ROCKINGER Michael

This thesis consists of an introductory chapter and three empirical studies that contribute to the international finance literature by examining the dynamics of simultaneous jumps in international stock markets and assessing their impact on international portfolio allocation and financial asset pricing.In the first study, we examine the impact of co-jumps between international stock markets on the demand for foreign assets and the gains from international diversification. Using intraday data from three international indices (SPY, EFA and EEM), we identify a significant number of intraday co-jumps between the three markets considered. We also find that the intensity of co-jumps is particularly high during the 2008-2009 global financial crisis. Furthermore, the application of the Hawkes process shows that jumps tend to propagate from the US and other developed markets to emerging markets. To assess the impact of co-jumps on international diversification, we consider a U.S. investor who chooses his portfolio composition from one domestic risky asset (SPY) and two foreign risky assets (EFA and EEM) so as to reduce his overall portfolio risk. Our results also show that the impact of higher order moments (skewness, kurtosis,.) induced by idiosyncratic and systematic jumps on the optimal portfolio composition is not significant.In the second paper, we address the international equity valuation problem by decomposing systematic risk into continuous and discontinuous components (jumps). We contribute to the financial literature on the explanation of international asset returns by proposing a model with six risk factors. Using intraday data from a set of exchange-traded funds covering developed, emerging and frontier markets, we show that risks of continuous and discontinuous market swings are positively rewarded in the pre-financial crisis period while risks of positive and large jumps are rewarded with a negative premium in the post-financial crisis period. We also show that market price jumps are negatively correlated with volatility jumps, suggesting that price and volatility risks share compensation for the same underlying risk factors.In the third paper, we jointly examine price and volatility jump risks and study the response of international markets to jumps in an aggregate risk factor in both price and volatility. Using intraday data from ten exchange traded funds covering major developed and emerging markets and two volatility indices (VIX and VXEEM), we show that price and volatility jump betas vary over time and exhibit asymmetry between positive and negative jumps. We also show that price and volatility jumps in the market have significant predictive power on future stock returns.

The main focus of the thesis is on modern nonlinear approaches to the analysis of economic and financial data, with a particular emphasis on business cycles and financial crises. A consensus in the statistical and financial literature has developed around the fact that economic variables behave non-linearly during different phases of the business cycle. As such, nonlinear approaches/models are required to capture the characteristics of the inherently asymmetric data generation mechanism, which linear models are unable to reproduce.In this regard, the thesis proposes a new interdisciplinary and open approach to the analysis of economic and financial systems. The thesis presents approaches robust to extreme values and non-stationarity, applicable to both small and large samples, for both economic and financial time series. The thesis provides step-by-step procedures in the analysis of economic and financial indicators by integrating concepts based on the data substitution method, wavelets, phase embedding space, delay vector variance (DVV) method and plot recurrences. The thesis also highlights transparent methods for identifying and dating turning points and assessing the impacts of economic and financial crises. In particular, the thesis also provides a procedure for anticipating future crises and its consequences.The study shows that the integration of these techniques in learning the structure and interactions within and between economic and financial variables will be very useful in the development of crisis policies, as it facilitates the choice of appropriate treatment methods suggested by the data.In addition, a new procedure for testing linearity and unit root in a nonlinear framework is proposed by introducing a new model - the MT-STAR model - which has similar properties to the ESTAR model but reduces the effects of identification problems and can also represent asymmetry in the adjustment mechanism towards equilibrium. The proposed asymptotic distributions of the unit root test are non-standard and are calculated. The power of the test is evaluated by simulation and some empirical illustrations on real exchange rates show its effectiveness. Finally, the thesis develops multi-variate Self-Exciting Threshold Autoregressive models with exogenous variables (MSETARX) and presents a parametric estimation method. The modeling of MSETARX models and the problems generated by its estimation are briefly discussed.

The research work carried out in this thesis is essentially aimed at detecting regime and behavioral changes of agents through options markets. Indeed, option prices allow us to deconstruct and analyze the behavior of agents in financial markets in order to obtain a better understanding of their expectations and preferences. This information resides in particular in the valuation method of these conditional contracts. It is indeed possible to extract from option prices an implicit probability density, known as risk neutral, that reflects agents' expectations about the future evolution of the underlying asset. In the first part of this thesis, we classify the different approaches used in the literature for this purpose. We also show the advantages and disadvantages of each of them. The second part of the thesis is devoted to the description of a new non-para metric method based on Padé approximants. This method of extracting the risk-neutral density does not require any assumption about the process followed by the returns of the underlying asset. It also has the advantage of respecting the no-arbitrage constraints and offers the possibility to differentiate locally the treatment of the initial information according to common characteristics. Nevertheless, the non-parametric extraction of the neutral risk density also has limitations due to the different sources of noise that alter the information contained in the option prices. In the third part, we study the components of this noise while simulating their respective effects on the extracted information. Among these sources of information distortion, we focus on the bid-ask spread, which is omnipresent in the markets. Thus, the fourth part, in which we introduce the notion of discontinuity cost, allows us to complete the recent literature on the typology of micro-structure phenomena impacting the bid-ask spread applied on options markets. Our contribution in this area also modifies the symmetry that is often assumed in the literature between the bid and ask prices in options markets. Finally, this microeconomic phenomenon studied on high-frequency data also allows us to state robust hypotheses regarding the location of a fair price for each option. On this basis, we have proposed an innovative approach, based on the supervised learning technique of support vector machines (SVM), which allows us to take into account this asymmetry in our regression of the fair price on the bid-ask spreads quoted continuously on the options markets. The last part of this thesis is devoted to the empirical application of our nonparametric method and to the interpretation of the extracted information. First, we explain how Padé's multi-point approximants can be implemented on a set of simulated and noiseless option prices. We then use real data to show that there is a clear relationship between the implied volatility computed from the risk-neutral density and the realized volatility of the underlying asset in the short run. However, the relationship between the higher order moments extracted from option prices (in other words, the skewness and kurtosis implied by the risk-neutral density) and those that can be computed directly from the realized returns of the underlying asset does not seem to be as obvious or even non-existent.

This thesis focuses on the study of the long memory of the volatility of stock returns. In the first part, we provide an interpretation of long memory in terms of agents' behavior thanks to a long memory volatility model whose parameters are related to the heterogeneous behaviors of agents that can be rational or boundedly rational. We determine theoretically the conditions necessary to obtain long memory. We then calibrate our model on the basis of daily realized volatility series of US mid and large cap stocks and observe the change in agents' behavior between the period preceding the bursting of the internet bubble and the one following it. The second part is devoted to the consideration of long memory in portfolio management. We start by proposing a model of portfolio choice with stochastic volatility in which the dynamics of log-volatility is characterized by an Ornstein-Uhlenbeck process. We show that increasing the level of uncertainty about future volatility induces a revision of the consumption and investment plan. Then, in a second model, we introduce the long memory thanks to the fractional Brownian motion. This has the consequence of transposing the economic system from a Markovian framework to a non-Markovian framework. We then provide a new resolution method based on the Monte Carlo technique. Then, we show the importance of modeling volatility correctly and warn the portfolio manager against model specification errors.

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