Blog Archives

Prediction model for the FIFA World Cup 2014

June 12, 2014
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Prediction model for the FIFA World Cup 2014

Like a last minute goal, so to speak, Andreas Groll and Gunther Schauberger of Ludwig-Maximilians-University Munich announced their predictions for the FIFA World Cup 2014 in Brazil - just hours before the opening game. Andreas Groll, with his successful prediction of the European Championship 2012 already experienced in this field, and Gunther Schauberger did set out to predict the 2014 world cup champion based on statistical...

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Real world quantitative risk management: estimation risk and model risk

December 2, 2013
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Real world quantitative risk management: estimation risk and model risk

Real world quantitative risk management is more than deriving risk from a given model. In reality, we do know neither the true structure of the model, nor its parameters. Hence, measuring risk in reality always entails estimation risk and model risk.

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Analyzing volatility patterns for SP500 stocks

October 23, 2013
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Analyzing volatility patterns for SP500 stocks

Analyzing SP500 volatility paths for common underlying factors

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Presenting your findings with R

September 7, 2013
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Presenting your findings with R

R packages to provide more interactive and richer user experience: knitr, rCharts, slidify, shiny and OpenCPU.

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Programming style guidelines: R and MATLAB

August 16, 2013
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Programming style guidelines: R and MATLAB

summary of programming style conventions in R and MATLAB

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Downloading SP500 stock price data with R

August 7, 2013
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Downloading SP500 stock price data with R

Using R, we show how to download historic stock prices for all S&P500 components from Yahoo!Finance. We visualize missing data, and process stock prices to get clean daily logarithmic returns. The data then could readily be used in financial applications like risk management or asset management.

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Student’s t: location-scale

April 30, 2013
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Student’s t: location-scale

Location-scale extension for Student's t-distribution in R

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Time series data in R

April 22, 2013
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Time series data in R

Handling time series data in R In this blog post I want to write some thoughts about handling time series data in R. In contrast to cross-sectional data, in time series applications each observation has an additional component besides it’s value: the point of time. This requires some additional efforts, for example: x-axis has to

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