Blog Archives

Again with Ledoit-Wolf and factor models

May 4, 2011
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Again with Ledoit-Wolf and factor models

We come closer to a definitive answer on the relative merit of Ledoit-Wolf shrinkage versus a statistical factor model for variance matrices. Previously This post builds on the post entitled: A test of Ledoit-Wolf versus a factor model That post depended on some posts previous to it. New information Previously we generated random portfolios with … Continue reading...

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The R Inferno revised

May 1, 2011
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The R Inferno revised

Hell is new and improved. The R Inferno has been revised.  If you don’t know of it, it is a short explanation of a few trouble spots when using the R language.  Somehow the short explanation grew to approach book-length. It can be found at the usual place: http://www.burns-stat.com/pages/Tutor/R_inferno.pdf Major improvements An index has been … Continue reading...

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A test of Ledoit-Wolf versus a factor model

April 27, 2011
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A test of Ledoit-Wolf versus a factor model

Statistical factor models and Ledoit-Wolf shrinkage are competing methods for estimating variance matrices of returns.  So which is better?  This adds a data point for answering that question. Previously There are past blog posts on: the idea of variance matrices factor models of variance The data in this post are from the blog posts: “Weight … Continue reading...

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Risk fraction constraints and volatility

April 21, 2011
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Risk fraction constraints and volatility

What is the effect on predicted and realized volatility of substituting risk fraction constraints for weight constraints? Previously This post depends on two previous blog posts: “Unproxying weight constraints” “Weight compared to risk fraction” The exact same sets of random portfolios are used in this post that were generated in the second of these. Payoff … Continue reading...

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Weight compared to risk fraction

April 18, 2011
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Weight compared to risk fraction

How well do asset weight constraints constrain risk? The setup In “Unproxying weight constraints” I claimed that many constraints on asset weights are really a proxy for constraining risk. That is not a problem if weights are a good proxy for risk.  So the question is: how good of a proxy are they? To give … Continue reading...

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The devil of overfitting

March 27, 2011
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The devil of overfitting

Overfitting is a problem when trying to predict financial returns.  Perhaps you’ve heard that before.  Some simple examples should clarify what overfitting is — and may surprise you. Polynomials Let’s suppose that the true expected return over a period of time is described by a polynomial. We can easily do this in R.  The first … Continue reading...

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Factor models of variance in finance

March 7, 2011
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Factor models of variance in finance

In “What the hell is a variance matrix?” I talked about the basics of variance matrices and highlighted challenges for estimating them in finance.  Here we look more deeply at the most popular estimation technique. Models for variance matrices The types of variance estimates that are used in finance can be classified as: Sample estimate … Continue reading...

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4 and a half myths about beta in finance

February 8, 2011
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4 and a half myths about beta in finance

Much of what has been said and thought about beta in finance is untrue. Myth 1: beta is about volatility This myth is pervasive. Beta is associated with the stock’s volatility but there is more involved.  Beta is the ratio of the volatility of the stock to the volatility of the market times the correlation … Continue reading...

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Review of “R Graphs Cookbook” by Hrishi Mittal

January 24, 2011
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Review of “R Graphs Cookbook” by Hrishi Mittal

Executive summary: Extremely useful for new users, informative to even quite seasoned users. Refereeing Once upon a time a publisher asked if I would referee a book (unspecified) about R.  In an instance that can only be described as psychotic I said yes.  That bit of insanity turned out to be a good thing. I … Continue reading...

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Paying interest and the number e

January 24, 2011
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Paying interest and the number e

Suppose I borrow a dollar from you and I’ll pay you 100% interest at the end of the year.  How much money will you have then? $1 * (1 + 1) = $2 What happens if instead the interest is calculated as  50% twice in the year? $1 * (1.5 * 1.5) = $2.25 After … Continue reading...

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