Nonparametric estimation of linear multiplier for stochastic differential equations driven by multiplicative stochastic volatility

Fuente: arXiv
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Main Author: Rao, B. L. S Prakasa
Format: Preprint
Published: 2024
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author Rao, B. L. S Prakasa
author_facet Rao, B. L. S Prakasa
contents We study the problem of nonparametric estimation of the linear multiplier function $θ(t)$ for processes satisfying stochastic differential equations of the type $$dX_t= θ(t)X_t dt+ ε\; σ_1(t,X_t)σ_2(t,Y_t)dW_t, X_0=x_0, 0 \leq t \leq T$$ where $\{W_t, t\geq 0\}$ is a standard Brownian motion, $\{Y_t, t\geq 0\}$ is a process adapted to the filtration generated by the Brownian motion. We study the problem of estimation of the unknown function $θ(.)$ as $ε\rightarrow 0$ based on the observation of the process $\{X_t,0\leq t \leq T\}.$
format Preprint
id arxiv_https___arxiv_org_abs_2412_00005
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Nonparametric estimation of linear multiplier for stochastic differential equations driven by multiplicative stochastic volatility
Rao, B. L. S Prakasa
Statistics Theory
Probability
62G05
We study the problem of nonparametric estimation of the linear multiplier function $θ(t)$ for processes satisfying stochastic differential equations of the type $$dX_t= θ(t)X_t dt+ ε\; σ_1(t,X_t)σ_2(t,Y_t)dW_t, X_0=x_0, 0 \leq t \leq T$$ where $\{W_t, t\geq 0\}$ is a standard Brownian motion, $\{Y_t, t\geq 0\}$ is a process adapted to the filtration generated by the Brownian motion. We study the problem of estimation of the unknown function $θ(.)$ as $ε\rightarrow 0$ based on the observation of the process $\{X_t,0\leq t \leq T\}.$
title Nonparametric estimation of linear multiplier for stochastic differential equations driven by multiplicative stochastic volatility
topic Statistics Theory
Probability
62G05
url https://arxiv.org/abs/2412.00005