Posts Tagged ‘ autocorrelation ’

Inference and autoregressive processes

September 6, 2012
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Inference and autoregressive processes

Consider a (stationary) autoregressive process, say of order 2,for some white noise with variance . Here is a code to generate such a process, > phi1=.5 > phi2=-.4 > sigma=1.5 > set.seed(1) > n=240 > WN=rnorm(n,sd=sigma) > ...

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The volatility mystery continues

December 5, 2011
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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...

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Another look at autocorrelation in the S&P 500

November 11, 2011
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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...

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The mystery of volatility estimates from daily versus monthly returns

November 8, 2011
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The mystery of volatility estimates from daily versus monthly returns

What drives the estimates apart? Previously A post by Investment Performance Guy prompted “Variability of volatility estimates from daily data”. In my comments to the original post I suggested that using daily data to estimate volatility would be equivalent to using monthly data except with less variability.  Dave, the Investment Performance Guy, proposed the exquisitely … Continue reading...

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Graphical comparison of MCMC performance [arXiv:1011.445]

November 22, 2010
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Graphical comparison of MCMC performance [arXiv:1011.445]

A new posting on arXiv by Madeleine Thompson on a graphical tool for assessing performance. She has developed a software called SamplerCompare, implemented in R and C. The graphical evaluation plots “log density evaluations per iteration times autocorrelation time against a tuning parameter in a grid of plots where rows represent distributions and columns represent

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