Violence Exposure and Welfare over Time: Evidence from the Burundi Civil War

We investigate the relationship between exposure to conflict and poverty dynamics over time, using original three-waves panel data for Burundi which tracked individuals and reported local-level violence exposure from 1998 to 2012. Firstly, the data reveal that headcount poverty has not changed since 1998 while we observe multiple transitions into and out of poverty. Moreover, households exposed to the war exhibit a lower level of welfare than non-exposed households, with the difference between the two groups predicted to remain significant at least until 2017, i.e. twelve years after the conflict termination. The correlation between violence exposure and deprivation over time is confirmed in a household-level panel setting. Secondly, our empirical investigation shows how violence exposure over different time spans interacts with households' subsequent welfare. Our analysis of the determinants of households' likelihood to switch poverty status (i.e. to fall into poverty or escape poverty) combined with quantile regressions suggest that, (i) exposure during the first phase of the conflict has affected the entire distribution, and (ii) exposure during the second phase of the conflict has mostly affected the upper tail of the distribution: initially non-poor households have a higher propensity to fall into poverty while initially poor households see their propensity to pull through only slightly decrease with recent exposure to violence. Although not directly testable with the data at hand, these results are consistent with the changing nature of violence in the course of the Burundi civil war, from relatively more labour-destructive to relatively more capital-destructive.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.