Monthly Archives: October 2011

Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

October 25, 2011
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Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

In the Maximum Loss and Mean-Absolute Deviation risk measures post I started the discussion about alternative risk measures we can use to construct efficient frontier. Another alternative risk measures I want to discuss are Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR). I will use methods presented in Comparative Analysis of Linear Portfolio Rebalancing

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Email Netiquette

October 25, 2011
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Email Netiquette

A short piece of web-scrapping I sent as a reminder to my colleague. If you run it the result should be something like... Datatata!

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Sabermetrics Meets R Meetup

October 25, 2011
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I just ran across this post at Big Computing. On November 14th, there will be an R User meet-up in Washington, DC (Tyson's Corner) led by Mike Driscoll about using R for sabermetric analysis (linked here). I will actually be home in Maryland for a co...

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Pair trading strategy : how to use "PairTrading" package

October 25, 2011
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Pair trading strategy : how to use "PairTrading" package

Mr.Ishikawa(my old friend) and I developed "PairTrading" package, and uploaded it on CRAN.This article shows you how you can use it.The pair trading is a market neutral trading strategy and gives traders a chance to profit regardless of market conditions. The idea of this strategy is quite simple. 1 : Select two stocks(or any assets) moving similarly 2 : Short...

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Approximate Bayesian computational methods on-line

October 25, 2011
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Approximate Bayesian computational methods on-line

Fig. 4 – Boxplots of the evolution of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time

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Machine Learning Ex 5.1 – Regularized Linear Regression

October 25, 2011
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Machine Learning Ex 5.1 – Regularized Linear Regression

The first part of the Exercise 5.1 requires to implement a regularized version of linear regression. Adding regularization parameter can prevent the problem of over-fitting when fitting a high-order polynomial. Read More: 194 Words Totally

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Vanilla C code for the Stochastic Simulation Algorithm

October 24, 2011
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Vanilla C code for the Stochastic Simulation Algorithm

The Gillespie stochastic simulation algorithm (SSA) is the gold standard for simulating state-based stochastic models. If you are a R buff, a SSA novice and want to get quickly up and running stochastic models (in particular ecological models) that are not … Continue reading →

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Simple Heatmap in R with Formula One Dataset

October 24, 2011
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Simple Heatmap in R with Formula One Dataset

Now, that the 2011 F1 season is over I decided to quickly scrub the Formula 1 data of the F1.com website, such as the list of drivers, ordered by the approximate amount of salary driver is getting (top list driver is making the most, approx. 30MM) and position at the end of each race. There

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One week left to enter the $20,000 "Applications of R" contest

October 24, 2011
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One week left to enter the $20,000 "Applications of R" contest

The deadline to enter the "Applications of R in Business" contest is just a week away. To qualify for $20,000 in prizes from Revolution Analytics, your entry must be submitted to inside-r.org by midnight PST on October 31. Note that this doesn't have to be your final submission: as long as you've entered a draft version, you can still...

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Two seasonal investors – R snippet

October 24, 2011
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Two seasonal investors – R snippet

In “A tale of 2 Seasonal Investors“, the Big Picture discusses the simple idea of comparing two simple investment approaches: being exposed to the market 6 months every year (from November to April), as opposed to investing in the other 6 months of every year (from May to October). Going back 50 years in the

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