Policy impacts on Vietnam stock markets: a case of anomalies and disequilibria 2000-2006

Vietnam launched its first-ever stock market, named as Ho Chi Minh City Securities Trading Center (HSTC) on July 20, 2000. This is one of pioneering works on HSTC, which finds empirical evidences for the following: Anomalies of the HSTC stock returns through clusters of limit-hits, limit-hit sequences; Strong herd effect toward extreme positive returns of the market portfolio;The specification of ARMA-GARCH helps capture fairly well issues such as serial correlations and fat-tailed for the stabilized period. By using further information and policy dummy variables, it is justifiable that policy decisions on technicalities of trading can have influential impacts on the move of risk level, through conditional variance behaviors of HSTC stock returns. Policies on trading and disclosure practices have had profound impacts on Vietnam Stock Market (VSM). The over-using of policy tools can harm the market and investing mentality. Price limits become increasingly irrelevant and prevent the market from self-adjusting to equilibrium. These results on VSM have not been reported before in the literature on Vietnam’s financial markets. Given the policy implications, we suggest that the Vietnamese authorities re-think the use of price limit and give more freedom to market participants.

Empirical Evidence of Conditional Heteroskedasticity in Vietnam’s Stock Returns Time Series

This paper confirms presence of GARCH(1,1) effect on stock return time series of Vietnam’s newborn stock market. We performed tests on four different time series, namely market returns (VN-Index), and return series of the first four individual stocks listed on the Vietnamese exchange (the Ho Chi Minh City Securities Trading Center) since August 2000. The results have been quite relevant to previously reported empirical studies on different markets.

Analyses on Gold and US Dollar in Vietnam's Transitional Economy

This paper looks into economic insights offerred by considerations of two important financial markets in Vietnam, gold and USD. In general, the paper focuses on time series properties, mainly returns at different frequencies, and test the weak-form efficient market hypothesis. All the test rejects the efficiency of both gold and foreign exchange markets. All time series exhibit strong serial correlations. ARMA-GARCH specifications appear to have performed well with different time series. In all cases the changing volatility phenomenon is strongly supported through empirical data. An additional test is performed on the daily USD return to try to capture the impacts of Asian financial crisis and daily price limits applicable. No substantial impacts of the Asian crisis and the central bank-devised limits are found to influence the risk level of daily USD return.

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.