Category Archives: Quant finance
Again with variability of long-short decile tests
A simpler approach to producing the variability. Previously The post “Variability in long-short decile strategy tests” proposed a way of assessing the variability of strategy tests in which a long-short portfolio is created by equally weighting the top and bottom deciles. Improved idea Joe Mezrich suggests maintaining equal weights but bootstrapping the assets within the … Continue reading
Variability in long-short decile strategy tests
How to capture return variability when testing strategies with long-short deciles. Traditional practice Question: Does variable X have predictive power for our universe of assets? A common scheme of quants to answer the question is to form a series of portfolios over time. The portfolio at each time point: is long the equal weighting of … Continue reading
The guts of a statistical factor model
Specifics of statistical factor models and of a particular implementation of them. Previously Posts that are background for this one include: Three things factor models do Factor models of variance in finance The BurStFin R package The quality of variance matrix estimation The problem Someone asked me some questions about the statistical factor model in … Continue reading
An easy mistake with returns
When aggregating over both time and assets, the order of aggregation matters. Task We have the weights for a portfolio and we want to use those and a matrix of returns over time to compute the (long-term) portfolio return. “A tale of two returns” tells us that aggregation over time is easiest to do in … Continue reading
Posted in Quant finance, R language
Tagged log return, log return vs simple return, simple return
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Volatility from daily or monthly: garch evidence
Should you use daily or monthly returns to estimate volatility? Does garch explain why volatility estimated with daily data tends to be bigger than if it is estimated with monthly data? Previously There are a number of previous posts — with the variance compression tag — that discuss the phenomenon of volatility estimated with daily … Continue reading
Posted in Quant finance, R language
Tagged garch simulation, variance compression, volatility
6 Comments
S&P 500 sector strengths
Which sectors are coherent, and which aren’t? Previously The post “S&P 500 correlations up to date” looked at rolling mean correlations among stocks. In particular it looked at rolling mean correlations of stocks within sectors. Of importance to this post is that the sectors used are taken from Wikipedia. Relative correlations The thought is that … Continue reading
S&P 500 correlations up to date
I haven’t heard much about correlation lately. I was curious about what it’s been doing. Data The dataset is daily log returns on 464 large cap US stocks from the start of 2006 to 2012 October 5. The sector data were taken from Wikipedia. The correlation calculated here is the mean correlation of stocks among … Continue reading
How to add a benchmark to a variance matrix
There is a good way and a bad way to add a benchmark to a variance matrix that will be used for optimization and similar operations. Our examination sheds a little light on the process of variance matrix estimation in this realm. Role of benchmarks Investing Benchmarks are common in investment management. It’s my opinion … Continue reading
Posted in Quant finance, R language
Tagged benchmark, Ledoit-Wolf shrinkage, statistical factor model, variance matrix
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Variance targeting in garch estimation
What is variance targeting in garch estimation? And what is its effect? Previously Related posts are: A practical introduction to garch modeling Variability of garch estimates garch estimation on impossibly long series The last two of these show the variability of garch estimates on simulated series where we know the right answer. In response to … Continue reading
garch estimation on impossibly long series
The variability of garch estimates when the series has 100,000 returns. Experiment The post “Variability of garch estimates” showed estimates of 1000 series that were each 2000 observations long. Here we do the same thing except that the series each have 100,000 observations. That would be four centuries of daily data. It’s not presently feasible … Continue reading
