Essays in Financial Econometrics : Interlinked assets and High-Frequency Data.

Summary Institutional changes in the regulation of financial markets have increased the number of markets and the simultaneous listing of assets on several markets. The prices of a security on these exchanges or of a security and its derivatives are linked by arbitrage activities. In these "informationally connected" market settings, it is of interest to regulators, investors and researchers to understand how each market contributes to the dynamics of fundamental value. This thesis develops new tools to measure the contribution, relative to frequency, of each market to price formation and volatility formation. In the first chapter, I show that existing measures of price discovery lead to misleading conclusions when using high frequency data. Due to microstructure noises, they create a confusion between the "velocity" and "noise" dimensions in the information processing. I then propose noise-robust measures that detect "which market is fast" and produce very tight bounds. Using Monte Carlo simulations and Dow Jones stocks sold on the NYSE and NASDAQ, I show that the data corroborate my theoretical conclusions. In the second chapter, I propose a new definition of price discovery by constructing a response function that estimates the permanent impact of market innovation, and I give its asymptotic distribution. This framework breaks new ground by providing testable results for innovation variance-based metrics. I then present an equilibrium model of futures markets at different maturities, and show that it supports my measure: Consistent with the theoretical findings, the measure selects the market with the most participants as dominant. An application on LME metals shows that the 3-month futures contract dominates both the cash market and the 15-month contract. The third chapter introduces a complete continuous-time framework for high-frequency analysis, as the literature exists only in discrete time. It also has advantages over the literature by explicitly dealing with microstructure noise and by incorporating stochastic volatility. An application, made on the four Dow Jones stocks listed on NASDAQ and traded on NYSE, show that NASDAQ dominates the continuous price discovery process. In the fourth chapter, while the literature focuses on prices, I develop a framework to study volatility. This helps answer questions such as: Does futures market volatility contribute more than spot market volatility to the formation of fundamental volatility? I construct a VECM with Stochastic Volatility estimated with MCMC and Bayesian inference. I show that conditional volatilities have a common factor and propose measures of volatility discovery. I apply it to daily data of metal futures and the EuroStoxx50. I find that while price formation takes place in the spot market, volatility discovery takes place in the futures market. In a second part, I construct an analysis framework that exploits High Frequency data and avoids the computational burden of MCMC. I show that the Realized Volatilities are cointegrated and calculate the contribution of the NYSE and NASDAQ to the permanent volatility of the Dow Jones stocks. I obtain that volume volatility is the best determinant of volatility discovery. But the low numbers obtained suggest the existence of other factors.