Category Archives: R language
garch and the Algorithmic Trading Conference
The Imperial College Algorithmic Trading Conference was Saturday. Talks Massoud Mussavian Massoud gave a great talk on “Algo Evolution”. It started with a historical review of how trading used to be done “by hand”. It culminated in a phylogenetic tree of trading algorithms. There was an herbivore branch and a carnivore branch. Robert Macrae Robert … Continue reading
Posted in Quant finance, R language
Tagged Algorithmic Trading Conference, garch, volatility clustering
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Again with variability of long-short decile tests
A simpler approach to producing the variability. Previously The post “Variability in long-short decile strategy tests” proposed a way of assessing the variability of strategy tests in which a long-short portfolio is created by equally weighting the top and bottom deciles. Improved idea Joe Mezrich suggests maintaining equal weights but bootstrapping the assets within the … Continue reading
Variability in long-short decile strategy tests
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
Discovering the quality of portfolio decisions
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
Upcoming events
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
Posted in Events, R language
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The estimation of Value at Risk and Expected Shortfall
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
Posted in R language, Risk
Tagged Conditional Value at Risk, Expected Shortfall, Value at Risk
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The guts of a statistical factor model
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
An easy mistake with returns
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
Posted in Quant finance, R language
Tagged log return, log return vs simple return, simple return
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Volatility from daily or monthly: garch evidence
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
Posted in Quant finance, R language
Tagged garch simulation, variance compression, volatility
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The basics of Value at Risk and Expected Shortfall
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
Posted in R language, Risk
Tagged Conditional Value at Risk, Expected Shortfall, Value at Risk
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