Blog Archives

garch and the Algorithmic Trading Conference

December 10, 2012
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garch and the Algorithmic Trading Conference

The Imperial College Algorithmic Trading Conference was Saturday. Talks Massoud Mussavian Massoud gave a great talk on “Algo Evolution”.  It started with a historical review of how trading used to be done “by hand”.  It culminated in a phylogenetic tree of trading algorithms.  There was an herbivore branch and a carnivore branch. Robert Macrae Robert … Continue reading...

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Again with variability of long-short decile tests

December 6, 2012
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Again with variability of long-short decile tests

A simpler approach to producing the variability. Previously The post “Variability in long-short decile strategy tests” proposed a way of assessing the variability of strategy tests in which a long-short portfolio is created by equally weighting the top and bottom deciles. Improved idea Joe Mezrich suggests maintaining equal weights but bootstrapping the assets within the … Continue reading...

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Variability in long-short decile strategy tests

December 3, 2012
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Variability in long-short decile strategy tests

How to capture return variability when testing strategies with long-short deciles. Traditional practice Question: Does variable X have predictive power for our universe of assets? A common scheme of quants to answer the question is to form a series of portfolios over time.  The portfolio at each time point: is long the equal weighting of … Continue reading...

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Discovering the quality of portfolio decisions

November 26, 2012
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Discovering the quality of portfolio decisions

Performance analysis of an example portfolio. The portfolio We explore a particular portfolio during 2007.  It invests in S&P 500 stocks and starts the year with a value of $10 million.  Initially there are 50 names in the portfolio.  It also ends the year with 50 names but has up to 53 names during the … Continue reading...

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The estimation of Value at Risk and Expected Shortfall

November 19, 2012
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The estimation of Value at Risk and Expected Shortfall

An introduction to estimating Value at Risk and Expected Shortfall, and some hints for doing it with R. Previously “The basics of Value at Risk and Expected Shortfall” provides an introduction to the subject. Starting ingredients Value at Risk (VaR) and Expected Shortfall (ES) are always about a portfolio. There are two basic ingredients that … Continue reading...

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The guts of a statistical factor model

November 12, 2012
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The guts of a statistical factor model

Specifics of statistical factor models and of a particular implementation of them. Previously Posts that are background for this one include: Three things factor models do Factor models of variance in finance The BurStFin R package The quality of variance matrix estimation The problem Someone asked me some questions about the statistical factor model in … Continue reading...

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An easy mistake with returns

November 5, 2012
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An easy mistake with returns

When aggregating over both time and assets, the order of aggregation matters. Task We have the weights for a portfolio and we want to use those and a matrix of returns over time to compute the (long-term) portfolio return. “A tale of two returns” tells us that aggregation over time is easiest to do in … Continue reading...

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Volatility from daily or monthly: garch evidence

October 29, 2012
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Volatility from daily or monthly: garch evidence

Should you use daily or monthly returns to estimate volatility? Does garch explain why volatility estimated with daily data tends to be bigger than if it is estimated with monthly data? Previously There are a number of previous posts — with the variance compression tag — that discuss the phenomenon of volatility estimated with daily … Continue reading...

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The basics of Value at Risk and Expected Shortfall

October 23, 2012
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The basics of Value at Risk and Expected Shortfall

Value at Risk and Expected Shortfall are common risk measures.  Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of … Continue reading...

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Review of “R For Dummies”

October 15, 2012
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Review of “R For Dummies”

The authors are Andrie de Vries and Joris Meys. Executive summary Pretty much all I’d hoped for — and I had high hopes. Significance The “Dummies” series is popular for introducing specific topics in an inviting way. R For Dummies is a worthy addition to the pack. There is a competitor by the name of … Continue reading...

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