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.

The Vietnamese Corporate Bond Market An Early Exploration into the 1992-1999 Period

This paper summarizes the situation of corporate bonds in Vietnam for the period 1992-1999. Corporate bonds are new in the transitional economy, but the capital shortage and operational inefficiency of the banking sector and financial system would likely drive the bond market up in the future. The paper also discusses some conditions for the Vietnamese bond market to further develop, based on the facts and observation.

Corporate Bond Market in the Transition Economy of Vietnam, 1990-2010

Corporate bond appeared early in 1992-1994 in Vietnamese capital markets. However, it is still not popular to both business sector and academic circle. This paper explores different dimensions of Vietnamese corporate bond market using a unique, and perhaps, most complete dataset. State not only intervenes in the bond markets with its powerful budget and policies but also competes directly with enterprises. The dominance of SOEs and large corporations also prevents SMEs from this debt financing vehicle. Whenever a convertible term is available, bondholders are more willing to accept lower fixed income payoff. But they would not likely stick to it. On one hand, prospective bondholders could value the holdings of equity when realized favorably ex ante. On the other hand, the applicable coupon rate for such bond could turn out negative inflationadjusted payoff when tight monetary policy is exercised and the corresponding equity holding turns out valueless, ex post. Given the weak primary market and virtually nonexistent secondary market, the corporate bond market in Vietnam reflects our perception of the relationship-based and rent-seeking behavior in the financial markets. For the corporate bonds to really work, they critically need a higher level of liquidity to become truly tradable financial assets.