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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 ... [Read more...]

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 ... [Read more...]

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 … ... [Read more...]

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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 ... [Read more...]

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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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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Which sectors are coherent, and which aren’t? Previously The post “S&P 500 correlations up to date” looked at rolling mean correlations among stocks.  In particular it looked at rolling mean correlations of stocks within sectors. Of importance to this post is that the sectors used are taken from Wikipedia. ... [Read more...]

I haven’t heard much about correlation lately.  I was curious about what it’s been doing. Data The dataset is daily log returns on 464 large cap US stocks from the start of 2006 to 2012 October 5. The sector data were taken from Wikipedia. The correlation calculated here is the mean correlation ... [Read more...]

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What is variance targeting in garch estimation?  And what is its effect? Previously Related posts are: A practical introduction to garch modeling Variability of garch estimates garch estimation on impossibly long series The last two of these show the variability of garch estimates on simulated series where we know the ... [Read more...]

The variability of garch estimates when the series has 100,000 returns. Experiment The post “Variability of garch estimates” showed estimates of 1000 series that were each 2000 observations long.  Here we do the same thing except that the series each have 100,000 observations. That would be four centuries of daily data.  It’s not ... [Read more...]

Not exactly pin-point accuracy. Previously Two related posts are: A practical introduction to garch modeling garch and long tails Experiment 1000 simulated return series were generated.  The garch(1,1) parameters were alpha=.07, beta=.925, omega=.01.  The asymptotic variance for this model is 2.  The half-life is about 138 days. The simulated series used a Student’... [Read more...]

Previously This book and the associated R package were introduced before. Executive Summary A very nice — and enlightening — discussion of a wide range of topics. Principles The Introduction to the book sets out 5 principles.  This is probably the most important part of the book.  The principles are: We don’t ... [Read more...]

The paper is “Not Fooled by Randomness: Using Random Portfolios to Analyze Investment Funds” by Roberto Stein.  Here is an explanation of the idea of random portfolios. Favorite sentence The real question here is whether we’re actually measuring skill, or these are still measures of performance, so influenced by ... [Read more...]