Category Archives: R language

Reminiscences of R in Finance 2016

When I announced R in Finance 2016 I talked about 2 days of conference and 50 speakers.  I missed out the 3 days of sleep deprivation. But a pleasant 3 days of sleep deprivation it was — seeing old friends and making new ones. I’m not sure that Mother Mary believed me that in our … Continue reading

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R in Finance and other events

Highlighted R in Finance 2016 May 20-21, Chicago. 2 days, limited space, 50 speakers, including: Pat Burns on “Some Linguistics of Quantitative Finance” Abstract: How can the abstract be written for a talk with an ambiguous and possibly misleading title without itself being vague and misleading? I don’t know, but perhaps: A quest to discover how markets work … Continue reading

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Effective risk management with R

Conference The first EARL Conference (Effective Applications of the R Language) was held 2014 September 15-17 in London. Talk My talk was “Effective risk management with R” (annotated slides). Instability hypothesis When I was preparing for the talk, one of my ideas was to show the Google trend for searches for Minsky’s instability hypothesis.  I … Continue reading

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EARL and other upcoming events

Highlighted EARL As in “Effective Applications of the R Language”. 2014 September 15-17, London. Somehow they gave higher billing to Ben Goldacre than to Pat Burns.  If Obama were coming, they’d probably bill him above me too — and what does he know about R?  In spite of that little glitch, I’m sure it will … Continue reading

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BurStFin R package version 1.02 released

More efficiency and an additional function in the new version on CRAN. Variance estimation The major functionality in the package is variance estimation: Ledoit-Wolf shrinkage via var.shrink.eqcor statistical factor model (principal components) via factor.model.stat There have been a number of previous blog posts on both factor models and Ledoit-Wolf shrinkage. Positive-definiteness The default value of … Continue reading

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A complicated answer to a simple correlation question

A data analysis surprise party. Simple question If I have correlation matrices each estimated with a month of daily returns, how much worse is the average of six of those compared to the estimate with six months of daily data? Expected answer Do a statistical bootstrap with the returns and compare the standard deviations across … Continue reading

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What is volatility?

Some facts and some speculation. Definition Volatility is the annualized standard deviation of returns — it is often expressed in percent. A volatility of 20 means that there is about a one-third probability that an asset’s price a year from now will have fallen or risen by more than 20% from its present value. In … Continue reading

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garch models caught in the spotlight

An attempt to clarify the basics. Previously There have been several posts about garch.  In particular: A practical introduction to garch modeling The components garch model in the rugarch package Genesis A reader emailed me because he was confused about the workings of garch in general, and simulation with the empirical distribution in particular. If … Continue reading

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S&P that might have been

The S&P 500 returned 29.6% in 2013.  How might that have varied? S&P weights There are many features that could vary — here we will keep the same constituents (almost) and weights with similar sizes but that are randomly assigned rather than based on market capitalization. That is, we want the large weights of our … Continue reading

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Blog year 2013 in review

Highlights of the blog over the past year. Most popular posts The posts with the most hits during the year. A practical introduction to garch modeling (posted in 2012) A tale of two returns (posted in 2010) The top 7 portfolio optimization problems (posted in 2012) The number 1 novice quant mistake (posted in 2011) On smart beta … Continue reading

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