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Our popular course Introduction to QuantLib Development will be taking place June 18-20th, 2018.

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Title: Mr

Short Description: PhD Candidate

Institution: University of Connecticut

Special Academic Interests: ,

Linked in Profile: linkedin

Blog URL: link

Is this Member on the AGENDA team?: No

Joined: July 31st, 2011


Stephen Rush wrote a new blog post titled Timing csv File Reading in R and Python

I am currently working with trade data organized in large csv files. I took this as an opportunity to learn the Pandas package in Python mostly for the HDF5 integration. As a sanity check, I decided to time three methods: read.csv in R base, fread in the data.table package in R, and read.csv in the Pandas package in Python. Let me first say that I am not a programmer. This test is not a definitive benchmark of the three methods. It is a benchmark of the “out-of-the-box” functionality available to the non-programmer trying to use code to get shit done. I used time.time() in Python...
(1585 days ago)

Stephen Rush wrote a new blog post titled Two-way Sorting in R

One of the popular ways to motivate a two dimensional model is to double sort your data based on quantiles. This makes empirical asset pricing models exceedingly easy to understand. If I had already defined the breakpoints of the sort, I could just use a function like “plyr” to apply a function like “mean” to the data subsets. For my application, I want to create five portfolios based on one characteristic and then form five portfolio within each of the first five based on another characteristic. To generalize from a 5×5 sort, I include the parameter q to allow...
(1840 days ago)

Stephen Rush wrote a new blog post titled Managerial Game Theory

One of the first models covered in game theory is the Prisoner’s Dilemma. I’m sure you’ve heard of it or at least recognize it from episodes of Law and Order. Let’s look at the simple model with 2 individual players so we can eliminate mixed strategy equilibria and focus strictly on pure strategies. The two strategies for each player are “productive” and “exploitive”. The payoffs for the two players can be summarized on the usual grid. Employee Productive Exploitive Manager Productive 1,1 0,2 Exploitive 2,0 0,0 One possible economic...
(2317 days ago)