Category Archives: R language
Portfolio tests of predicted returns
Exploring the quality of predictions using random portfolios and optimization. Previously “Simple tests of predicted returns” showed a few ways to look at expected returns at the asset level. Here we move to the portfolio level. The previous post focused on correlation. Win Vector Blog points out that gauging prediction quality using correlation can be … Continue reading
Posted in Quant finance, R language, Random portfolios
Tagged alpha generation, MACD, S&P 500
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Simple tests of predicted returns
Some ways to explore how good a method of predicting returns is. Data and model The universe is 443 large cap US stocks that have data back to the beginning of 2004. The daily (adjusted) close was used. The model that is used as an example is the default signal from the MACD function of … Continue reading
R for finance and other upcoming events
Featured R for Finance Workshop 2013 March 5-6 in London. The target audience are professionals and academics, who wish to learn the basics of the statistical software R and its use in Finance. The workshop is led by Ron Hochreiter, Pat Burns and Michael Sun. Details are on the Unicom website. Please reference Burns Statistics … Continue reading
Posted in Events, R language
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Variability of predicted portfolio volatility
A prediction of a portfolio’s volatility is an estimate — how variable is that estimate? Data The universe is 453 large cap US stocks. The variance matrices are estimated with the daily returns in 2012. Variance estimation was done with Ledoit-Wolf shrinkage (shrinking towards equal correlation). Two sets of random portfolios were created. In both … Continue reading
Posted in Quant finance, R language
Tagged Ledoit-Wolf shrinkage, statistical bootstrap, variance matrix
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An infelicity with Value at Risk
More risk does not necessarily mean bigger Value at Risk. Previously “The incoherence of risk coherence” suggested that the failure of Value at Risk (VaR) to be coherent is of little practical importance. Here we look at an attribute that is not a part of the definition of coherence yet is a desirable quality. Thought … Continue reading
Posted in R language, Risk
Tagged Conditional Value at Risk, Expected Shortfall, Value at Risk
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The components garch model in the rugarch package
How to fit and use the components model. Previously Related posts are: A practical introduction to garch modeling Variability of garch estimates garch estimation on impossibly long series Variance targeting in garch estimation The model The components model (created by Engle and Lee) generally works better than the more common garch(1,1) model. Some hints about … Continue reading
Clustering and sector strength
An exploration of the usefulness of sectors. Previously This subject was discussed in “S&P 500 sector strengths”. Idea Stocks are put into groups based on the sector that the company is considered to be in. Cluster analysis is a statistical technique that finds groups. If sectors really move together, then clustering should recover sectors. Will … Continue reading
Market predictions for year 2013
Calibrations of 2013 predictions for 18 equity indices — plus some publicly available predictions. Orientation The distributions are an attempt to see the variability if there were no market-driving news for the whole year. Another way of thinking: mentally moving the distribution to center on a prediction gives a sense of the variability of results … Continue reading
Miles of iles
An explanation of quartiles, quintiles deciles, and boxplots. Previously “Again with variability of long-short decile tests” and its predecessor discusses using deciles but doesn’t say what they are. The *iles These are concepts that have to do with approximately equally sized groups created from sorted data. There are 4 groups with quartiles, 5 with quintiles … Continue reading
Posted in Quant finance, R language
Tagged boxplot, decile, interquartile range, IQR, quartile, quintile
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A look at historical Value at Risk
Historical Value at Risk (VaR) is very popular because it is easy and intuitive: use the empirical distribution of some specific number of past returns for the portfolio. Previously “The estimation of Value at Risk and Expected Shortfall” included an R function to estimate historical VaR. Generating portfolios A useful tool to explore risk models … Continue reading
