Category Archives: R language
Further adventures with higher moments
Additional views of the stability of skewness and kurtosis of equity portfolios. Previously A post called “Four moments of portfolios” introduced the idea of looking at the stability of the mean, variance, skewness and kurtosis of portfolios through time. That post gave birth to a presentation at the London Quant Group. That talk gave birth … Continue reading
Historical Value at Risk versus historical Expected Shortfall
Comparing the behavior of the two on the S&P 500. Previously There have been a few posts about Value at Risk (VaR) and Expected Shortfall (ES) including an introduction to Value at Risk and Expected Shortfall. Data and model The underlying data are daily returns for the S&P 500 from 1950 to the present. The VaR and … Continue reading
Posted in R language, Risk
Tagged Conditional Value at Risk, Expected Shortfall, S&P 500, Value at Risk
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Quant finance blogs
What I’ve learned from updating the blogroll. New entries The easy option is to go to The Whole Street which aggregates lots of quant finance blogs. Somehow Bookstaber missed out being on the blogroll before — definitely an oversight. Timely Portfolio was another that I was surprised wasn’t already there. The R Trader talks about … Continue reading
Posted in Quant finance, R language
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Four moments of portfolios
What good are the skewness and kurtosis of portfolios? Previously The post “Cross-sectional skewness and kurtosis: stocks and portfolios” looked at skewness and kurtosis in portfolios. The key difference between that post and this one is what distribution is being looked at. The previous post specified a single time and looked at the distribution across … Continue reading
The look of verifying data
Get data that fit before you fit data. Why verify? Garbage in, garbage out. How to verify The example data used here is daily (adjusted) prices of stocks. By some magic that I’m yet to fathom, market data can be wondrously wrong even without the benefit of the possibility of transcription errors. It doesn’t seem … Continue reading
Posted in Quant finance, R language
Tagged data errors, data verification, missed stock split
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Changeability of Value at Risk estimators
How does Value at Risk change through time for the same portfolio? Previously There has been a number of posts on Value at Risk, including a basic introduction to Value at Risk and Expected Shortfall. The components garch model was also described. Issue The historical method for Value at Risk is by far the most commonly … Continue reading
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
