Monthly Archives: January 2013
The components garch model in the rugarch package
How to fit and use the components model. Previously Related posts are: A practical introduction to garch modeling Variability of garch estimates garch estimation on impossibly long series Variance targeting in garch estimation The model The components model (created by Engle and Lee) generally works better than the more common garch(1,1) model. Some hints about … Continue reading
US market portrait 2013 week 4
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
A sister blog is born
The Burns Statistics blog had it’s first real post today (about the corner function in the BurStMisc package). The blog will talk mainly about the R language, statistics and programming — it will not have the financial focus of the Portfolio Probe blog. The posts on the Burns Statistics blog will be announced on Twitter … Continue reading
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Clustering and sector strength
An exploration of the usefulness of sectors. Previously This subject was discussed in “S&P 500 sector strengths”. Idea Stocks are put into groups based on the sector that the company is considered to be in. Cluster analysis is a statistical technique that finds groups. If sectors really move together, then clustering should recover sectors. Will … Continue reading
US market portrait 2013 week 3
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
My missed opportunity with random portfolios
The Observer tells of a ginger tabby named Orlando who selected a random portfolio that won an investment contest. Meanwhile I have a gray tabby here on the desk doing nothing. All that effort to write software to generate random portfolios efficiently when I could have been using cat power instead. Not a single random … Continue reading
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The incoherence of risk coherence
What coherent risk measures are, why some people think coherence is important, and why I don’t. The rules A risk measure is considered to be coherent if it satisfies some mathematical properties. They are formulated in various ways — here is one set: (monotonicity) If the value of portfolio X is always bigger than the … Continue reading
US market portrait 2013 week 2
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
Market predictions for year 2013
Calibrations of 2013 predictions for 18 equity indices — plus some publicly available predictions. Orientation The distributions are an attempt to see the variability if there were no market-driving news for the whole year. Another way of thinking: mentally moving the distribution to center on a prediction gives a sense of the variability of results … Continue reading
US market portrait 2013 week 1
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Appendix R The R commands to get the data were: require(XML) wikisptab <- … Continue reading
