Category Archives: R language

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

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

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

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

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

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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Highlights of R in Finance 2012

I unfortunately was not there, but we can vicariously enjoy it via the presentations that are posted on the conference website. Below is my take on the highlights (in chronological order). Peter Carl and Brian Peterson “Constructing Strategic Hedge Fund Portfolios” is wonderful from my perspective.  Promoting random portfolios is sure to win my heart.  … Continue reading

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Returns with negative net asset values

How are returns calculated when net asset value goes negative? Previously In “A tale of two returns” we highlighted the similarities and differences of log returns versus simple returns. Positive valuation We create — in R — an example of net asset value at four times: > nav1 <- c(1000, 900, 950, 1010) > nav1 … Continue reading

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R Inferno-ism: order is not rank

Do not use order when you want rank. Background The update of “A comparison of some heuristic optimization methods” is due to the bug that Luca Scrucca spotted. Actually, it is two bugs: I used order when I meant rank This somehow escaped being in The R Inferno   Problem What I said in my … Continue reading

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A comparison of some heuristic optimization methods

A simple portfolio optimization problem is used to look at several R functions that use randomness in various ways to do optimization. Orientation Some optimization problems are really hard. In these cases sometimes the best approach is to use randomness to get an approximate answer. Once you decide to go down this route, you need … Continue reading

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