Nonparametric Estimation for I.I.D. Paths of a Martingale Driven Model with Application to Non-Autonomous Financial Models

TL;DR

This paper develops nonparametric estimators for functions in martingale-driven models, providing risk bounds and applying these methods to complex financial models like non-autonomous Black-Scholes and fractional stochastic volatility models.

Contribution

It introduces a projection least squares estimator for the function J_0 and extends the approach to non-autonomous financial models with risk bounds and practical discrete versions.

Findings

Risk bounds established for the estimator of J_0

Adaptive estimator performance analyzed

Application to non-autonomous financial models

Abstract

This paper deals with a projection least squares estimator of the function $J_0$ computed from multiple independent observations on $[0,T]$ of the process $Z$ defined by $dZ_t = J_0(t)d\langle M\rangle_t + dM_t$, where $M$ is a continuous and square integrable martingale vanishing at $0$. Risk bounds are established on this estimator, on an associated adaptive estimator and on an associated discrete-time version used in practice. An appropriate transformation allows to rewrite the differential equation $dX_t = V(X_t)(b_0(t)dt +\sigma(t)dB_t)$, where $B$ is a fractional Brownian motion of Hurst parameter $H\in [1/2,1)$, as a model of the previous type. So, the second part of the paper deals with risk bounds on a nonparametric estimator of $b_0$ derived from the results on the projection least squares estimator of $J_0$. In particular, our results apply to the estimation of the drift function in a non-autonomous Black-Scholes model and to nonparametric estimation in a non-autonomous fractional stochastic volatility model.