A bit of testing of the estimation of the variance matrix for S&P 500 stocks in 2011. Previously There was a plot in “Realized efficient frontiers” showing the realized volatility in 2011 versus a prediction of volatility at the beginning of the year for a set of random portfolios. A reader commented to me privately … Continue reading...
How wide is the darkness? Uses of models The main way models are used is to: shine light on the “truth” We create and use a model to learn how some part of the world works. But there is a another use of models that is unfortunately rare — a use that should be common … Continue reading...
2011 was a good vintage for minimum variance, at least among stocks in the S&P 500. Previously The post “Realized efficient frontiers” included, of course, a minimum variance portfolio. That portfolio seemed interesting enough to explore some more. “What does ‘passive investing’ really mean” suggests that minimum variance should be considered a form of passive … Continue reading...
A look at the distortion from predicted to realized. The idea The efficient frontier is a mainstay of academic quant. I’ve made fun of it before. This post explores the efficient frontier in a slightly less snarky fashion. Data The universe is 474 stocks in the S&P 500. The predictions are made using data from … Continue reading...
We know the words but what do they mean? Some definitions Here are some definitions of “passive investment management”. Investopedia says: A style of management associated with mutual and exchange-traded funds (ETF) where a fund’s portfolio mirrors a market index. Wikipedia says: Passive management (also called passive investing) is a financial strategy in which an investor (or … Continue reading...
Version 1.01 of BurStFin is now on CRAN. It is written entirely in R, and meant to be compatible with S+. Functionality The package is aimed at quantitative finance, but the variance estimation functions could be of use in other applications as well. Also of general interest is threeDarr which creates a three-dimensional array out … Continue reading...
How fat tailed are returns, and how does it change over time? Previously The sister post of this one is “A slice of S&P 500 skewness history”. Orientation The word “kurtosis” is a bit weird. The original idea was of peakedness — how peaked is the distribution at the center. That’s what we can see, … Continue reading...
The Super Bowl tells us so. The Super Bowl Indicator The championship of American football decides the direction of the US stock market for the year. If a “National” team wins, the market goes up; if an “American” team wins, the market goes down. Yesterday the Giants, a National team, beat the Patriots. The birth … Continue reading...
Why returns have a stable distribution As “A tale of two returns” points out, the log return of a long period of time is the sum of the log returns of the shorter periods within the long period. The log return over a year is the sum of the daily log returns in the year. … Continue reading...
A not unusual part of a response on the R-sig-finance mailing list is: “Search the list archives.” In principle that makes sense. In practice it might not be clear what to do. Now it should be. The list The R-sig-finance mailing list deals with the intersection of questions about the R language and finance. It … Continue reading...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
