Date of Award

Spring 5-15-2016

Author's School

Graduate School of Arts and Sciences

Author's Department

Statistics

Degree Name

Master of Arts (AM/MA)

Degree Type

Thesis

Abstract

This thesis studies a unifying class of nonparametric spot volatility estimators proposed by Mancini et. al.(2013). This method is based on delta sequences and is conceived to include many of the existing estimators in the field as special cases. The thesis first surveys the asymptotic theory of the proposed estimators under an infill asymptotic scheme and fixed time horizon, when the state variable follows a Brownian semimartingale. Then, some extensions to include jumps and financial microstructure noise in the observed price process are also presented. The main goal of the thesis is to assess the suitability of the proposed methods with both high-frequency simulated data and real transaction data from the stock market. In conclusion, double exponential kernel shows the best properties when estimating. Besides, the theorem is robust with the presence of jumps and microstructure noise and the U-shape curves of intraday spot volatility are achieved.

Language

English (en)

Chair and Committee

Jose E. Figueroa-Lopez

Committee Members

Jose E. Figueroa-Lopez, Renato Feres, Todd Kuffner

Comments

Permanent URL: https://doi.org/10.7936/K71N7ZF5

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