Alex YiHou Huang

Analysis of decision making factors for equity investment by DEMATEL and Analytic Network Process

Existing methodologies of equity investment, such as fundamental analysis, technical analysis, and institutional investor analysis, explore important factors of stock price behaviors. However, the interdependent relationships of the key factors have not yet been fully studied. This paper provides the first analysis on the interactive relationships among the factors in incorporating the methods of Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). The empirical results show that factors from the existing analytical methodologies have significant interactive and self-feedback dynamics. Among the key factors, profitability is the most important one affecting investment decision, followed by growth and trading volume. In addition, due to the complexity of the ANP, this study proposes a new methodology to simplify the process, and empirical evidences indicate that the approach is effective and efficient.

Price Discovery between Sovereign Credit Default Swaps and Bond Yield Spreads of Emerging Markets

Given the vast growth in credit default swap (CDS) market over the last few years, a dramatic improvement is projected in pricing discovery of sovereign CDS as well as its interaction with the underlying bond markets. In this article, a recent comprehensive sample of 20 sovereign CDS spreads, along with their underlying bonds, is examined for the pricing equilibrium for emerging markets. On the basis of the tests of vector error correction model and Granger causality, and by comparing with prior studies, this article finds that sovereign CDS market has served as more significant tool in measuring sovereign credit risk than before.

Forecast of value at risk for equity indices: an analysis from developed and emerging markets

Purpose - The purpose of this paper is to compare the performance of commonly used value at risk (VaR) estimation methods for equity indices from both developed countries and emerging markets. Design/methodology/approach - In addition to traditional time-series models, this paper examines the recently developed nonparametric kernel estimator (KE) approach to predicting VaR. KE methods model tail behaviors directly and independently of the overall return distribution, so are better able to take into account recent extreme shocks. Findings - The paper compares the performance and reliability of five major VaR methodologies, using more than 26 years of return data on 37 equity indices. Through back-testing of the resulting models on a moving window and likelihood ratio tests, it shows that KE models produce remarkably good VaR estimates and outperform the other common methods. Practical implications - Financial assets are known to have irregular return patterns; not only the volatility but also the distributions themselves vary over time. This analysis demonstrates that a nonparametric approach (the KE method) can generate reliable VaR estimates and accurately capture the downside risk. Originality/value - The paper evaluates the performance of several common VaR estimation approaches using a comprehensive sample of empirical data. The paper also reveals that kernel estimation methods can achieve remarkably reliable VaR forecasts. A detailed and complete investigation of nonparametric estimation methods will therefore significantly contribute to the understanding of the VaR estimation processes.

A value-at-risk approach with kernel estimator

This article proposes an alternative approach of Value-at-Risk (VaR) estimation. Financial assets are known to have irregular return patterns; not only the volatility but also the distribution functions themselves may vary with time. Therefore, traditional time-series models of VaR estimation assuming constant and specific distribution are often unreliable. The study addresses the issue and employs the nonparametric kernel estimator technique directly on the tail distributions of financial assets to produce VaR estimates. Various key methodologies of VaR estimation are briefly discussed and compared. The empirical study utilizing a sample of stocks and stock indices for almost 14 years data shows that the proposed approach outperforms other existing methods.

The Impact of Major Events from the Recent FinancialCrisis on Credit Default Swaps

This study explores the impact of key events from the recent financial crisis on credit default swaps (CDS). We show that shocks from the CDS spread significantly coincide with major credit events, and the magnitudes of shocks are greater for negative events than for positive events. The CDS spreads of the financial industry jump prior to the occurrence of events. In the beginning of the crisis, competitive effects for the nonfinancial industry are present, and when the series of credit events continues, such competitive effects turn into contagion effects. The impact levels of the effects vary across sample firms with different industrial characteristics.

Volatility forecasting by quantile regression

Quantile regression allows one to predict the volatility of time series without assuming an explicit form for the underlying distribution. Financial assets are known to have irregular return patterns; not only the volatility but also the distribution functions themselves may vary with time, so traditional time series models are often unreliable. This study presents a new approach to volatility forecasting by quantile regression utilizing a uniformly spaced series of estimated quantiles. The proposed method provides much more complete information on the underlying distribution, without recourse to an assumed functional form. Based on an empirical study of seven stock indices, using 16 years of daily return data, the proposed approach produces better volatility forecasts for six of the seven indices.

Volatility forecasting in emerging markets with application of stochastic volatility model

The volatility of financial asset returns is a key variable in risk management and derivative pricing. The behaviours of emerging equity markets are now significant to global economies. This research examines the performance of five popular categories of volatility forecasting models on 31 emerging and developed stock indices with data series comprising recent 7 years. A modification in estimation processes of the Stochastic Volatility Model (SVM) is proposed. The empirical analysis shows that the equity markets of emerging markets are more volatile and difficult to model than those of developed countries. The SVM performs well in both settings, and has a clear advantage in developed markets.