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I'm an aspiring computer scientist who want to move into algorithmic trading at some point. At the moment I'm mostly focusing on courses in machine learning/data analysis etc. but I've noticed that my uni offers modules in stochastic analysis covering the topics mentioned in the question title. My question is if this would be useful given my career aspirations. Hope this isn't off topic, and thanks for reading :) |
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it depends on how applied the class is. A deep understanding of stochastic calculus is not required for "P-Quants", the type of person that lives in the physical word of forecasting and risk. That being said understanding the type of models that get used by the Q-Side (requiring lots of stochasic theory) is a useful skill to have. Like John said, if you wanted to forecast option returns, you need to have a good understanding of the statistical properties of options timeseries/cross sections to build either a risk model or a forecast model. Understanding the depths of stochastic calculus is not required here, but understanding black scholes (and the underlying assumptions) could be useful. In this regard a stochastic calculus class will help you. For a better distinction between p-quants and q-quants take a look at some of the work attilio maucci has done. article here: http://symmys.com/node/62 p-quant class here: http://symmys.com/arpm-bootcamp/program |
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