Tag Archives: S&P 500
US market portrait 2012 week 17
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Subscribe to the Portfolio Probe blog by Email
Replacing market indices
If equity markets suddenly sprang into existence now, would we create market indices? I’m doubtful. Why an index? The Dow Jones Industrial Average was born in 1896. This was when computers were humans with adding machines (but they did do parallel processing). At that point boiling “the market” down to a single number had value. … Continue reading
Beta is not volatility
The missing link between beta and volatility is correlation. Previously “4 and a half myths about beta in finance” attempted to dislodge several myths about beta, including that beta is about volatility. “Low (and high) volatility strategy effects” showed a plot of beta versus volatility for stocks in the S&P 500 for estimates from 2006. … Continue reading
A minimum variance portfolio in 2011
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
Realized efficient frontiers
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
Posted in Quant finance, R language
Tagged efficient frontier, Ledoit-Wolf shrinkage, MACD, S&P 500
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A slice of S&P 500 kurtosis history
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
A slice of S&P 500 skewness history
How symmetric are the returns of the S&P 500? How does the skewness change over time? Previously We looked at the predictability of kurtosis and skewness in S&P constituents. We didn’t see any predictability of skewness among the constituents. Here we look at skewness from a different angle. The data Daily log returns of the … Continue reading
Volatility estimation and time-adjusted returns
Do non-trading days explain the mystery of volatility estimation? Previously The post “The volatility mystery continues” showed that volatility estimated with daily data tends to be larger (in recent years) than when estimated with lower frequency returns. Time adjusting One of the comments — from Joseph Wilson — was that there is a problem with … Continue reading
Posted in Quant finance, R language
Tagged S&P 500, time-adjusted returns, variance compression, volatility
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The volatility mystery continues
How do volatility estimates based on monthly versus daily returns differ? Previously The post “The mystery of volatility estimates from daily versus monthly returns” and its offspring “Another look at autocorrelation in the S&P 500” discussed what appears to be an anomaly in the estimation of volatility from daily versus monthly data. In recent times … Continue reading
Posted in Quant finance, R language
Tagged autocorrelation, S&P 500, variance compression, volatility
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Another look at autocorrelation in the S&P 500
Casting doubt on the possibility of mean reversion in the S&P 500 lately. Previously A look at volatility estimates in “The mystery of volatility estimates from daily versus monthly returns” led to considering the possibility of autocorrelation in the returns. I estimated an AR(1) model through time and added a naive confidence interval to the … Continue reading
Posted in Quant finance, R language
Tagged autocorrelation, mean reversion, S&P 500, variance compression
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