Category Archives: Quant finance
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
Posted in Quant finance, R language
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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
The portfolio optimization higher-moment credo
The question of skewness and kurtosis in portfolio optimization. Previously Problem 4 of “The top 7 portfolio optimization problems” concerns the use of higher moments. “Further adventures with higher moments” is the most recent in a series of posts on the efficacy of higher moments in optimization. This set includes the observation that “trade selection” … Continue reading
Posted in Quant finance
Tagged kurtosis, portfolio optimisation, portfolio optimization, skewness, trade selection
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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
Further adventures with higher moments
Additional views of the stability of skewness and kurtosis of equity portfolios. Previously A post called “Four moments of portfolios” introduced the idea of looking at the stability of the mean, variance, skewness and kurtosis of portfolios through time. That post gave birth to a presentation at the London Quant Group. That talk gave birth … Continue reading
Another tale of two returns
The further adventures of returns on short positions. Previously There are three posts that are instructive about returns: A tale of two returns Returns with negative net asset values An easy mistake with returns There is also a (satirical) post on the statistical distribution of returns: “The distribution of financial returns made simple”. Scenarios You … Continue reading
The efficacy of higher moments in portfolio optimization
On Monday I gave a talk at the London Quant Group entitled “Exploring the efficacy of higher moments in portfolio optimisation”. A substantial number of people showed up, and they taught me quite a lot about the subject. So it seems to have been successful. There are now annotated slides available. The slides point towards … Continue reading
Posted in Quant finance
Tagged kurtosis, portfolio optimisation, portfolio optimization, skewness
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Quant finance blogs
What I’ve learned from updating the blogroll. New entries The easy option is to go to The Whole Street which aggregates lots of quant finance blogs. Somehow Bookstaber missed out being on the blogroll before — definitely an oversight. Timely Portfolio was another that I was surprised wasn’t already there. The R Trader talks about … Continue reading
Posted in Quant finance, R language
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Four moments of portfolios
What good are the skewness and kurtosis of portfolios? Previously The post “Cross-sectional skewness and kurtosis: stocks and portfolios” looked at skewness and kurtosis in portfolios. The key difference between that post and this one is what distribution is being looked at. The previous post specified a single time and looked at the distribution across … Continue reading
