CloudStat School is a not yet released open source project. The objective is to create an interactive R Learning Platform. The best way to learn R programming is doing while learning. In CloudStat School, you will see a console box at your top left han... [Read more...]

Here is an interview with Kai Chew, Founder of Cloudstat. CloudStat is developing a cloud-based statistical platform to help researchers who want to make sense of data to do statistical analysis collaboratively with its high performance computing infra... [Read more...]

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For those who did not know, Stanford university offered free off charge 3 courses at beginning of the autumn. It is kind of shocking – US based institution offers education for free! Take any socialism oriented country and one of the promises is education for free. But it seems, that the argument ... [Read more...]

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 ... [Read more...]

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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 ... [Read more...]

During the summer I was contacted by a hedge fund from Bahamas. The fund was looking for someone with R language skills on-site and insisted for phone interview. Besides obvious questions about finance, statistics, coding and how many tennis balls can fit in Boeing 747 (ok, this question was omitted), they ... [Read more...]

How does the effect of our expected returns change over time?  This is not academic  curiosity, we want to know in the context of our portfolio if we can.  And we can — we visualize the effect of expected returns in situ. First step The idea is to look at the ... [Read more...]

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Be careful if you have global daily data. The issue Markets around the world are open at different times.  November 21 for the Tokyo stock market is different from November 21 for the London stock market.  The New York stock market has yet a different November 21. The effect The major effect is ... [Read more...]

Hi! My fellow useRs! I’m making a web-based R Language platform ( http://cloudst.at/ ) for my students. My aim is to decrease the learning curve of learning R and collaboration. With CloudStat, there is no more download, installation, update and mai... [Read more...]

The return of a hypothetical fund was 17.9% in 2010.  We want to know if that is good or bad. The benchmark method The assets in the portfolio are constituents of the S&P 500, so we can compare our fund return to the return of the index. Figure 1: 2010 returns of: the fund ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

Investment Performance Guy has a post “Periodicity of risk statistcs (and other measures)” in which it is wondered how valid volatility estimates are from a month of daily returns. Here is a quick look.  Figure 1 shows the variability (and a 95% confidence interval) of volatility estimates for the S&P 500 index ... [Read more...]

Some thoughts and resources regarding a popular fund management buzzword. The idea Given asset categories (like stocks, bonds and commodities) create a portfolio where each category contributes equally to the portfolio variance. Two operations There are two cases in creating a risk parity portfolio: the universe is the asset categories ... [Read more...]

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For those who naturally compute portfolio returns correctly here are some lessons in how to do it wrong. The data Random portfolios were generated from constituents of the S&P 500 with constraints: long-only exactly 20 assets in the portfolio no more than 10% weight for any asset (just for fun) the sum ... [Read more...]

Are there times of the year when returns are better or worse? Abnormal Returns prompted this question with “SAD and the Halloween indicator” in which it is claimed that the US market tends to outperform from about Halloween until April. Data The data consisted of 15,548 daily returns of the S&... [Read more...]