Applied Quantitative Finance

This text explores developments and solutions for many practical problems confronting quantitative methods in financial research and industry. It is a synthesis of scientific contributions on practical implementation and theoretical concepts.

Presents the latest developments in risk management; market risk, credit risk and dynamics of risk management

Provides a unique balance between theoretical concepts, computational tools and practical implementation

Features fully reproducible results and provides the underlying quantlets on the accompanying website www.quantlet.de

Includes an innovative analysis of the dynamics of cryptocurrencies

Time varying LASSO yields a financial risk meter (FRM hu.berlin/frm)

Autorentext

Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. (Center for Applied Statistics and Economics), director of the CRC-649 (Collaborative Research Center) Economic Risk and director of the IRTG 1792 High Dimensional Non-stationary Time Series. He teaches quantitative finance and semi-parametric statistics. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and advisor to the Guanghua School of Management, Peking University and a senior fellow of Sim Kee Boon Institute of Financial Economics at the Singapore Management University.

Cathy Yi-Hsuan Chen is guest professor at the Humboldt-Universität zu Berlin School of Business & Economics, a principal investigator in the International Research Training Group 1792 High Dimensional Non-stationary Time Series and visiting fellow at Sim Kee Boon Institute for Financial Economics, Singapore Management University. Her research interests focus on text mining, finance analysis and risk analysis and management. She has dedicated herself to applying text-mining techniques to distill news flow from social media. She has published in key journals and has written important software for financial econometrics. She applies modern econometric techniques, such as copulae and ultra-high dimensional factor models to financial data on systemic risk indicators. She has professional experience in risk modeling and management in banking industry.

Ludger Overbeck is Professor of Mathematics at the University of Gießen, specializing in stochastic processes, as well as mathematical financial and quantitative methods in risk management. His research covers a wide range of topics from infinite-dimensional stochastic analysis, like measure-valued processes, path-dependent stochastic equations and partial differential equations; pricing issues such as term structure modelling for credit products; risk management like portfolio credit risk; and the axiomatic approach to systemic dynamic risk measures. He gained broad professional experience in risk-management quantification issues during his career with the Deutsche Bundesbank, Deutsche Bank, UniCredit and Commerzbank.

Inhalt
Part I Market Risk: VaR in High-Dimensional Systems.- Multivariate Volatility Models.- Portfolio Selection with Spectral Risk Measures.- Implementation of Local Stochastic Volatility Model.- Part II Credit Risk: Estimating DTD via Sequential Monte Carlo.- Risk Measurement with Spectral Capital Allocation.- Market Based Credit Rating and its Applications.- Using Public Information to Predict Corporate Default Risk.- Stress Testing in Credit Portfolio Models.- Penalized Independent Factor.- Term Structure of Loss Cascades in Portfolio Securitisation.- Credit Rating Score Analysis.- Part III Dynamics Risk Measurement: Copulae in High Dimensions - An Introduction.- Measuring and Modeling Risk Using High-Frequency Data.- Measuring Financial Risk in Energy Markets.- Risk Analysis of Cryptocurrency as an Alternative Asset Class.- Time Varying Quantile Lasso.- Dynamic Topic Modelling for Cryptocurrency Community Forums.