by Haksun Li | Jan 6, 2015 | AlgoQuant, Algorithmic Trading, Seminars

This blog article is taken from our book [1]. In most entry-level materials on pairs trading such as in [2], a mean reverting basket is usually constructed by this relationship: , where is the price of asset at time t, the price of asset at time t, and the price of the mean reverting asset to trade. One way to find is to use cointegration. There are numerous problems in this approach as detailed in [1]. To mention a few: the identified portfolios are dense; executions involve considerable transaction costs; the resultant portfolios behave like insignificant and non-tradable noise; cointegration is too stringent and often unnecessary a requirement to satisfy. This article highlights one important problem: it is much better to work in the space of (log) returns than in the space of prices. Therefore, we would like to build a mean reverting portfolio using a similar relationship to (eq. 1) but in returns rather than in prices. The Benefits of Using Log Returns When we compare the prices of two assets, [… TODO …] A Model for a Mean Reverting Synthetic Asset Let’s assume prices are log-normally distributed, which is a popular assumption in quantitative finance, esp. in options pricing. Then, prices are always positive, satisfying the condition of “limited liability” of stocks. The upside is unlimited and may go to infinity. [5] We have: is the return for asset between times 0 and t; likewise for asset . Instead of applying a relationship, e.g., cointegration (possible but not a very good way), to the pair on prices, we can do it on returns. This is possible because, just like prices, the returns at...
by Haksun Li | Jul 23, 2014 | SuanShu

a picture is worth a thousand words… Check out the release notes here: http://numericalmethod.com/forums/topic/suanshu-3-0-0-is-now-released/ Happy birthday to me...
by Haksun Li | Jul 15, 2013 | News

Numerical Method Inc. has the vision to promote rational investment and trading. Jointly organized with top universities, we are offering a 6-months 9-courses program that teach mathematics, programming and quantitative or algorithmic trading. We invite famous and established traders from Wall Street banks and funds to share their experience. Students may choose to participate in classroom or online. More information can be found here: http://numericalmethod.com/cqien...
by Haksun Li | May 16, 2013 | Seminars

Change of Measure or Girsanov’s Theorem is such an important theorem in Real Analysis or Quantitative Finance. Unfortunately, I never really understood it until much later after having left school. I blamed it to the professors and the textbook authors, of course. The textbook version usually goes like this. Given a probability space , and a non-negative random variable Z satisfying (why 1?). We then defined a new probability measure Q by the formula, for all . Any random variable X, a measurable process adapted to the natural filtration of the , now has two expectations, one under the original probability measure P, which denoted as , and the other under the new probability measure Q, denoted as . They are related to each other by the formula If , then P and Q agree on the null sets. We say Z is the Radon-Nikodym derivatives of Q with respect to P, and we write . To remove the mean, μ, of a Brownian motion, we define Then under the probability measure Q, the random variable Y = X + μ is standard normal. In particular, (so what?). This text made no sense to me when I first read it in school. It was very frustrated that the text was filled with unfamiliar terms like probability space and adaptation, and scary symbols like integration and . (I knew what meant when y was a function and x a variable. But what on earth were dQ over dP?) Now after I have become a professor to teach students in finance or financial math, I would get rid of all the jargon...
by Haksun Li | Dec 25, 2012 | SuanShu

On this Christmas Day, we are happy to announce that SuanShu.net is FREE for all! SuanShu.net has all the features as its Java sibling as well as has undergone the same many thousands of test cases daily. There are a tutorial and examples that show you how to build a SuanShu application in Visual Studio. One major advantage of using SuanShu.net over the Java version is that it integrates seamlessly with Microsoft Excel. By incorporating SuanShu library in your spreadsheet, you literally have access to hundreds of numerical algorithms when manipulating and analyzing your data, significantly enhancing Excel’s productivity. We hope that you enjoy using SuanShu.net in your work. If you have any interesting story, comments or feedback, we’d love to hear from you. Starting downloading SuanShu.net...
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