Posts Tagged ‘ Quant finance ’

Cross-sectional skewness and kurtosis: stocks and portfolios

April 30, 2012
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Cross-sectional skewness and kurtosis: stocks and portfolios

Not quite expected behavior of skewness and kurtosis. The question In each time period the returns of a universe of stocks will have some distribution — distributions as displayed in “Replacing market indices” and Figure 1. Figure 1: A cross-sectional distribution of simple returns of stocks. In particular they will have values for skewness and … Continue reading...

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A variance campaign that failed

April 23, 2012
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A variance campaign that failed

they ought at least be allowed to state why they didn’t do anything and also to explain the process by which they didn’t do anything. First blush One of the nice things about R is that new statistical techniques fall into it.  One such is the glasso (related to the statistical lasso) which converts degenerate … Continue reading...

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Betas of the low vol cohorts

April 4, 2012
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Betas of the low vol cohorts

How did the constraints affect portfolio betas, and how did the betas change over time? Previously “Low (and high) volatility strategy effects” created 6 sets of random portfolios — the so-called low vol cohorts — as of 2007 and showed their performance up to about a month ago. “Rebalancing the low vol cohorts” looked at … Continue reading...

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Beta is not volatility

March 26, 2012
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Beta is not volatility

The missing link between beta and volatility is correlation. Previously “4 and a half myths about beta in finance” attempted to dislodge several myths about beta, including that beta is about volatility. “Low (and high) volatility strategy effects” showed a plot of beta versus volatility for stocks in the S&P 500 for estimates from 2006.  … Continue reading...

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Low (and high) volatility strategy effects

March 23, 2012
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Low (and high) volatility strategy effects

Does minimum variance act differently from low volatility?  Do either of them act like low beta?  What about high volatility versus high beta? Inspiration Falkenblog had a post investigating differences in results when using different strategies for low volatility investing.  Here we look not at a single portfolio of a given strategy over time, but … Continue reading...

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The quality of variance matrix estimation

March 12, 2012
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The quality of variance matrix estimation

A bit of testing of the estimation of the variance matrix for S&P 500 stocks in 2011. Previously There was a plot in “Realized efficient frontiers” showing the realized volatility in 2011 versus a prediction of volatility at the beginning of the year for a set of random portfolios.  A reader commented to me privately … Continue reading...

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The shadows and light of models

March 5, 2012
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The shadows and light of models

How wide is the darkness? Uses of models The main way models are used is to: shine light on the “truth” We create and use a model to learn how some part of the world works. But there is a another use of models that is unfortunately rare — a use that should be common … Continue reading...

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A minimum variance portfolio in 2011

February 29, 2012
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A minimum variance portfolio in 2011

2011 was a good vintage for minimum variance, at least among stocks in the S&P 500. Previously The post “Realized efficient frontiers” included, of course, a minimum variance portfolio.  That portfolio seemed interesting enough to explore some more. “What does ‘passive investing’ really mean” suggests that minimum variance should be considered a form of passive … Continue reading...

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Realized efficient frontiers

February 27, 2012
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Realized efficient frontiers

A look at the distortion from predicted to realized. The idea The efficient frontier is a mainstay of academic quant.  I’ve made fun of it before.  This post explores the efficient frontier in a slightly less snarky fashion. Data The universe is 474 stocks in the S&P 500.  The predictions are made using data from … Continue reading...

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The BurStFin R package

February 16, 2012
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The BurStFin R package

Version 1.01 of BurStFin is now on CRAN. It is written entirely in R, and meant to be compatible with S+. Functionality The package is aimed at quantitative finance, but the variance estimation functions could be of use in other applications as well. Also of general interest is threeDarr which creates a three-dimensional array out … Continue reading...

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