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Financial Volatility Predictability and Model Validation

Xingyi Li at the School of Business and Law will defend her thesis "Financial Volatility Predictability and Model Validation" for the PhD-degree Tuesday 7 January 2020. (Private photo)

The empirical study of the essay demonstrates a model validation procedure that tests the ability of a volatility model to replicate the list of statistics. The main conclusion in this essay is that none of the tested models is able to fully replicate the volatility stylized facts.

Xingyi Li

PhD Candidate

Xingyi Li at the School of Business and Law  has submitted her thesis entitled  "Essays on the Horizon of Volatility Predictability and Volatility Model Validation" and will defend the thesis for the PhD-degree Tuesday 7 January 2020.

She has followed the PhD programme at the School of Business and Law.

Summary of the thesis by Xingyi Li:

Financial Volatility Predictability and Model Validation

Volatility is a measure of uncertainty about financial returns. Modelling and forecasting volatility has become a rapidly growing interest and crucial task in financial industry since the early sixties. For forecasting, this dissertation answers how far into the future volatility forecasts can be made (the first and third essays) and how volatility predictability varies across the market states (the second essay). For modelling, this dissertation examines how well volatility models capture stylized facts or dynamics observed in financial markets (the fourth essay).

The first essay uses the model-based approach to explore the horizon of volatility predictability. It quantifies the forecast accuracy across horizons by introducing the notions of the spot and forward predicted volatility. These notions contribute to describe the term structure of volatility predictability and provide more deep information on the horizon of volatility predictability and forecast accuracy than previous studies. The results of this essay suggest that the horizon of volatility predictability is substantially larger than that reported in previous studies.

The stock market states are classified into bull and bear markets in the second essay. This essay investigates the horizon of volatility predictability and quantifies the volatility forecast accuracy across the bull and bear market states separately. Both the first and second essays use the same forecast accuracy measure. The general conclusion of the essay is that the volatility predictability is strongest in bad economic times represented by bear market states. That is, volatility predictability is best when it is most needed, during periods of high turbulence and uncertainty.

The third essay investigates the same question as the first essay using model-free tests. The name “model-free” comes from the fact that the conclusion on the horizon of volatility predictability is drawn directly from the observed returns without any volatility models involved. The essay significantly extends the horizon of volatility forecastability reported in the earlier model-free studies.

A common research novelty in the first three essays is that they, for the first time, adopt the so-called “meta-analysis” to aggregate the results on volatility predictability from individual assets. A key benefit of this approach is that the aggregation of information on individual assets leads to a higher statistical power and more robust forecast accuracy estimates than it is possible to obtain from the information on any individual asset.

The fourth essay introduces a novel framework which answers the question of how well a volatility model reproduces the stylized facts. Specifically, the framework encompasses a list of statistics quantifying the volatility stylized facts. The empirical study of the essay demonstrates a model validation procedure that tests the ability of a volatility model to replicate the list of statistics.  The main conclusion in this essay is that none of the tested models is able to fully replicate the volatility stylized facts.

 

Disputation facts:

The Candidate: Xingyi Li (1987, Shanxi Province, China) BSc in Statistics, Minzu University of China, Master degree (MSc) in Financial Mathematics, University of Edinburgh, UK.

The trial lecture and the public defence will take place at Sørlandets auditorium, Kunnskapsparken, Universitetsveien 19, Campus Kristiansand Tuesday 7 January 2020.

Head of the Department of Economics and Finance, Associate Professor Liv Bente Hannevik Friestad, will chair the disputation.

Trial lecture at 10:15 a.m.

Public defense at 12:15 p.m.

Given topic for trial lecture: "Libra, the new global cryptocurrency: pros and cons of its proposed design and the look into its future”. With the view of discussing Libra’s potential developments, its use, volatility, comparison to other crypto currencies, potential for the secondary market (options, futures), its effect on interest rates and such issues.

Thesis Title: “Essays on the Horizon of Volatility Predictability and Volatility Model Validation”

Search for the thesis in AURA - Agder University Research Archive, a digital archive of scientific papers, theses and dissertations from the academic staff and students at the University of Agder. The thesis will also be available at the University Library, and some copies will also be available for loan at the auditorium where the disputation takes place.

Opponents:

First opponent: Professor Svein-Arne Persson, NHH Norwegian School of Economics

Second opponent: Associate Professor Svetlana Borovkova, School of Business and Economics, Finance at Vrije Universiteit Amsterdam, Nederland

Professor Jochen Jungeilges, Department of Economics and Finance, UiA, is appointed as the administrator for the assessment commitee.

Supervisor were Professor Valeriy Zakamulin, Department of Economics and Finance, UiA