Category Archives: Quant finance
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
Posted in Quant finance, R language
Tagged covariance matrix, factor model, Ledoit-Wolf shrinkage, risk fraction, S&P 500, variance matrix
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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
Posted in Quant finance, R language
Tagged covariance matrix, factor model, Ledoit-Wolf shrinkage, risk fraction, S&P 500, variance matrix
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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
Posted in Quant finance, R language, Random portfolios
Tagged risk fraction, S&P 500, variance partition
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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
Posted in Quant finance, R language, Random portfolios
Tagged risk fraction, S&P 500, variance partition
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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
Inflexible regime, inflexible prices
There is a deep connection between political mechanisms and economic mechanisms, at least according to Ajay Shah. Price flexibility Ajay Shah has a post called Jittery regimes fix prices. It is well worth reading the whole piece (which isn’t very long anyway). Here’s an excerpt: Flexible prices are constantly disruptive. Every day, there are a … Continue reading
Posted in Economics, Fund management in general, Quant finance
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Thalesians: events and videos
The Thalesians is a group that has been going for a few years in London, and is just about to have its first event in New York. It holds events on various topics that are generally not far from quantitative finance. Events The first New York talk will be held Wednesday 2011 February 23. Gerald … Continue reading
Quantitative finance now on Stack Exchange
The site is http://quant.stackexchange.com/ A new area has emerged in Stack Exchange for Quantitative Finance (in trying to spell that I now know why it is usually just “quant”). It has been in private beta for a few weeks and has become public in the last few days. Already it has reasonable traffic. I predict … Continue reading
Posted in Quant finance
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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
Posted in Quant finance, R language
Tagged beta in finance, Capital Asset Pricing Model, CAPM, statistical bootstrap
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The number 1 novice quant mistake
It is ever so easy to make blunders when doing quantitative finance. Very popular with novices is to analyze prices rather than returns. Regression on the prices When you want returns, you should understand log returns versus simple returns. Here we will be randomly generating our “returns” (with R) and we will act as if … Continue reading
