Quantitative Finance Collector is a blog on Quantitative finance analysis, methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management.
Jan
13
The Top 25 Best Performing Hedge Funds In The World: the top 25 large (more than $1 billion AUM) hedge fund performers around the world, with strategy from macro, long/short to multi.
2011 IAFE/SunGard Financial Engineer of the Year: Robert Engle Selected as the Recipient of the 2011 IAFE/SunGard Financial Engineer of the Year Award.
A "Ratings Neutral" Investment Policy: lessors learned from the current financial crisis.
Research Seminar: Optimization: learn optimization online, free ebooks downloadable.
How to Identify and Predict Bull and Bear Markets?: We compare methods based on rules with methods based on econometric models, in particular Markov regime-switching models.
2011 IAFE/SunGard Financial Engineer of the Year: Robert Engle Selected as the Recipient of the 2011 IAFE/SunGard Financial Engineer of the Year Award.
A "Ratings Neutral" Investment Policy: lessors learned from the current financial crisis.
Research Seminar: Optimization: learn optimization online, free ebooks downloadable.
How to Identify and Predict Bull and Bear Markets?: We compare methods based on rules with methods based on econometric models, in particular Markov regime-switching models.
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:
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
7
My flight to Australia will be tomorrow, so this post is the one ahead of schedule.
mlpy - Machine Learning Python: mlpy is a free Python module for Machine Learning. It facilitates classification, regression, clustering and feature selection in Python.
Global Optimization Algorithms – Theory and Application: a free ebook on global optimization, algorithms including Evolutionary Algorithms, Genetic Algorithms, Genetic Programming, Learning Classifier Systems, Hill Climbing, Simulated Annealing... Not easy to understand but worth to save it.
Improved Moving Average Code, and Improved Moving Average?: both posts are aiming to introduce a newly improved moving average trading strategy with detailed codes and examples.
How Expected Shortfall Can Simplify the Equally-Weighted Risk Contribution Portfolio: compare the performance of portfolios under several construction strategies: minimum variance, equally weighted, and Expected Shortfall stable.
mlpy - Machine Learning Python: mlpy is a free Python module for Machine Learning. It facilitates classification, regression, clustering and feature selection in Python.
Global Optimization Algorithms – Theory and Application: a free ebook on global optimization, algorithms including Evolutionary Algorithms, Genetic Algorithms, Genetic Programming, Learning Classifier Systems, Hill Climbing, Simulated Annealing... Not easy to understand but worth to save it.
Improved Moving Average Code, and Improved Moving Average?: both posts are aiming to introduce a newly improved moving average trading strategy with detailed codes and examples.
How Expected Shortfall Can Simplify the Equally-Weighted Risk Contribution Portfolio: compare the performance of portfolios under several construction strategies: minimum variance, equally weighted, and Expected Shortfall stable.
Nov
18
Resampling and Shrinkage : Solutions to Instability of mean-variance efficient portfolios: we know mean-variance portfolio highly depends on the input of expected return and covariance matrix, a post demonstrates with full R codes two common techniques to make portfolios in the mean-variance efficient frontier more diversified and immune to small changes in the input assumptions.
Improving Portfolio Selection Using Option-Implied Volatility and Skewness: is option-implied information useful for improving the out-of-sample performance of a mean-variance efficient equity portfolio? this paper tells you an answer.
SDE Matlab Toolbox: a nice Matlab toolbox for simulation and estimation of stochastic differential equations, it supports both univariate and multivariate SDEs.
Black-Litterman Model: Black-Litterman model is used to overcome a few shortcomings of Markowitz efficient frontier method, here is a post with full R codes demonstrating how to implement Black-Litterman model.
Using Neural Network For Regression: compare the performance of Artificial Neural Network (ANN) and OLS for a simple linear regression. Not surprisingly, ANN wins.
Improving Portfolio Selection Using Option-Implied Volatility and Skewness: is option-implied information useful for improving the out-of-sample performance of a mean-variance efficient equity portfolio? this paper tells you an answer.
SDE Matlab Toolbox: a nice Matlab toolbox for simulation and estimation of stochastic differential equations, it supports both univariate and multivariate SDEs.
Black-Litterman Model: Black-Litterman model is used to overcome a few shortcomings of Markowitz efficient frontier method, here is a post with full R codes demonstrating how to implement Black-Litterman model.
Using Neural Network For Regression: compare the performance of Artificial Neural Network (ANN) and OLS for a simple linear regression. Not surprisingly, ANN wins.
Jun
3
Constructing 130/30-Portfolios with the Omega ratio is an interesting paper forthcoming in Journal of Asset Management by Gilli, Manfred, Schumann, Enrico, Di Tollo, Giacomo and Cabej, Gerda. Typical portfolio construction theory uses Markowitz efficient frontier under mean-variance framework to find an optimized portfolio, the authors in this paper construct portfolios with an alternative selection criterion, the Omega function.
Any portfolio return r can be decomposed into
define the downside and upside partial moments as follows
our objective is to minimize the below Omega ratio, a known performance measure, subject to additional constraints such as long short weights.
Any portfolio return r can be decomposed into
define the downside and upside partial moments as follows
our objective is to minimize the below Omega ratio, a known performance measure, subject to additional constraints such as long short weights.
