Posted by Serhat Yucel, FRM on July 30, 2011
A Nice and easy to read short paper about quantitative finance and how it evolved by Attilio Meucci…
Abstract: There exist two separate branches of finance that require advanced quantitativetechniques: the “Q” area of derivatives pricing, whose task is to “extrapolatethe present”; and the “P” area of quantitative risk and portfolio management,whose task is to “model the future”.We briefly trace the history of these two branches of quantitative finance,highlighting their different goals and challenges. Then we provide an overview oftheir areas of intersection: the notion of risk premium; the stochastic processesused, often under different names and assumptions in the Q and in the P world;the numerical methods utilized to simulate those processes; hedging; and statisticalarbitrage.
Posted by Tolgahan YILMAZ on July 29, 2011
Abstract- In this paper, the performance of global minimum variance (GMV) portfolios constructed by DCC and DECO-GARCH are compared to that of GMV portfolios constructed by sample covariance and constant correlation methods in terms of reduced volatility. Also, the performance of GMV portfolios are tested against that of equally weighted and cap weighted portfolios. Portfolios are constructed from the stocks listed in Istanbul Stock Exchange 30 index (hereafter, ISE-30). The results show that GMV portfolios constructed by DCC-GARCH outperformed the other portfolios. In addition, the performance of GMV portfolios estimated by DCC and DECO-GARCH methods are improved by extending calibration period from three years to four years and lowering rolling window term from one week to one day, while the performances of other GMV portfolios decrease. It shows the effect of time varying variance and dynamic correlations on portfolio optimization at Turkish stock market.
Keywords: DCC-GARCH, DECO-GARCH, GMV portfolio, Sample Covariance, Constant Correlation
Improving Portfolio Optimization by Dynamic Correlations
Posted by Serhat Yucel, FRM on July 28, 2011
a paper on linearization by our Ph.D Proffesor Gazanfer UNAL and his collegue Masood KHALIQUE
Abstract:It is shown that invertible linearizing transformations of the one-dimensional Ito stochastic differential equations cast the associated Fokker-Planck equation to the heat equation. This leads to the time-dependent exact solutions to the Fokker-Planckequations via inverse transformations. To obtain the linearizing transformations ofthe Itˆo stochastic differential equation we have extended Gard’s theorem to the time dependent case.
exact lineraziton of fokker planck
Posted by Emre Ozcan on July 26, 2011
By Thomas M. Idzorek
Abstract:The Black-Litterman model enables investors to combine their unique views
regarding the performance of various assets with the market equilibrium in a manner that
results in intuitive, diversified portfolios. This paper consolidates insights from the
relatively few works on the model and provides step-by-step instructions that enable the
reader to implement this complex model. A new method for controlling the tilts and the
final portfolio weights caused by views is introduced. The new method asserts that the
magnitude of the tilts should be controlled by the user-specified confidence level based
on an intuitive 0% to 100% confidence level. This is an intuitive technique for specifying
one of most abstract mathematical parameters of the Black-Litterman model.
As I notice that incorporating views of portfolio managers into the model seems quite complex. This article gives a clear idea how to incorporate portfolio managers views into Black-Litterman model.
A STEP-BY-STEP GUIDE TO THE BLACK-LITTERMAN MODEL Incorporating user-specified confidence levels