title: Statistical Inference with High-Frequency Financial Data
title: New Perspectives from Alternative Observation Schemes
titleType: Subtitle
Creators: name: Yu, Shifan
Publisher: name: Lancaster University
Year: 2024

description: This thesis is a compilation of three main studies with the common theme:
 statistical inference with high-frequency financial data under alternative
 observation or sampling schemes. The increasing availability of
 high-frequency financial data has motivated the development of new
 statistical inference tools, such as (i) advanced volatility estimators
 with better robustness in the presence of local extreme phenomena in
 intraday asset prices, and (ii) techniques for identifying these unusual
 extreme events, which contribute to a comprehensive understanding of their
 impact on financial markets. The investigation on both topics in this
 thesis adopts an innovative perspective by exploring potential
 enhancements from utilizing high-frequency financial data under
 alternative observation schemes, which stands in contrast to traditional
 methods that rely on equidistantly sampled transaction price records.
 
 
 In Chapter 2, we introduce a novel nonparametric high-frequency jump test
 for discretely observed Itô semimartingales. Based on observations sampled
 recursively at first exit times from a symmetric double barrier, our
 method distinguishes between threshold exceedances caused by the Brownian
 component and jumps, which enables the construction of a feasible,
 noise-robust statistical test. Simulation results demonstrate superior
 finite-sample performance of our test compared to classical methods. An
 empirical analysis of NYSE-traded stocks provides clear statistical
 evidence for jumps, with the results highly robust to spurious detections.
 
 
 In Chapter 3, we develop a new nonparametric estimator of integrated
 variance that utilizes intraday candlestick information, comprised of the
 high, low, open, and close prices within short time intervals. The
 range-return-difference volatility (RRDV) estimator is robust to
 short-lived extreme return persistence hardly attributable to the
 diffusion component, such as gradual jumps and flash crashes. By modelling
 such sharp but continuous price movements following some recent
 theoretical advances, we demonstrate that RRDV can provide consistent
 estimates with variances about four times smaller than those obtained with
 the differenced-return volatility (DV) estimator. Monte Carlo simulations
 and empirical applications further validate the practical reliability of
 our proposed estimator with some finite-sample refinements.
 
 
 In Chapter 4, we introduce an innovative semiparametric framework for
 duration-based volatility estimation. We filter out daily volatility
 dynamics from intraday price durations by employing a nonparametrically
 predicted threshold that dynamically adapts to the volatility of each day.
 This enables the application of parametric models to price durations
 collected over various days, which greatly enhances the flexibility of
 model estimation and facilitates the construction of more accurate
 volatility estimators. Simulation results demonstrate superior
 finite-sample performance of our duration-based estimators for both spot
 and integrated volatility compared to some established methods. An
 empirical application based on intraday data for the SPDR S&amp;P 500
 ETF highlights the improved forecasting accuracy of our integrated
 volatility estimator within a standard daily volatility forecasting
 framework. Furthermore, an intraday analysis based on our spot volatility
 estimator reveals an immediate and substantial impact of FOMC news
 announcements on market volatility.