Monthly Archives: April 2012
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
US market portrait 2012 week 18
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Subscribe to the Portfolio Probe blog by Email
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
US market portrait 2012 week 17
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Subscribe to the Portfolio Probe blog by Email
Low volatility investing and benchmarks
The focus on tracking error rules out a low volatility strategy. Simply put, most money managers are focused on outperforming their benchmarks without adding risk. And because risk is measured on a relative basis, a portfolio that moves up and down less than its benchmark is perceived as more risky on a relative basis because … Continue reading
Information flows like water
Guiding a ship, it takes more than your skill Spark David Rowe’s Risk column this month is about data leverage. The idea is that you are leveraging your data if you are using it to answer questions that are too demanding of information. The piece reminded me of a talk that Dave gave a few … Continue reading
US market portrait 2012 week 16
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Subscribe to the Portfolio Probe blog by Email
Posted in Market portrait
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Three things factor models do
Factor models are heavily used in finance to create variance matrices. Here’s why. Factor models: Provide non-degenerate estimates Save space Quantify sources of risk Non-degenerate estimates First off, what does this mean? The technical term is that you want your estimate of the variance matrix to be positive definite. In practical terms what that means … Continue reading
Posted in Quant finance
Tagged factor model, Ledoit-Wolf shrinkage, statistical factor model
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US market portrait 2012 week 10
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Subscribe to the Portfolio Probe blog by Email
Posted in Market portrait
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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
Posted in Quant finance, R language, Random portfolios
Tagged beta in finance, low volatility investing
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