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Quantitative Finance Collector is a blog on Quantitative finance analysis, financial engineering methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management. Random thoughts on financial markets and personal staff are posted at the sub personal blog.

Jan 6
This week-in-review list is longer than usual since it actually covers over two weeks readings. Back to work from holiday, cheers up.

Quantpedia: The Encyclopedia of Trading Systems - turn academic research into financial profit.

PortfolioProbe: Blog year 2011 in review.

Portfolio optimization using forward-looking information: A minimum-variance strategy based on price information from a cross-section of plain-vanilla options consistently outperforms a wide range of benchmark strategies.

The Most General Methodology to Create a Valid Correlation Matrix for Risk Management and Option Pricing Purposes:  two simple methods to produce a feasible (i.e. real, symmetric, and positivesemidefinite) correlation matrix when the econometric one is either noisy, unavailable, or inappropriate.

Forecasting with Option Implied Information: surveys the methods available for extracting forward-looking information from option prices.

Machine Learning: enroll an online class of machine learning for free.

Collusion and CDS Dealer Volume: roughly 76-82% of all single name credit default swaps are trades between Bill Smith at Goldman Sachs and John Smith at JPMorgan or other dealer firms, should an investor take these traded prices as meaningful information?

The top 7 portfolio optimization problems: an excellent list of top 7 optimization problems we often meet and possible way to solve them.

A youtube video showing how to calculate Value at Risk of put options:
Dec 21
Like other conference, the last day of the 24th Australasian Finance & Banking Conference witnessed fewer attendance and less active discussion: people have left or eager to leave. Fortunately or unfortunately, my session was in the afternoon and had even fewer audiences.

Entropic Least-Squares Valuation of American Options Subject to Moment Constraints: improvement of pricing accuracy of American options by incorporating a set of risk-neutral moment constraints into an entropic pricing framework.

Forecasting Equicorrelations: We study the out-of-sample forecasting performance of several time-series models of equicorrelation, which is the average pairwise correlation between a number of assets.

Integrated Framework for Portfolio Risk Management: Various risk measures are managed in a unique integrated framework for portfolio selection problems.

Information Asymmetry and Momentum Anomalies: In this paper, we construct an information asymmetry factor (VECINF) based on the price discovery of large trades. VECINF is significantly negatively correlated with market excess return, indicating that market-wide information asymmetry is lower in bull markets.

Why Did Some Banks Perform Better During the Credit Crisis?: thoughtful question and investigation.

Volatility, Correlation, and Spread ETFs as Factors: Several methods for measuring factors have been investigated in previous literature, but an easy-to-implement general method is simply to specify a group of heterogeneous indexes or traded portfolios.

That's the end of this conference, hopefully you have found some interesting articles as I did, enjoy them.

Nov 10
Long ago I shared a post for nearest correlation matrix calculation, with the main aim to compute the nearest correlation matrix to an approximate correlation matrix when i.e. the correlation matrix is not positive semidefinite. However, the Matlab codes in that post requires a call of C++ function, specifically, eig_mex(), which brings a problem for some users.

Therefore I re-introduce a Matlab-only file for nearest correlation matrix in case you are interested @ http://www.math.nus.edu.sg/~matsundf/#Codes
Oct 24
Correlation matrix exists almost everywhere for derivative pricing and risk management, especially when Monte Carlo simulation is applied, for instance, to simulate correlated random numbers via Cholesky decomposition of correlation matrix. However, one strong requirement of Cholseky decomposition on correlation matrix is positive semi-definite, in other words, eigenvalues must be positive. Another example of positive semi-definite correlation matrix requirement is for risk management measurement, otherwise the volatility calculated might be negative, which is non-acceptable.

In practice, sometimes we need to change correlation matrix to our forecasting values, even minor change might lead to invalid matrix, for this problem, http://www.maths.manchester.ac.uk/~nareports/narep369.pdf details the way to overcome it, accompanying Matlab code can also be found at http://www.maths.manchester.ac.uk/~clucas/near_cor.m and http://www.maths.manchester.ac.uk/~clucas/eig_mex.c.
Sep 24
Rank reduction is useful for multi-factor derivative pricing and risk analysis, for instance, for a Bermudan swaption, Major, MajorW and MajorPower are MATLAB templates that may be used to find a low-rank correlation matrix locally nearest to a given correlation matrix, by means of majorization. Major implements equal weights on the entries of the correlation matrix. MajorW implements non-constant weights.

For an introductory of Rank reduction of correlation matrices by majorization paper can be downloaded at http://www.pietersz.org/majorization.pdf, with Matlab codes
http://www.pietersz.org/major.htm
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