Category Archives: Quant finance
Maximum weight of the low vol cohorts
Maximum weight was constrained to 4% at the start of 2007, how does that grow when unhindered? Previously “Low (and high) volatility strategy effects” created 6 sets of random portfolios as of 2007 and showed their performance up to about a month ago. “Rebalancing the low vol cohorts” looked at how much turnover was required … Continue reading
Posted in Quant finance, Random portfolios
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Rebalancing the low vol cohorts
How much turnover is required to get portfolios back to their constraints? Previously “Low (and high) volatility strategy effects” created 6 sets of random portfolios as of 2007 and showed their performance up to about a month ago. This post explores how much turnover it takes to get the portfolios to obey their constraints at … Continue reading
Posted in Portfolio Probe, Quant finance, Random portfolios
Tagged rebalancing, turnover
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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
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
Posted in Quant finance, R language, Random portfolios
Tagged beta in finance, low volatility investing
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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
Posted in Quant finance, R language
Tagged Ledoit-Wolf shrinkage, statistical factor model, variance matrix
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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
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
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
Posted in Quant finance, R language
Tagged efficient frontier, Ledoit-Wolf shrinkage, MACD, S&P 500
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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
Posted in Quant finance, R language
Tagged BurStFin, Ledoit-Wolf shrinkage, statistical factor model, variance estimation
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A slice of S&P 500 kurtosis history
How fat tailed are returns, and how does it change over time? Previously The sister post of this one is “A slice of S&P 500 skewness history”. Orientation The word “kurtosis” is a bit weird. The original idea was of peakedness — how peaked is the distribution at the center. That’s what we can see, … Continue reading
