Three Essays on Financial Risks Using High Frequency Data.

Summary The general topic of this thesis is financial risk in a context of high frequency data availability, with a particular focus on systemic risk, large portfolio risk and microstructure noise. It is organized in three main chapters. The first chapter proposes a reduced-form, continuous-time model to characterize the propagation of negative idiosyncratic shocks within a set of multiple financial entities. Using a factor model with mutually excited jumps on both prices and volatility, we distinguish different sources of financial shock transmission such as correlation, connectivity and contagion. The estimation strategy is based on the generalized method of moments and takes advantage of the availability of very high frequency data. We use specific model parameters to define weighted networks for the transmission of shocks. Also, we provide new measures of financial system fragility. We construct shock propagation maps, first for some key banks and insurance companies in the US, and then for the nine largest sectors of the US economy. The result is that, beyond common factors, financed shocks propagate through two distinct and complementary channels: prices and volatility. In the second chapter, we develop a new estimator of the realized covolatility matrix, applicable in situations where the number of assets is large and the high-frequency data are contaminated by microstructure noise. Our estimator is based on the assumption of a factor structure of the noise component, distinct from the latent systematic risk factors that characterize the cross-sectional variation in returns.The new estimator provides theoretically more efficient and accurate finite sample estimates, relative to other recent estimation methods. The theoretical and simulation-based results are corroborated by an empirical application related to portfolio allocation and risk minimization involving several hundred individual stocks. The last chapter presents a methodology for estimating microstructure noise characteristics and latent returns in a high-dimensional setting. We rely on factorial assumptions on both the latent returns and the microstructure noise. The procedure is capable of estimating common factor rotations, loading coefficients, and microstructure noise volatilities for a large number of assets. Using the stocks included in the S & P500 over the period from January 2007 to December 2011, we estimate the common factors of the microstructure noise and compare them to some market-wide liquidity measures calculated from real financial variables. The result is that: the first factor is correlated with the average spread and the average number of shares outstanding . the second and third factors are uniquely related to the spread . the fourth and fifth factors vary significantly with the average closing stock price. In addition, the volatilities of the microstructure noise factors are largely explained by the average spread, the average volume, the average number of trades and the average size of those trades.