Dunis Advances in Quantitative Asset Management

Erscheinungsjahr 2012
ISBN: 978-1-4615-4389-3
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1, 342 Seiten, eBook

Reihe: Studies in Computational Finance

Advances in Quantitative Asset Management contains selected articles which, for the most part, were presented at the `Forecasting Financial Markets' Conference. `Forecasting Financial Markets' is an international conference on quantitative finance which is held in London in May every year. Since its inception in 1994, the conference has grown in scope and stature to become a key international meeting point for those interested in quantitative finance, with the participation of prestigious academic and research institutions from all over the world, including major central banks and quantitative fund managers. The editor has chosen to concentrate on advances in quantitative asset management and, accordingly, the papers in this book are organized around two major themes: advances in asset allocation and portfolio management, and modelling risk, return and correlation.

Zielgruppe

Research

Autoren/Hrsg.

Dunis, Christian

Inhaltsverzeichnis

1: Advances In Asset Allocation And Portfolio Management.- 1. Introducing Higher Moments in the CAPM: Some Basic Ideas.- 2. Fat Tails and the Capital Asset Pricing Model.- 3. The Efficiency of Fund Management: An Applied Stochastic Frontier Model.- 4. Investment Styles in the European Equity Markets.- 5. Advanced Adaptive Architectures for Asset Allocation.- 6. High Frequency Data and Optimal Hedge Ratios.- 2: Modelling Risk, Return And Correlation.- 7. Large Scale Conditional Correlation Estimation.- 8. The Pitfalls in Fitting GARCH(1,1) Processes.- 9. Factor GARCH, Regime-Switching and the Term Structure of Interest Rates.- 10. Hedging a Portfolio of Corporate Bonds Using PCA/GARCH Yield Curve Analysis.- 11. Analysis of Time Varying Exchange Rate Risk Premia.- 12. Volatility Modelling in the Forex Market: An Empirical Evaluation.- 13. Five Classification Algorithms to Predict High Performance Stocks.- 14. Forecasting Financial Time Series with Generalized Long Memory Processes.