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Contents
1 INTRODUCTION
2 VOLATILITY: CONCEPT AND EMPIRICAL EVIDENCE
2.1 Definition of Price Volatility
2.2 Analysis of Oil Price Volatility
2.3 Economic Sources of Price Volatility
3 METHODOLOGY
3.1 Modelling Oil Price Volatility
3.2 Univariate Volatility Models
3.2.1 Historical Volatility
3.2.2 ARCH
3.2.3 GARCH
3.2.4 GJR-GARCH
3.2.5 ARMA-GJR-GARCH
3.2.6 Conditional Distribution
3.2.7 Regime-Switching Models
3.2.8 Markov-Switching Models
3.2.9 Markov-Switching ARCH (SWARCH)
3.2.10 Indicators of synchronization
3.3 Bivariate GARCH Models - Volatility Transmission Models
3.3.1 VECH
3.3.2 BEKK GARCH (1,1)
4 FINANCIAL MARKET AND OIL PRICE VOLATILITY: CONTAGION AND TRANSMISSION CHANNELS
4.1 Definition of Contagion
5 EMPIRICAL SECTION
5.1 Data description
5.2 Estimation of Oil Price Volatility
5.2.1 Univariate GARCH (1,1) and GJR-GARCH (1,1)
5.2.2 Results of GARCH (1,1) and GJR-GARCH (1,1) Models
5.3 Volatility Transmission
5.3.1 Markov-Switching Estimation
5.3.2 Indicator of Synchronization
5.4 Estimation of BEKK GARCH (1,1)
5.4.1 Testing for Volatility Transmission
5.4.2 Empirical Results
6 DISCUSSION OF THE EMPIRICAL RESULTS
6.1 Volatility Transmission Channel – Financial Deleveraging
6.2 Extension of the model
6.2.1 Overvaluation and GARCH (1,1)
7 CONCLUSION
8 REFERENCES
Objectives and Research Themes
The primary objective of this thesis is to analyze the structural volatility transmission between the global oil market and the financial market, specifically investigating how volatility shocks spill over across these asset classes during periods of financial distress. The research explores the causal linkages and propagation mechanisms of market volatility, aiming to determine whether financial market instability functions as a contagion source for oil price fluctuations.
- Analysis of oil price volatility determinants (fundamental vs. non-fundamental factors).
- Econometric modeling of volatility clustering using ARCH and GARCH frameworks.
- Application of Markov-Switching models to detect structural breaks and volatility regimes.
- Evaluation of volatility spillover and contagion effects using multivariate BEKK GARCH models.
- Quantification of synchronization between the oil and financial markets during the 2008 financial crisis.
Excerpt from the Book
3.2.2 ARCH
Autoregressive Conditional Heteroscedasticity (ARCH) model was originally created by Engle (1982) to analyze U.K. inflationary uncertainty. Indeed, the ARCH models have been found wide application in statistical techniques in the estimation of time varying financial volatility. The ARCH process interprets the conditional heteroscedasticity of financial returns by assuming that current conditional variance is a function, or more precisely a weighted average of past squared unexpected returns.
Thus, sigma^2_t = var(r_t|psi_{t-1}) denotes the conditional variance of the series, and psi_{t-1} = {r_{t-1}, r_{t-2}, ...} is the available information set at time t-1. The conditional variance in the ARCH (q) model is a function of the magnitude of previous unanticipated innovations, epsilon_{t-1}. Based on the assumption that variances are non-negative, the ARCH model imposes the constraints that alpha_0 >= 0 and alpha_1,...,alpha_q >= 0, then the conditional variances are positively related to the value of epsilon^2_{t-1}. These features permits the prediction of time series conditional variance, when the dispersion of the residual returns is normal distributed. For example, if an appreciable market movement occurred yesterday, the day before or up to n days ago, the effect will be an increase in today’s conditional variance due to the fact that all parameters are constrained to be non-negative. However the use of ARCH model for the estimation of financial volatility is not recommended, because it requires many lags to approach the estimation to a GARCH model, which will be presented in the following section. As the lag increases in ARCH model framework it becomes more complex to estimate parameters because the likelihood function becomes flat.
Summary of Chapters
1 INTRODUCTION: This chapter provides an overview of market volatility and its impact on the economy, establishing the rationale for investigating volatility spillovers between oil and financial markets.
2 VOLATILITY: CONCEPT AND EMPIRICAL EVIDENCE: This section defines price volatility and discusses economic sources, highlighting the emergence of oil as a financial asset and the influence of speculation on market instability.
3 METHODOLOGY: This chapter outlines the econometric framework, detailing univariate models (ARCH/GARCH) and multivariate regimes (Markov-Switching) used to analyze return and volatility dynamics.
4 FINANCIAL MARKET AND OIL PRICE VOLATILITY: CONTAGION AND TRANSMISSION CHANNELS: This section defines the concepts of financial contagion and shift-contagion, focusing on how shocks propagate from financial sectors to the oil market.
5 EMPIRICAL SECTION: This chapter presents the data description, econometric estimation results for univariate and bivariate models, and the analysis of synchronization indicators.
6 DISCUSSION OF THE EMPIRICAL RESULTS: This section interprets the econometric findings, focusing on the impact of financial distress (Lehman Brothers failure) on volatility transmission and the role of financial deleveraging.
7 CONCLUSION: The final chapter summarizes the findings regarding the deviation from the Efficient Market Hypothesis and discusses the potential for model extensions to capture asset overvaluation.
Keywords
Oil Price Volatility, Financial Markets, GARCH, Markov-Switching, Contagion, Volatility Transmission, Financial Deleveraging, Structural Breaks, Heteroscedasticity, Asset Pricing, Speculation, Hedging, Spillover Effects, WTI Crude Oil, S&P 500.
Frequently Asked Questions
What is the core subject of this thesis?
The thesis examines the volatility transmission and interdependence between the oil market (WTI crude) and the financial market (S&P 500) during the period of the 2008 global financial crisis.
What are the primary themes addressed in the work?
Key themes include the impact of market shocks, the role of financial contagion, volatility persistence, the influence of informational asymmetry, and the effectiveness of hedging mechanisms under distress.
What is the central research question?
The research asks whether volatility shocks originating in financial markets spill over into the oil market and whether this relationship qualifies as a contagion effect.
Which econometric methods are utilized?
The author employs univariate GARCH models, GJR-GARCH for asymmetry, Markov-Switching models (SWARCH) for regime detection, and bivariate BEKK-GARCH models for volatility transmission channels.
What does the main body of the work cover?
The main body focuses on theoretical definitions of volatility, the detailed methodology for modeling non-linear variance, the empirical estimation of shocks, and a comprehensive discussion of how financial deleveraging fueled volatility.
What are the characterizing keywords of this study?
The study is characterized by terms such as volatility transmission, contagion, Markov-Switching models, financial distress, and cross-market linkages.
How did the Lehman Brothers collapse affect the results?
The bankruptcy triggered a structural break in the volatility term structure, causing parameters that were previously insignificant to become significant, signaling a shift to a high-volatility regime.
What is the significance of the "shift-contagion" concept here?
Shift-contagion describes how the sudden increase in correlation between the financial and oil markets during the credit crunch demonstrates an amplification of cross-market linkages beyond normal tranquil periods.
Why are synchronization indicators important in this analysis?
These indicators are used to measure the proportion of time the two markets share the same volatility state, helping to verify if market movements were truly synchronized during crises.
Does the author suggest improvements for future research?
Yes, the author proposes integrating an exogenous variable representing asset overvaluation into GARCH specifications to better explain the probability of volatility regime switching.
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- Fidel Farias (Autor:in), 2010, Volatility Transmission between the Oil and Stock Markets, München, Page::Imprint:: GRINVerlagOHG, https://www.diplomarbeiten24.de/document/335376
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