Applied Quantitative Finance

• Value at Risk.- Modeling Dependencies with Copulae.- Quantification of Spread Risk by Means of Historical Simulation.- A Copula-Based Model of the Term Structure of CDO Tranches.- VaR in High Dimensional Systems – a Conditional Correlation Approach.- Credit Risk.- Rating Migrations.- Cross- and Autocorrelation in Multi-Period Credit Portfolio Models.- Risk Measurement with Spectral Capital Allocation.- Valuation and VaR Computation for CDOs Using Stein’s Method.- Implied Volatility.- Least Squares Kernel Smoothing of the Implied Volatility Smile.- Numerics of Implied Binomial Trees.- Application of Extended Kalman Filter to SPD Estimation.- Stochastic Volatility Estimation Using Markov Chain Simulation.- Measuring and Modeling Risk Using High-Frequency Data.- Valuation of Multidimensional Bermudan Options.- Econometrics.- Multivariate Volatility Models.- The Accuracy of Long-term Real Estate Valuations.- Locally Time Homogeneous Time Series Modelling.- Simulation Based Option Pricing.- High-Frequency Volatility and Liquidity.- Statistical Process Control in Asset Management.- Canonical Dynamics Mechanism of Monetary Policy and Interest Rate.

From the reviews of the second edition: "The second edition ... compared with the first, has widened the scope of the overall message and topics. ... have also included more up-to-date data. ... designed for students and researchers who want to develop a professional skill in modern quantitative applications in finance. ... The aim is to make the course readable for graduate students in financial engineering but also to those who are newcomers to quantitative finance and who want to get a grip on modern statistical tools in financial data analysis." (Richard Kirby, The Mathematical Association of America, September, 2009)