Blog Archives

Review of “R For Dummies”

October 15, 2012
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Review of “R For Dummies”

The authors are Andrie de Vries and Joris Meys. Executive summary Pretty much all I’d hoped for — and I had high hopes. Significance The “Dummies” series is popular for introducing specific topics in an inviting way. R For Dummies is a worthy addition to the pack. There is a competitor by the name of … Continue reading...

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Annotations for “R For Dummies”

October 15, 2012
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Annotations for “R For Dummies”

Here are detailed comments on the book.  Elsewhere there is a review of the book. How to read R For Dummies In order to learn R you need to do something with it.  After you have read a little of the book, find something to do.  Mix reading and doing your project. You cannot win … Continue reading...

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S&P 500 sector strengths

October 10, 2012
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S&P 500 sector strengths

Which sectors are coherent, and which aren’t? Previously The post “S&P 500 correlations up to date” looked at rolling mean correlations among stocks.  In particular it looked at rolling mean correlations of stocks within sectors. Of importance to this post is that the sectors used are taken from Wikipedia. Relative correlations The thought is that … Continue reading...

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Upcoming events

October 9, 2012
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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...

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S&P 500 correlations up to date

October 8, 2012
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S&P 500 correlations up to date

I haven’t heard much about correlation lately.  I was curious about what it’s been doing. Data The dataset is daily log returns on 464 large cap US stocks from the start of 2006 to 2012 October 5. The sector data were taken from Wikipedia. The correlation calculated here is the mean correlation of stocks among … Continue reading...

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How to add a benchmark to a variance matrix

October 1, 2012
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How to add a benchmark to a variance matrix

There is a good way and a bad way to add a benchmark to a variance matrix that will be used for optimization and similar operations.  Our examination sheds a little light on the process of variance matrix estimation in this realm. Role of benchmarks Investing Benchmarks are common in investment management.  It’s my opinion … Continue reading...

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Two particular courses and other upcoming events

September 25, 2012
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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...

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Variance targeting in garch estimation

September 24, 2012
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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...

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garch estimation on impossibly long series

September 20, 2012
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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...

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Variability of garch estimates

September 17, 2012
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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...

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