Category Archives: R language
The scaling of Expected Shortfall
Getting Expected Shortfall given the standard deviation or Value at Risk. Previously There have been a few posts about Value at Risk and Expected Shortfall. Properties of the stable distribution were discussed. Scaling One way of thinking of Expected Shortfall is that it is just some number times the standard deviation, or some other number … Continue reading
Posted in R language, Risk
Tagged Conditional Value at Risk, Expected Shortfall, stable distribution, Value at Risk
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Introduction to stable distributions for finance
A few basics about the stable distribution. Previously “The distribution of financial returns made simple” satirized ideas about the statistical distribution of returns, including the stable distribution. Origin As “A tale of two returns” points out, the log return of a long period of time is the sum of the log returns of the shorter … Continue reading
Posted in Quant finance, R language
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Value at Risk and Expected Shortfall, and other upcoming events
Highlighted Value at Risk and Expected Shortfall A two-day course exploring Value at Risk and Expected Shortfall, and their role in risk management. 2013 June 25 & 26, London. Lead by Patrick Burns. Details at the CFP Events site. New Events Thalesians — San Francisco 2013 June 5. Jesse Davis on “Risk Model Imposed Manager-to-Manager … Continue reading
Posted in Events, R language
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Value at Risk with exponential smoothing
More accurate than historical, simpler than garch. Previously We’ve discussed exponential smoothing in “Exponential decay models”. The same portfolios were submitted to the same sort of analysis in “A look at historical Value at Risk”. Issue Markets experience volatility clustering. As the previous post makes clear, historical VaR suffers dramatically from this. An alternative is … Continue reading
Implied alpha and minimum variance
Under the covers of strange bedfellows. Previously The idea of implied alpha was introduced in “Implied alpha — almost wordless”. In a comment to that post Jeff noticed that the optimal portfolio given for the example is ever so close to the minimum variance portfolio. That is because there is a problem with the example … Continue reading
Posted in Quant finance, R language
Tagged implied alpha, minimum variance portfolio, reverse optimization
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Variance matrix differences
Torturing portfolios to give different volatilities between a factor model and Ledoit-Wolf shrinkage. Previously There have been posts on: “What the hell is a variance matrix?” factor models Ledoit-Wolf shrinkage Question Two of the several ways to produce an estimate of the variance matrix of asset returns is a statistical factor model and Ledoit-Wolf shrinkage. … Continue reading
The half variance approximation for mean returns
What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy that … Continue reading
Slouching towards simulating investment skill
When investment skill is simulated, it is often presented as if it is obvious how to do it. Maybe I’m wrong, but I don’t think it’s obvious. Previously In “Simple tests of predicted returns” we saw that prediction quality need not look like what you would find in a textbook. For example, there was a … Continue reading
garch and the distribution of returns
Using garch to learn a little about the distribution of returns. Previously There are posts on garch — in particular: A practical introduction to garch modeling The components garch model in the rugarch package garch and long tails There has also been discussion of the distribution of returns, including a satire called “The distribution of … Continue reading
Stock-picking opportunity and the ratio of variabilities
How good is the current opportunity to pick stocks relative to the past? Idea The more stocks act differently from each other relative to how volatile they are, the more opportunity there is to benefit by selecting stocks. This post looks at a particular way of investigating that idea. Data Daily (log) returns of 442 … Continue reading
Posted in Quant finance, R language
Tagged opportunity ratio, S&P 500, stock-picking opportunity, volatility
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