Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach

TL;DR

This paper introduces a novel approach combining dimension reduction on non-positive curvature manifolds with deep learning to efficiently approximate the conditional law of Volterra processes, addressing high-dimensional challenges in financial modeling.

Contribution

It proposes a stable dimension reduction technique onto non-positive curvature manifolds and a deep learning model with hypernetworks to adaptively approximate Volterra process laws.

Findings

Effective dimension reduction on complex stochastic processes.

Deep learning model accurately approximates the projected conditional law.

Hypernetwork enables dynamic adaptation to non-stationary process dynamics.

Abstract

Predicting the conditional evolution of Volterra processes with stochastic volatility is a crucial challenge in mathematical finance. While deep neural network models offer promise in approximating the conditional law of such processes, their effectiveness is hindered by the curse of dimensionality caused by the infinite dimensionality and non-smooth nature of these problems. To address this, we propose a two-step solution. Firstly, we develop a stable dimension reduction technique, projecting the law of a reasonably broad class of Volterra process onto a low-dimensional statistical manifold of non-positive sectional curvature. Next, we introduce a sequentially deep learning model tailored to the manifold's geometry, which we show can approximate the projected conditional law of the Volterra process. Our model leverages an auxiliary hypernetwork to dynamically update its internal parameters, allowing it to encode non-stationary dynamics of the Volterra process, and it can be interpreted as a gating mechanism in a mixture of expert models where each expert is specialized at a specific point in time. Our hypernetwork further allows us to achieve approximation rates that would seemingly only be possible with very large networks.