Posts Tagged ‘ R Language ’

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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Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann

September 12, 2012
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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...

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Not fooled by randomness

September 10, 2012
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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...

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Thalesians, and other events

September 5, 2012
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Thalesians, and other events

Featured Thalesians, London 2012 September 12. Chia Tan on “Practical Financial Modeling”. Abstract: Financial modelling is not a competition in the mastery of complexity. Rather, the aim is to come up with the simplest models adequate to capture salient market features of traded products. There exists a wide gulf between material covered by traditional books … Continue reading...

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A look at Bayesian statistics

September 3, 2012
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A look at Bayesian statistics

An introduction to Bayesian analysis and why you might care. Fight club The subject of statistics is about how to learn.  Given that it is about the unknown, it shouldn’t be surprising that there are deep differences of opinion on how to go about doing it (in spite of the stereotype that statisticians are accountants … Continue reading...

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garch and long tails

August 27, 2012
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garch and long tails

How much does garch shorten long tails? Previously Pertinent blog posts include: “A practical introduction to garch modeling” “The distribution of financial returns made simple” “Predictability of kurtosis and skewness in S&P constituents” Induced tails Part of the reason that the distributions of returns have long tails is because of volatility clustering.  It’s not really … Continue reading...

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Another comparison of heuristic optimizers

August 20, 2012
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Another comparison of heuristic optimizers

A herd of heuristic algorithms is compared using a portfolio optimization. Previously “A comparison of some heuristic optimization methods” used two simple and tiny portfolio optimization problems to compare a number of optimization functions in the R language. This post expands upon that by using a portfolio optimization problem that is of a realistic size … Continue reading...

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