Category Archives: R language
2 dimensions of portfolio diversity
Portfolio diversity is a balancing act. Previously The post “Portfolio diversity” talked about the role of the correlation between assets and the portfolio. The current post fills a hole in that post. The 2 dimensions asset-portfolio correlation Each asset in the universe has a correlation with the portfolio. If there are any assets that have … Continue reading
A practical introduction to garch modeling
We look at volatility clustering, and some aspects of modeling it with a univariate GARCH(1,1) model. Volatility clustering Volatility clustering — the phenomenon of there being periods of relative calm and periods of high volatility — is a seemingly universal attribute of market data. There is no universally accepted explanation of it. GARCH (Generalized AutoRegressive … Continue reading
Random portfolios versus Monte Carlo
What is the difference between Monte Carlo — as it is usually defined in finance — and random portfolios? The meaning of “Monte Carlo” The idea of “Monte Carlo” is very simple. It is a fancy word for “simulation”. As usual, it is all too possible to find incredibly muddied explanations of such a simple … Continue reading
Posted in R language, Random portfolios
Tagged Monte Carlo in finance, personal pension planning
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Two new, important books on R
Two books were recently published that are sure to help R grow even faster. R has a reputation, partially deserved, for being hard to learn. These books will help. The first makes learning easier, the second can make learning less necessary for initiates. I have not yet touched either book. R for Dummies The authors … Continue reading
Posted in Book review, R language
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To R or not to R, and other events
New events To R, or not to R, that is the question The Statistical Computing Section of the Royal Statistical Society presents a one-day event on 2012 June 29. The details of the day. See in particular the abstract for “Teaching statistics: a pain in the R?” by Andy Field — it involves a sheepdog … Continue reading
Posted in Events, R language
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Cross sectional spread of stock returns
A look at a simplistic measure of stock-picking opportunity. Motivation The interquartile range (the spread of the middle half of the data) has recently been added to the market portrait plots. Putting those numbers into historical context was the original impulse. However, this led to thinking about change in stock-picking opportunity over time. Data Daily … Continue reading
Posted in Quant finance, R language
Tagged garch, interquartile range, S&P 500, stock-picking opportunity
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Variability in maximum drawdown
Maximum drawdown is blazingly variable. Psychology Probably the most salient feature that an investor notices is the amount lost since the peak: that is, the maximum drawdown. Just because drawdown is noticeable doesn’t mean it is best to notice. Statistics The paper “About the statistics of the maximum drawdown in financial time series” explores drawdown … Continue reading
Inferno-ish R
CambR was nice enough to invite Markus Gesmann and me to speak at their event on Tuesday. My talk was Inferno-ish R. See also The R Inferno. Epilogue Subscribe to the Portfolio Probe blog by Email
Jackknifing portfolio decision returns
A look at return variability for portfolio changes. The problem Suppose we make some change to our portfolio. At a later date we can see if that change was good or bad for the portfolio return. Say, for instance, that it helped by 16 basis points. How do we properly account for variability in that … Continue reading
Posted in Performance, Quant finance, R language
Tagged jackknife, statistical bootstrap
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Correlations and postive-definiteness
On the way to another destination, I found some curious behavior with average correlations. The data Daily log returns from almost all of the constituents of the S&P 500 for years 2006 through 2011. The behavior Figure 1 shows the actual mean correlation among stocks for the set of years and the mean correlation with … Continue reading
Posted in Quant finance, R language
Tagged BurStFin, Ledoit-Wolf shrinkage, positive definite, variance estimation
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