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The Default Risk of Firms Examined with Smooth Support Vector Machines

Härdle, Wolfgang; Lee, Yuh-Jye; Schäfer, Dorothea; … - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007

In the era of Basel II a powerful tool for bankruptcy prognosis isvital for banks. The tool must be precise but also easily adaptable tothe bank's objections regarding the relation of false acceptances (TypeI error) and false rejections (Type II error). We explore the suitabil-ity of Smooth...

Persistent link: https://www.econbiz.de/10005860752

Ein Vergleich des binären Logit-Modells mit künstlichen neuronalen Netzen zurInsolvenzprognose anhand relativer Bilanzkennzahlen

Franken, Ronald - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007

Jahresabschlüssen von Kapitalgesellschaften abgeleitet werden können. In der aktuellen Praxis der empirischen Insolvenz- und …

Persistent link: https://www.econbiz.de/10005860996

Estimating Probabilities of Default With Support Vector Machines

Härdle, Wolfgang; Moro, Rouslan; Schäfer, Dorothea - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of...

Persistent link: https://www.econbiz.de/10005861009

Estimation of Default Probabilities with Support Vector Machines

Shiyi, Chen; Härdle, Wolfgang K.; Moro, Rouslan Arthur - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 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/10005861245

The Bayesian Additive Classification Tree Applied to Credit Risk Modelling

Zhang, Junni L.; Härdle, Wolfgang - Sonderforschungsbereich Ökonomisches Risiko <Berlin>

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

Persistent link: https://www.econbiz.de/10005860755

CDO Pricing with Copulae

Choroś, Barbara; Härdle, Wolfgang; Okhrin, Ostap - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2009

Modeling the portfolio credit risk is one of the crucial issues of the last yearsin the financial problems. We propose the valuation model of Collateralized DebtObligations based on a one- and two-parameter copula and default intensities estimatedfrom market data. The presented method is used to...

Persistent link: https://www.econbiz.de/10005865449

Robust Optimal Control for a Consumptioninvestment Problem

Schied, Alexander - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007

volatility and long-term trend are driven by an external stochastic factor process. The robust utility functional is defined in …

Persistent link: https://www.econbiz.de/10008939751

Yield Curve Factors, Term Structure Volatility, and Bond Risk Premia

Hautsch, Nikolaus; Ou, Yangguoyi - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2008

autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term … additional determinants of future excess returns. Finally, we illustrate that the yield and volatility factors are closely …

Persistent link: https://www.econbiz.de/10005860483

Simultaneous Stochastic Volatility TransmissionAcross American Equity Markets

Weber, Enzo - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2008

Information ows across international financial markets typically occur within hours, making volatility spillover appear … contemporaneous in daily data. Such simultaneous transmission of variances is featured by the stochastic volatility model developed in … heteroscedasticity of the structural volatility innovations, and estimation takes place in an appropriately specified state space setup …

Persistent link: https://www.econbiz.de/10005860498

Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models

Hautsch, Nikolaus; Jeleskovic, Vahidin - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2008

In this paper, we study the dynamic interdependencies between high-frequency volatility, liquidity demand as well as …. Liquidity is causal for future volatility but not vice versa. Furthermore, trade sizes are negatively driven bypast trading …

Persistent link: https://www.econbiz.de/10005860504