Posted by **Veslefrikk** at Jan. 11, 2015

Wiley | 2002-01-18 | ISBN: 0471405566 | 256 pages | PDF | 2 MB

Posted by **Veslefrikk** at June 9, 2014

Wiley | 2002-01-18 | ISBN: 0471405566 | 256 pages | PDF | 1,7 MB

Posted by **Book-er** at March 24, 2009

Wiley | 2002-01-18 | ISBN: 0471405566 | 256 pages | PDF | 1,7 MB

Covers the hottest topic in investment for multitrillion pension market and institutional investors

Institutional investors and fund managers understand they must take risks to generate superior investment returns, but the question is how much. Enter the concept of risk budgeting, using quantitative risks measurements, including VaR, to solve the problem…

Posted by **Willson** at Nov. 30, 2016

English | 2005 | ISBN: 0471491446 | 424 pages | PDF | 2.3 MB

Posted by **ChrisRedfield** at Nov. 29, 2013

Published: 2005-03-25 | ISBN: 0471491446 | PDF | 424 pages | 4 MB

Posted by **ChrisRedfield** at Aug. 17, 2012

Published: 2005-03-25 | ISBN: 0471491446 | DJVU | 424 pages | 3 MB

Posted by **Rare-1** at July 14, 2015

English | Springer (2008) | ISBN-10: 3540786562 | 206 pages | ُPDF | 7.40 MB

This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of these models from a Bayesian viewpoint is relatively recent and can be considered very promising due to the advantages of the Bayesian approach, in particular the possibility of obtaining small-sample results and integrating these results in a formal decision model. The first two chapters introduce the work and give an overview of the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal innovations and the linear regression models with conditionally Normal and Student-t-GJR errors. The sixth chapter shows how agents facing different risk perspectives can select their optimal Value at Risk Bayesian point estimate and documents that the differences between individuals can be substantial in terms of regulatory capital. The last chapter proposes the estimation of a Markov-switching GJR model.

Posted by **step778** at June 11, 2014

2008 | pages: 203 | ISBN: 3540786562 | PDF | 7,4 mb

Posted by **arundhati** at March 8, 2017

2015 | 208 pages | ISBN: 3319260375 | PDF | 4 MB

Posted by **hill0** at March 3, 2017

English | 20 Mar. 2017 | ISBN: 3319516663 | 184 Pages | PDF | 2.45 MB

This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing,