My intuition tells me that objects traveling through the air would meet more resistance when there is more moisture in the air. It turns out that my intuition is wrong. It still doesn’t make sense to me but apparently humid … Continue reading →

Here's a cool application of calendar heat maps: runner Andy used R to catalogue his daily running mileage over the last 2+ years: There are lots of ways to chart data like this (a simple time-series chart, for example), but sometimes looking at data in new ways offers fresh perspectives. For example, Andy notes: "Apparently I missed running on...

Small changes in the input assumptions often lead to very different efficient portfolios constructed with mean-variance optimization. I will discuss Resampling and Covariance Shrinkage Estimator – two common techniques to make portfolios in the mean-variance efficient frontier more diversified and immune to small changes in the input assumptions. Resampling was introduced by Michaud in Efficient

Time Series as calendar heat maps + All of my running data since April 1, 2009 = Generated by the following code: #Sample Code based on example program at: source(file = "calendarHeat.R") run<- read.csv("log.csv", header = TRUE, sep=",") sum(run$Distance) date <- c() for (i in 1: dim(run)){ if(run$DistanceUnit== 'Kilometer'){ miles <- c(miles,run$Distance * 0.62) }

What is important for an investor? The rate of return is at the top of the list. Does the expected rate of return shown on the mean-variance efficient frontier paints the full picture? If investor’s investment horizon is longer than one period, for example 5 years, than the true measure of portfolio performance is Geometric

The Seattle R User Group was kind enough to invite me to give a talk about R, C++ and Rcpp. So if you can make it to the Thomas building of the Fred Hutchinson Cancer Research Center in Seattle, WA, on December 7, I would love to see you there. I ha...

The Omega Ratio was introduced by Keating and Shadwick in 2002. It measures the ratio of average portfolio wins over average portfolio losses for a given target return L. Let x.i, i= 1,…,n be weights of instruments in the portfolio. We suppose that j= 1,…,T scenarios of returns with equal probabilities are available. I will

In the Maximum Loss and Mean-Absolute Deviation risk measures, and Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) posts I started the discussion about alternative risk measures we can use to construct efficient frontier. Another alternative risk measure I want to discuss is Downside Risk. In the traditional mean-variance optimization both returns above and