Monthly Archives: September 2012
US market portrait 2012 week 40
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
Two particular courses and other upcoming events
Featured I’ll be leading two courses in the near future: Value-at-Risk versus Expected Shortfall 2012 October 30-31, London. 30th: “Addressing the critical challenges and issues raised by the Basel proposal to replace VaR with Expected Shortfall” 31st: “Variability in Value-at-Risk and Expected Shortfall” led by Patrick Burns Details at CFP Events. Finance with R Workshop … Continue reading
Posted in Events, R language
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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
US market portrait 2012 weeks 38 and 39
US large cap market returns. Notice The week plot this time is for quite a long “week”. Last week there were problems downloading the data. There were still some this week, but the R code has been updated to be more useful in this event (it is no longer necessary to get all the data … 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
Variability of garch estimates
Not exactly pin-point accuracy. Previously Two related posts are: A practical introduction to garch modeling garch and long tails Experiment 1000 simulated return series were generated. The garch(1,1) parameters were alpha=.07, beta=.925, omega=.01. The asymptotic variance for this model is 2. The half-life is about 138 days. The simulated series used a Student’s t distribution … Continue reading
Horses and volatility
Two items struck me as being connected. Maybe they are. The items: Payoff of betting on horses versus the odds The Missing Risk Premium by Eric Falkenstein As you can see there is a unicorn on the cover, but that is not the horse connection I had in mind. Horses The post at Thinking in … Continue reading
Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann
Previously This book and the associated R package were introduced before. Executive Summary A very nice — and enlightening — discussion of a wide range of topics. Principles The Introduction to the book sets out 5 principles. This is probably the most important part of the book. The principles are: We don’t know much in … Continue reading
Posted in Book review, Quant finance, R language
Tagged heuristic optimization, simulation
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Not fooled by randomness
The paper is “Not Fooled by Randomness: Using Random Portfolios to Analyze Investment Funds” by Roberto Stein. Here is an explanation of the idea of random portfolios. Favorite sentence The real question here is whether we’re actually measuring skill, or these are still measures of performance, so influenced by extraneous factors that the existence of … Continue reading
US market portrait 2012 week 37
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
