How to capture return variability when testing strategies with long-short deciles. Traditional practice Question: Does variable X have predictive power for our universe of assets? A common scheme of quants to answer the question is to form a series of portfolios over time. The portfolio at each time point: is long the equal weighting of … Continue reading...
Performance analysis of an example portfolio. The portfolio We explore a particular portfolio during 2007. It invests in S&P 500 stocks and starts the year with a value of $10 million. Initially there are 50 names in the portfolio. It also ends the year with 50 names but has up to 53 names during the … Continue reading...
New Events Thalesians (London) 2012 November 21: Isabel Ehrlich on “Basket Options with Smile”. Abstract: Due to the distinct lack of models for basket options that remain consistent with the market smile we look at approximations that are able to accurately replicate the volatility smile. Notably we turn to the use of an Edgeworth series … Continue reading...
An introduction to estimating Value at Risk and Expected Shortfall, and some hints for doing it with R. Previously “The basics of Value at Risk and Expected Shortfall” provides an introduction to the subject. Starting ingredients Value at Risk (VaR) and Expected Shortfall (ES) are always about a portfolio. There are two basic ingredients that … Continue reading...
Specifics of statistical factor models and of a particular implementation of them. Previously Posts that are background for this one include: Three things factor models do Factor models of variance in finance The BurStFin R package The quality of variance matrix estimation The problem Someone asked me some questions about the statistical factor model in … Continue reading...
When aggregating over both time and assets, the order of aggregation matters. Task We have the weights for a portfolio and we want to use those and a matrix of returns over time to compute the (long-term) portfolio return. “A tale of two returns” tells us that aggregation over time is easiest to do in … Continue reading...
Should you use daily or monthly returns to estimate volatility? Does garch explain why volatility estimated with daily data tends to be bigger than if it is estimated with monthly data? Previously There are a number of previous posts — with the variance compression tag — that discuss the phenomenon of volatility estimated with daily … Continue reading...
Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of … Continue reading...
The authors are Andrie de Vries and Joris Meys. Executive summary Pretty much all I’d hoped for — and I had high hopes. Significance The “Dummies” series is popular for introducing specific topics in an inviting way. R For Dummies is a worthy addition to the pack. There is a competitor by the name of … Continue reading...
Here are detailed comments on the book. Elsewhere there is a review of the book. How to read R For Dummies In order to learn R you need to do something with it. After you have read a little of the book, find something to do. Mix reading and doing your project. You cannot win … Continue reading...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
