Optimal Stopping with Gaussian Processes
This work addresses the problem of fast approximate optimal stopping for financial markets, offering a domain-specific incremental improvement over existing methods.
The authors tackled the problem of optimal stopping for financial time series by proposing Gaussian Process-based algorithms that leverage structural properties like mean reversion, resulting in outperformance over state-of-the-art benchmarks on historical datasets including equity stock prices and treasury yield rates.
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that structural properties commonly exhibited by financial time series (e.g., the tendency to mean-revert) allow the use of Gaussian and Deep Gaussian Process models that further enable us to analytically evaluate optimal stopping value functions and policies. We additionally quantify uncertainty in the value function by propagating the price model through the optimal stopping analysis. We compare and contrast our proposed methods against a sampling-based method, as well as a deep learning based benchmark that is currently considered the state-of-the-art in the literature. We show that our family of algorithms outperforms benchmarks on three historical time series datasets that include intra-day and end-of-day equity stock prices as well as the daily US treasury yield curve rates.