Likelihood-based inference and forecasting for trawl processes: a stochastic optimization approach

TL;DR

This paper introduces a likelihood-based inference framework for trawl processes, utilizing stochastic optimization and novel gradient estimators, enabling improved parameter estimation and forecasting for complex stochastic models.

Contribution

It develops the first likelihood-based methodology for real-valued trawl processes, including new gradient estimators and a Python library for practical implementation.

Findings

Gradient estimators significantly reduce variance.

Estimators outperform generalized method of moments.

Method improves out-of-sample forecasting accuracy.

Abstract

We consider trawl processes, which are stationary and infinitely divisible stochastic processes and can describe a wide range of statistical properties, such as heavy tails and long memory. In this paper, we develop the first likelihood-based methodology for the inference of real-valued trawl processes and introduce novel deterministic and probabilistic forecasting methods. Being non-Markovian, with a highly intractable likelihood function, trawl processes require the use of composite likelihood functions to parsimoniously capture their statistical properties. We formulate the composite likelihood estimation as a stochastic optimization problem for which it is feasible to implement iterative gradient descent methods. We derive novel gradient estimators with variances that are reduced by several orders of magnitude. We analyze both the theoretical properties and practical implementation details of these estimators and release a Python library which can be used to fit a large class of trawl processes. In a simulation study, we demonstrate that our estimators outperform the generalized method of moments estimators in terms of both parameter estimation error and out-of-sample forecasting error. Finally, we formalize a stochastic chain rule for our gradient estimators. We apply the new theory to trawl processes and provide a unified likelihood-based methodology for the inference of both real-valued and integer-valued trawl processes.