Monthly Archives: January 2014

The portfolio optimization higher-moment credo

The question of skewness and kurtosis in portfolio optimization. Previously Problem 4 of “The top 7 portfolio optimization problems” concerns the use of higher moments. “Further adventures with higher moments” is the most recent in a series of posts on the efficacy of higher moments in optimization.  This set includes the observation that “trade selection” … Continue reading

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US market portrait 2014 week 4

US large cap market returns. Fine print The data are from Yahoo The S&P 500 stocks are used (as implied by S&P on 2014 January 11) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R — you are free to use these functions however you like

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What is volatility?

Some facts and some speculation. Definition Volatility is the annualized standard deviation of returns — it is often expressed in percent. A volatility of 20 means that there is about a one-third probability that an asset’s price a year from now will have fallen or risen by more than 20% from its present value. In … Continue reading

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US market portrait 2014 week 3

US large cap market returns. Fine print The data are from Yahoo The S&P 500 stocks are used (as implied by S&P on 2014 January 11) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R — you are free to use these functions however you like

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garch models caught in the spotlight

An attempt to clarify the basics. Previously There have been several posts about garch.  In particular: A practical introduction to garch modeling The components garch model in the rugarch package Genesis A reader emailed me because he was confused about the workings of garch in general, and simulation with the empirical distribution in particular. If … Continue reading

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US market portrait 2014 week 2

US large cap market returns. Fine print The data are from Yahoo The S&P 500 stocks are used (as implied by S&P on 2014 January 11) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R — you are free to use these functions however you like

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S&P that might have been

The S&P 500 returned 29.6% in 2013.  How might that have varied? S&P weights There are many features that could vary — here we will keep the same constituents (almost) and weights with similar sizes but that are randomly assigned rather than based on market capitalization. That is, we want the large weights of our … Continue reading

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US market portrait 2013 final

US large cap market returns. Prior year The full year portrait for 2012 is shown below. 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 … Continue reading

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