Posts Tagged ‘ Quant finance ’

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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S&P 500 sector strengths

October 10, 2012
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S&P 500 sector strengths

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. Relative correlations The thought is that … Continue reading...

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S&P 500 correlations up to date

October 8, 2012
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S&P 500 correlations up to date

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 of stocks among … Continue reading...

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How to add a benchmark to a variance matrix

October 1, 2012
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How to add a benchmark to a variance matrix

There is a good way and a bad way to add a benchmark to a variance matrix that will be used for optimization and similar operations.  Our examination sheds a little light on the process of variance matrix estimation in this realm. Role of benchmarks Investing Benchmarks are common in investment management.  It’s my opinion … Continue reading...

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Variance targeting in garch estimation

September 24, 2012
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Variance targeting in garch estimation

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 right answer.  In response to … Continue reading...

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garch estimation on impossibly long series

September 20, 2012
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garch estimation on impossibly long series

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 presently feasible … Continue reading...

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Variability of garch estimates

September 17, 2012
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Variability of garch estimates

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’s t distribution … Continue reading...

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Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann

September 12, 2012
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Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann

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 know much in … Continue reading...

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