Blog Archives

The mystery of volatility estimates from daily versus monthly returns

November 8, 2011
By
The mystery of volatility estimates from daily versus monthly returns

What drives the estimates apart? Previously A post by Investment Performance Guy prompted “Variability of volatility estimates from daily data”. In my comments to the original post I suggested that using daily data to estimate volatility would be equivalent to using monthly data except with less variability.  Dave, the Investment Performance Guy, proposed the exquisitely … Continue reading...

Read more »

Variability of volatility estimates from daily returns

November 3, 2011
By
Variability of volatility estimates from daily returns

Investment Performance Guy has a post “Periodicity of risk statistcs (and other measures)” in which it is wondered how valid volatility estimates are from a month of daily returns. Here is a quick look.  Figure 1 shows the variability (and a 95% confidence interval) of volatility estimates for the S&P 500 index in January 2011.  … Continue reading...

Read more »

Risk parity

October 31, 2011
By
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...

Read more »

Introduction to “Numerical Methods and Optimization in Finance”

October 27, 2011
By
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...

Read more »

How to compute portfolio returns badly

October 24, 2011
By
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...

Read more »

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

October 20, 2011
By
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...

Read more »

Predictability of kurtosis and skewness in S&P constituents

October 3, 2011
By
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...

Read more »

Time series equivalence of brains and markets

September 27, 2011
By
Time series equivalence of brains and markets

fMRI data from 90 locations in the brain look somewhat like daily closing prices on 116 stocks if you squint just right. Marginal Revolution was nice enough to point to “Topological isomorphisms of human brain and financial market networks”. I’ve only just glanced through the paper.  I find it interesting, but I’m fairly skeptical.  The … Continue reading...

Read more »

Beta and expected returns

September 16, 2011
By
Beta and expected returns

Some pictures to explore the reality of the theory that stocks with higher beta should have higher expected returns. Figure 2 of “The effect of beta equal 1″ shows the return-beta relationship as downward sloping.  That’s a sample of size 1.  In this post we add six more datapoints. Data The exact same betas of … Continue reading...

Read more »

Solve your R problems

September 12, 2011
By
Solve your R problems

  download ‘The R Inferno’ Epilogue I’m not a lawyer, but here is my understanding of the rules should you want to extract images from this page: Most of the images are from istockphoto.com. You would need to pay for each image that you want to use. It is unlikely that Sandro Botticelli is going … Continue reading...

Read more »