Category Archives: R language
How to search the R-sig-finance archives
A not unusual part of a response on the R-sig-finance mailing list is: “Search the list archives.” In principle that makes sense. In practice it might not be clear what to do. Now it should be. The list The R-sig-finance mailing list deals with the intersection of questions about the R language and finance. It … Continue reading
A slice of S&P 500 skewness history
How symmetric are the returns of the S&P 500? How does the skewness change over time? Previously We looked at the predictability of kurtosis and skewness in S&P constituents. We didn’t see any predictability of skewness among the constituents. Here we look at skewness from a different angle. The data Daily log returns of the … Continue reading
Physical books of “The R Inferno” and “S Poetry”
Hardcopy versions of both The R Inferno and S Poetry are now available for sale. Physical economy Buy The R Inferno (the version dated 2011 April 30) Buy S Poetry Discount The publisher, Lulu, has a coupon for a 25% discount off purchases (up to a maximum of $50) that is good until the … Continue reading
Posted in R language
Tagged R abnormalities, R absurdities, R anomalies, R oddities, R peculiarities, R quirks, R trouble spots, S Poetry, The R Inferno
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Sensitivity of risk parity to variance differences
Equal risk contribution of assets determines the asset weights given the variance matrix. How sensitive are those weights to the variance estimate? Previously The post “Risk parity” gave an overview of the idea. In particular it distinguished the cases: the assets have equal risk contribution groups of assets have equal risk contribution A key difference … Continue reading
Posted in Quant finance, R language
Tagged equal risk contribution, factor model, Ledoit-Wolf shrinkage, risk parity
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The top 7 portfolio optimization problems
Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements). If you are … Continue reading
Market predictions for years 2011 and 2012
A review of market predictions and results for 2011, and a calibration for 2012 predictions (of 19 equity indices plus oil). Previously One year ago the post “Revised market prediction distributions” presented plots showing the variability of various markets assuming no market-moving forces. The follow-up post “Some market predictions enhanced some of those plots with … Continue reading
Posted in Quant finance, R language
Tagged 2011 market prediction, 2012 market prediction, market prediction
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R-specific review of blog year 2011
Most popular posts Two of the ten most popular posts during the year were completely about R: The R Inferno revised (number 6) Solve your R problems (number 9) R played a role in the other eight top ten, and many of the rest of the posts as well. R The R Inferno was revised … Continue reading
Volatility estimation and time-adjusted returns
Do non-trading days explain the mystery of volatility estimation? Previously The post “The volatility mystery continues” showed that volatility estimated with daily data tends to be larger (in recent years) than when estimated with lower frequency returns. Time adjusting One of the comments — from Joseph Wilson — was that there is a problem with … Continue reading
Posted in Quant finance, R language
Tagged S&P 500, time-adjusted returns, variance compression, volatility
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LondonR recap
The biggest and perhaps best meeting yet. The talks James Long: “Easy Parallel Stochastic Simulations using Amazon’s EC2 & Segue”. This was a lively talk about James’ package to use Amazon’s cloud to speed up a (huge) call to lapply. The good part is that if you want to use Amazon as your cloud provider, … Continue reading
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The volatility mystery continues
How do volatility estimates based on monthly versus daily returns differ? Previously The post “The mystery of volatility estimates from daily versus monthly returns” and its offspring “Another look at autocorrelation in the S&P 500” discussed what appears to be an anomaly in the estimation of volatility from daily versus monthly data. In recent times … Continue reading
Posted in Quant finance, R language
Tagged autocorrelation, S&P 500, variance compression, volatility
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