Nikolaus; Mihoci, Andrija - 2012

modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM …

Persistent link: https://www.econbiz.de/10010330969

Adaptive order flow forecasting with multiplicative error models

Härdle, Wolfgang Karl; Mihoci, Andrija; Ting, … - 2014

Persistent link: https://www.econbiz.de/10010427064

Graphical data representation in bankruptcy analysis

Härdle, Wolfgang Karl; Moro, Rouslan A.; Schäfer, Dorothea - 2006

Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient representation of ratings in a closed form is possible reducing...

Persistent link: https://www.econbiz.de/10010274115

Estimation of default probabilities with Support Vector Machines

Chen, Shiyi; Härdle, Wolfgang Karl; Moro, Rouslan A. - 2006

Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to...

Persistent link: https://www.econbiz.de/10010274122

The bayesian additive classification tree applied to credit risk modelling

Zhang, Junni L.; Härdle, Wolfgang Karl - 2008

We propose a new nonlinear classification method based on a Bayesian sum-of-trees model, the Bayesian Additive Classification Tree (BACT), which extends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric additive...

Persistent link: https://www.econbiz.de/10010274137

The default risk of firms examined with smooth support vector machines

Härdle, Wolfgang Karl; Lee, Yuh-Jye; Schäfer, Dorothea; … - 2008

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth...

Persistent link: https://www.econbiz.de/10010274139

Recurrent support vector regression for a nonlinear ARMA model with applications to forecasting financial returns

Chen, Shiyi; Jeong, Kiho; Härdle, Wolfgang Karl - 2008

Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns … to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the … focus on fit in-sample, but SVR considers both fit and forecast out-of-sample which endows SVR with an excellent forecasting …

Persistent link: https://www.econbiz.de/10010274149