In this Brief, we visit the issue of “statistical arbitrage” in financial markets, and spotlight two courses:
- June 12 – July 10: Financial Risk Modeling (today)
- July 10 – Aug 7: Spatial Statistics for GIS Using R
See you in class!
P.S. Our newest course, Analyzing and Modeling Coronavirus Data, also starts June 12 (today)
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An economics professor and an engineering professor were walking across campus. The engineering professor spots something lying in the grass – “Look- here’s a $20 bill!” The economist doesn’t bother to look. “It can’t be – […]
Word of the Week
A consistent estimator (same as consistent statistic) is one that converges to the true value being measured as the sample size grows larger. One aspect of this convergence is that the statistic must be unbiased – it must converge to the correct value, not some other value.
The world is facing the deepest economic crash since the great depression, so, naturally, our spotlight this week is on
Financial Risk Modeling (June 12 – July 10)
In this course, you will learn how to
- Set up financial model simulations using appropriate probability distributions
- Use time-series models, and characterize the different components of a time series (trend, seasonality, autocorrelation, volatility, mean reversion)
- Use various technique to include correlations within simulation models
- Fit appropriate probability distributions to historical data, and assess the fit (AIC, etc.)
- Interpret results from simulation models, including expected Net Present Value (eNPV), Value at Risk (VaR), and the probability of negative NPV
Your instructor is Dr. Huybert Groenendaal, Managing Partner at EpiX Analytics, which specializes in risk analysis and modeling techniques for clients around the world. He has extensive experience in risk modeling and analysis for business development, financial valuation, R&D portfolios and portfolio evaluations in pharmaceuticals and medical devices.
Spatial Statistics for GIS Using R (July 10 – Aug 7)
In this course, you will learn how to:
- Describe spatial data using maps
- Describe and implement the ways spatial data is represented in R
- Use spastat to analyze patterns in point data, and detect non-randomness
- Use spdep to analyze patterns in area data, and measure spatial autocorrelation in lattice data
- Use gstat to analyze continuous field data and create contour maps
Your instructor is Prof. Dave Unwin, co-author of Geographic Information Analysis (Wiley), and a variety of other books on this topic.
Analyzing and Modeling Covid-19 Data (June 12 to July 10)
We’ll cover analysis of Covid data broadly, and focus on the epidemiological and statistical models used to forecast the spread of the pandemic. In this seminar-style course for statistically-literate* researchers, you will
- Explore key rates and features of the Coronavirus data
- Learn how to specify epidemiological models
- Learn how to fit statistical models
- The strengths and weaknesses of each type of model
The instructors are
- Dr. James Hardin, Epidemiology and Biostatistics Associate Professor and Associate Dean for Faculty Affairs and Curriculum for the Arnold School of Public Health, University of South Carolina.
- Wayne Folta a Lead Data Scientist at Elder Research, Inc. where he develops and deploys models for clients. His current work involves the design and implementation of text mining and deep learning models at the U.S. Dept. of Health and Human Services; he has also been active within Elder Research in exploring and assessing epidemiological models in R.
See you in class!
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