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.

Operational scales, sources of finance, and firms’ performance: evidence from Vietnamese longitudinal data

This study investigates a longitudinal dataset consisting of financial and operational data from 37 listed companies listed on Vietnamese stock market, covering the period 2004-13. By performing three main types of regression analysis - pooled OLS, fixed-effect and random-effect regressions - the investigation finds mixed results on the relationships between operational scales, sources of finance and firms' performance, depending on the choice of analytical model and use of independent/dependent variables. In most situation, fixed-effect models appear to be preferable, providing for reasonably consistent results. Toward the end, the paper offers some further explanation about the obtained insights, which reflect the nature of a business environment of a transition economy and an emerging market.

Empirical Evidence on Relationships between Ex Ante Innovation Pursuit and Post-M&A Performance in the Vietnamese M&A Industry, 2005-2012

This research aims to communicate new results of empirical investigations to learn about the relationship between determination of controlling an acquired firm’s capital, assets and brand versus its capability of innovation and ex post performance of the rising Vietnamese M&A industry in the 2005-2012 period. The analysis employs a categorical data sample, consisting of 212 M&A cases reported by various information sources, and performs a number of logistic regressions with significant results as follows. Firstly, the overall relationship between pre-M&A pursuit’s determination on acquiring resources and performance of the post-M&A performance is found significant. There exist profound effects of a ‘size matters’ strategy in M&A ex post performance. When there is an overwhelming ‘resources acquiring’ strategy, the innovation factor’s explanatory power becomes negligible. Secondly, for negative performance of post-M&A operations, the emphasis on both capital base and asset size, and the brand value at the time of the M&A pursuit is the major explanation in the post-M&A period. So does the absence of innovation as a goal in the pre-M&A period. These two insights together are useful in careful M&A planning. Lastly, expensive pre-M&A expenditures tend to adversely affect the post-M&A performance. As a general conclusion, this study shows that innovation can be an important factor to pursue in M&A transitions, together with the need to emphasize and find capable and willing human capital, rather than a capital base (equity or debt) and existing values of the acquired brands.

Be rich or don’t be sick: Estimating Vietnamese patients’ risk of falling into destitution

This paper represents the first research attempt to estimate the probabilities for Vietnamese patients to fall into destitution facing financial burdens occurring during their curative stay in hospital. The study models the risk against such factors as level of insurance coverage, location of patient, costliness of treatment, among others. The results show that very high probabilities of destitution, approximately 70%, apply to a large group of patients, who are nonresident, poor and ineligible for significant insurance coverage. There is also a probability of 58% that low-income patients who are seriously ill and face higher health care costs would quit their treatment. These facts will put Vietnamese government’s ambitious plan of increasing both universal coverage (UC) to 100% of expenditure and rate of UC beneficiaries to 100% at a serious test. The study also raises issues of asymmetric information and alternative financing options for the poor, who are most exposed to risk of destitution, following market-based health care reforms.

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.