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Cointegration, S&P, and random matrices
ProbabilitySpeaker: | Vadim Gorin, UC Berkeley |
Location: | 2112 MSB |
Start time: | Wed, Feb 15 2023, 10:00AM |
Cointegration is a property of an N-dimensional time series, which says that each individual component is non-stationary (growing like a random walk), but there exists a stationary linear combination. Testing procedures for the presence of cointegration have been extensively studied in statistics and economics, but most results are
restricted to the case when N is much smaller than the length of the time series. I will discuss the recently discovered mathematical structures, which make the large N case accessible.
On the applied side we will see a remarkable match between predictions of random matrix theory and behavior of S&P 100 stocks. On the theoretical side we will see how ideas from statistical mechanics and asymptotic representation theory play a crucial role in the analysis.