Testing for Structural Breaks and Dynamic Changes in Emerging Market Volatility

This paper aims to test for structural breaks and dynamic changes in emerging market volatility. We typically relate these issues to stock market liberalization since the latter is often considered as one of the most important forces that promote economic growth and rapid maturation of the emerging markets of the world. Using a bivariate GARCH-M model, stability tests in a linear framework and a pooled time-series cross-section model, we show that structural breaks detected in emerging market volatility series do not happen together with official liberalization dates, but they rather coincide with dates of the first ADR/Country Fund introduction and with dates of large increases in the US capital flows. Consistently, the pooled estimation results indicate that liberalization methods other than liberalization via a formal policy decree are the ones that significantly affect volatility.Stock Market Liberalization, Return Volatility, Emerging Markets, Bivariate GARCH-M models, Structural Breaks, Pooled Time-Series Analysis

Generalized dynamic factor models and volatilities: recovering the market volatility shocks

Decomposing volatilities into a common market-driven component and an idiosyncratic itemspecific one is an important issue in financial econometrics. This, however, requires the statistical analysis of large panels of time series, hence faces the usual challenges associated with highdimensional data. Factor model methods in such a context are an ideal tool, but they do not readily apply to the analysis of volatilities. Focusing on the reconstruction of the unobserved market shocks and the way they are loaded by the various items (stocks) in the panel, we propose an entirely non-parametric and model-free two-step general dynamic factor approach to the problem, which avoids the usual curse of dimensionality. Applied to the S&P100 asset return dataset, the method provides evidence that a non-negligible proportion of the market-driven volatility of returns originates in the volatilities of the idiosyncratic components of returns

Volatility transmission and changes in stock market interdependence in the European Community

A multivariate BEKK GARCH representation is employed to model stock market interdependence in groups of EC stock markets between 1987 and 2003. Using daily data, we estimate the effect that news or information spillovers from one market has on the next day returns in other markets. We quantify the sources of volatility transmission as price changes and noise. Our models allow interdependencies to vary over time allowing us to investigate whether interdependence changes following the introduction of the single currency. Generally, stock market integration increases after 1999 although there are differences in the levels of interdependence between (and within) northern and southern European markets. Information spillovers are tend to be transmitted more through noise than price changes though volatility transmission between Germany, Europe’s leading economic power, and the UK, Europe’s leading financial power, is through price changes after 1999. The results support the view that financial deregulation leads to financial market integration implying that further deregulatory acts can be expected to realise positive outcomes. The major European markets are increasingly integrated with the international (US) market. We observe the main transmission mechanism between Germany and the US is noise whereas it is price changes between the UK and US. Whereas US information influences UK returns more than UK information affects US returns, innovations in Germany are at least as important as US news is on next day German returns. Our conjecture is that the information content of European markets is not homogeneous to international markets

Application of statistical physics in time series analysis

This dissertation covers the four major parts of my PhD research: i) Modeling instantaneous correlation ii) Quantifying time-lag correlation iii) Modeling time-lag correlation iv) Modeling and application of heteroskedasticity. For modeling instantaneous correlation, we study the limitations of random matrix theory (RMT) and investigate the impact of autocorrelations on the results of RMT. We propose autoregressive random matrix theory (ARRMT) which takes into account the impact of autocorrelations on the study of crosscorrelations in multiple time series. We illustrate the method using air pressure data for 95 US cities. For quantifying time-lag correlation, we propose time-lag random matrix theory (TLRMT) and find long-range magnitude crosscorrelations in financial, physiological and genomic data. For modeling time-lag correlation, we propose a global factor model (GFM) and build the relationship between the autocorrelation of the global factor and the time-lag crosscorrelation among individual time series. We apply the method to equity indices data for 48 countries and find that a single global factor can explain most of the time lag crosscorrelations among these indices. For modeling and application of heteroskedasticity, we propose a high frequency trading model using two fractionally intergrated autoregressive conditional heteroskedasticity (FIARCH) processes, and explained the fat-tailed distribution of returns and the long memory in volatilities of financial data

Essays on Financial and Economic Risks

This dissertation consists of three essays on financial economics, focusing on different types of financial and economic risks and covering different geographical regions. These risk types are related to stock, bond and commodity markets, financial stress and country risk ratings. The first essay investigates directional relationships, regime variances, transition probabilities and expected regime durations for two systems of economic and financial variables. The first system consists of daily series which include credit and market risks. The second system is based on monthly data, and encompasses credit, and market risks and economic activity and oil variables. The methodology is based on the Markov-Switching cointegrated VAR model. The results suggest there is a pronounced regime-specific behavior in both systems with FTP-MS model. There is a significant difference between the higher expected duration in the low volatility regime and the lower duration in the high volatility regime in both systems. Both models suggest that during the 2007/2008 Great Recession, the system stays mainly in regime 2 but returns to the normality state in the 2009 recovery period. The fundamental variables (industrial production, oil prices and the real interest rate) have varying effects in both regimes and both systems. Quantitative easing has significant effects on the bond expected volatility index MOVE in the high volatility regime and industrial production in both regimes. I also examine the driving forces of the time-varying transition probabilities and find that increases of oil price will decrease the probability that the financial markets stay in the low volatility regime. The second essay examines the asymmetric adjustments of the stock markets of the five BRICS countries (Brazil, Russia, India, China and South Africa) to changes in the economic, financial and political country risk ratings of these countries in the short run and long run, using the momentum threshold autoregression (MTAR) and the vector error-correction(VEC) models. The findings suggest that the long-run relationships between these four variables respond asymmetrically depending on the direction of the shocks. The adjustment is faster when the spread between the actual level of stock market index and the level suggested by country risk ratings is narrowing than when it is widening, except for Russia which has the opposite response. The Chinese stock market seems to have the fastest adjustments in the short-and long-run among those of the five BRICS. In terms of the three country risk ratings the financial risk ratings for the five BRICS show the most responsiveness to all the variables in the long-run, while the political risk ratings exhibit the least. The economic and political risk ratings show the fastest adjustments for Brazil, while the financial risk rating is most pronounced in Russia. The third essay examines the Value-at-Risk for ten euro-zone equity markets individually and when divided into two groups: PIIGS and the Core, employing four VaR estimation methods. The results are evaluated according to four statistical properties as well as the Basel capital requirements for the period including the 2007/2008 financial crisis. The estimation and the evaluation are applied to the individual assets as well as to the portfolios consisting of the two groups. The results demonstrate that the CEVT method applied to the ten individual equity assets meet all the statistical criteria the best. The two optimal equity portfolios do not show diversification benefits as the PIIGS portfolio selects Spain's IBEX only and that of the Core opts for Austria's ATX only. The asset class-augmented portfolio that includes the Austrian (ATX) index, oil and gold gives the highest diversification gains. Adding other commodities such as corn and silver, or commodities indices to the augmented portfolio does not enhance the gains. At the optimal portfolio level, the Duration-Peak-Over-the-Threshold (DPOT) is recommended the best in terms of satisfying the Basel rules.Ph.D., Economics -- Drexel University, 201

Econometric Modeling and Evaluation of Fiscal-Monetary Policy Interactions

Thesis (Ph.D.) - Indiana University, Economics, 2015How do fiscal and monetary policies interact to determine inflation? The conventional view rests on the Taylor principle, that central banks can control inflation by raising nominal interest rate more than one-for-one with inflation. This principle embeds an implicit assumption that the government always adjusts taxes or spending to assure fiscal solvency. But when the required fiscal adjustments are not assured, as may occur during periods of fiscal stress, monetary policy may no longer be able to determine inflation. Under this alternative view, policy roles are reversed, with fiscal policy determining the price level and monetary policy acting to stabilize debt. Because these two policy regimes imply starkly different policy advice, identifying the prevailing regime is a prerequisite to understanding the macro economy and to making good policy choices. This dissertation employs econometric modeling and evaluation techniques to examine the empirical implications of the dynamic interactions between post-war U.S. fiscal and monetary policies. Chapter One compares two econometric interpretations of a dynamic macro model designed to study U.S. policy interactions. Two main findings emerge. First, the data overwhelmingly support the conventional view of inflation determination under the prevailing, "strong" econometric interpretation that takes literally all of the model's implications for the data. But this result is susceptible to any potential model misspecification. Second, according to the alternative, "minimal" econometric interpretation that is immune to the difficulties with the strong interpretation, the two views of inflation determination can explain the data about equally well. These findings imply that the apparent statistical support in favor of the conventional view over the alternative in the literature stems largely from the strong interpretation rather than from compelling empirical evidence. Therefore, a prudent policymaker should broaden her perspective beyond any single view on the inflation process. Chapter Two, joint with Todd B. Walker, develops an analytic function approach to solving generalized multivariate linear rational expectations models. This solution method is shown to provide important insights into equilibrium dynamics of well-known models. Chapter Three further demonstrates the usefulness of this method via a conventional new Keynesian model