Monthly Archives: October 2011

Risk parity

Some thoughts and resources regarding a popular fund management buzzword. The idea Given asset categories (like stocks, bonds and commodities) create a portfolio where each category contributes equally to the portfolio variance. Two operations There are two cases in creating a risk parity portfolio: the universe is the asset categories the universe is the assets … Continue reading

Posted in Fund management in general, Quant finance, R language | Tagged , | 7 Comments

Governors and stability

Last night at the Royal Institution’s 14-10 club George Cooper gave a talk based on his book The Origin of Financial Crises.  One of the analogies was between James Clerk Maxwell’s analysis of governors in steam engines and the governors of central banks. Both types of governors have the task of bringing a system into … Continue reading

Posted in Economics | Leave a comment

Introduction to “Numerical Methods and Optimization in Finance”

The book is by Manfred Gilli, Dietmar Maringer and Enrico Schumann.  I haven’t actually seen the book, so my judgement of it is mainly by the cover (and knowing the first two authors). The parts of the book closest to my heart are optimization, particularly portfolio optimization, and particularly particularly portfolio optimization via heuristic algorithms.  … Continue reading

Posted in Book review, optimization, R language | Tagged | 1 Comment

How to compute portfolio returns badly

For those who naturally compute portfolio returns correctly here are some lessons in how to do it wrong. The data Random portfolios were generated from constituents of the S&P 500 with constraints: long-only exactly 20 assets in the portfolio no more than 10% weight for any asset (just for fun) the sum of the 5 … Continue reading

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Does the S&P 500 exhibit seasonality through the year?

Are there times of the year when returns are better or worse? Abnormal Returns prompted this question with “SAD and the Halloween indicator” in which it is claimed that the US market tends to outperform from about Halloween until April. Data The data consisted of 15,548 daily returns of the S&P 500 starting in 1950.  … Continue reading

Posted in Quant finance, R language | Tagged , | 5 Comments

Review of “23 Things They Don’t Tell You about Capitalism” by Ha-Joon Chang

Executive Summary Simple but not simplistic explanations of several big ideas in economics. Surprises Thing 4 The washing machine has changed the world more than the internet has. We are used to marvelling at the wonders of computing.  However, we tend to forget that our lives would be extremely different without machines to do household … Continue reading

Posted in Book review, Economics | 1 Comment

Linear constraints with risk fractions

A different sort of generalization of variance partitions. Previously The post “Generalizing risk fractions” described additional (to version 1.04 of Portfolio Probe) ways of dividing the variance among the assets.  This post describes the other major addition in the new version. Linear constraints Linear constraints on sectors, industries and countries are quite common.  These constrain … Continue reading

Posted in Portfolio Probe, Quant finance | Tagged , | 1 Comment

Sargent and Sims Nobel Prize

Thomas Sargent and Chris Sims won the 2011 Nobel Prize in economics. In a nutshell Sargent won for dynamic learning of economic agents and Sims won for championing vector autoregression and impulse response in econometrics. Already there has been a lot written, here are the items (in order) that I’ve found that I recommend: Tyler … Continue reading

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Generalizing risk fractions

More ways of constraining the variance attributable to individual assets. Introduction This post describes some additions to the 1.04 version of Portfolio Probe.  A beta of that version was released last week. We’ve also added Linux 32-bit and 64-bit as platforms on which Portfolio Probe is for sale.  Unfortunately demo and academic versions are still … Continue reading

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Predictability of kurtosis and skewness in S&P constituents

How much predictability is there for these higher moments? Data The data consist of daily returns from the start of 2007 through mid 2011 for almost all of the S&P 500 constituents. Estimates were made over each half year of data.  Hence there are 8 pairs of estimates where one estimate immediately follows the other. … Continue reading

Posted in Quant finance, R language | Tagged , , , | 3 Comments