Author Archives: Pat
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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US market portrait 2013 week 24
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R — you are free … Continue reading
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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US market portrait 2013 week 23
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R — you are free … Continue reading
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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The low volatility anomaly and CAPM
A look at a paper that explores possible assumption failures of CAPM that would explain the low volatility anomaly. Previously We’ve talked about CAPM before, in particular: 4 and a half myths about beta in finance There has also been substantial talk about low volatility investing. The paper The paper is “Explanations for the Volatility … Continue reading
US market portrait 2013 week 22
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R — you are free … Continue reading
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
US market portrait 2013 week 21
US large cap market returns. Previous posts had the charts off by a day, and no one had the grace to say. The halving of DF is technically correct, but financially misleading. There was a spin-off where the other half of the value went. Fine print The data are from Yahoo Almost all of the … 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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