Semimartingale detection and goodness-of-fit tests

TL;DR

This paper introduces a new goodness-of-fit test for volatility processes in semimartingale models, utilizing wavelet-thresholding to achieve adaptive and near-optimal detection, applicable in financial modeling.

Contribution

The paper presents a novel wavelet-based goodness-of-fit test specifically designed for volatility-like processes in semimartingale models, improving detection efficiency.

Findings

The test achieves near-optimal detection rates.

It is easily applicable to various semimartingale models.

The method effectively detects deviations under noisy observations.

Abstract

In quantitative finance, we often fit a parametric semimartingale model to asset prices. To ensure our model is correct, we must then perform goodness-of-fit tests. In this paper, we give a new goodness-of-fit test for volatility-like processes, which is easily applied to a variety of semimartingale models. In each case, we reduce the problem to the detection of a semimartingale observed under noise. In this setting, we then describe a wavelet-thresholding test, which obtains adaptive and near-optimal detection rates.