Category Archives: Quant finance
Variability of garch estimates
Not exactly pin-point accuracy. Previously Two related posts are: A practical introduction to garch modeling garch and long tails Experiment 1000 simulated return series were generated. The garch(1,1) parameters were alpha=.07, beta=.925, omega=.01. The asymptotic variance for this model is 2. The half-life is about 138 days. The simulated series used a Student’s t distribution … Continue reading
Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann
Previously This book and the associated R package were introduced before. Executive Summary A very nice — and enlightening — discussion of a wide range of topics. Principles The Introduction to the book sets out 5 principles. This is probably the most important part of the book. The principles are: We don’t know much in … Continue reading
Posted in Book review, Quant finance, R language
Tagged heuristic optimization, simulation
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garch and long tails
How much does garch shorten long tails? Previously Pertinent blog posts include: “A practical introduction to garch modeling” “The distribution of financial returns made simple” “Predictability of kurtosis and skewness in S&P constituents” Induced tails Part of the reason that the distributions of returns have long tails is because of volatility clustering. It’s not really … Continue reading
Highlights of R in Finance 2012
I unfortunately was not there, but we can vicariously enjoy it via the presentations that are posted on the conference website. Below is my take on the highlights (in chronological order). Peter Carl and Brian Peterson “Constructing Strategic Hedge Fund Portfolios” is wonderful from my perspective. Promoting random portfolios is sure to win my heart. … Continue reading
Posted in Quant finance, R language
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Returns with negative net asset values
How are returns calculated when net asset value goes negative? Previously In “A tale of two returns” we highlighted the similarities and differences of log returns versus simple returns. Positive valuation We create — in R — an example of net asset value at four times: > nav1 <- c(1000, 900, 950, 1010) > nav1 … Continue reading
2 dimensions of portfolio diversity
Portfolio diversity is a balancing act. Previously The post “Portfolio diversity” talked about the role of the correlation between assets and the portfolio. The current post fills a hole in that post. The 2 dimensions asset-portfolio correlation Each asset in the universe has a correlation with the portfolio. If there are any assets that have … Continue reading
Alpha alignment
An explanation of alpha factor alignment in portfolio optimization, and a look at the spectrum of views on it. Venue FactSet recently hosted an event that included a panel of representatives from several risk model vendors. The first question thrown at the panel was about alpha alignment. The opinions varied widely. There was positive correlation … Continue reading
Posted in optimization, Quant finance
Tagged alpha alignment, alpha eating, factor alignment
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A practical introduction to garch modeling
We look at volatility clustering, and some aspects of modeling it with a univariate GARCH(1,1) model. Volatility clustering Volatility clustering — the phenomenon of there being periods of relative calm and periods of high volatility — is a seemingly universal attribute of market data. There is no universally accepted explanation of it. GARCH (Generalized AutoRegressive … Continue reading
Cross sectional spread of stock returns
A look at a simplistic measure of stock-picking opportunity. Motivation The interquartile range (the spread of the middle half of the data) has recently been added to the market portrait plots. Putting those numbers into historical context was the original impulse. However, this led to thinking about change in stock-picking opportunity over time. Data Daily … Continue reading
Posted in Quant finance, R language
Tagged garch, interquartile range, S&P 500, stock-picking opportunity
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Variability in maximum drawdown
Maximum drawdown is blazingly variable. Psychology Probably the most salient feature that an investor notices is the amount lost since the peak: that is, the maximum drawdown. Just because drawdown is noticeable doesn’t mean it is best to notice. Statistics The paper “About the statistics of the maximum drawdown in financial time series” explores drawdown … Continue reading
