The Laplace mixture distribution for stock share returns is derived from conditional N(0,x2) distribution The conditioning variable, x2, is assumed to be an exponentially distributed random variable This offers a natural stochastic interpretation of the risk involved with the stock share Maximum likelihood estimates for returns of the twenty most traded shares and the aggregate index of the Helsinki stock market in late 1980's do not reject the Laplace distriburion model The results extend to returns over longer periods than one day

A Model for Stock Return Distribution

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.

The role of oil futures intraday information on predicting US stock market volatility

COVID-19 and asymmetric volatility spillovers across global stock markets

Correlations and volatility spillovers between China and Southeast Asian stock markets

Do higher-order realized moments matter for cryptocurrency returns?

An important aspect of increased international financial integration is the associated increase in volatility spillover. Analyzing volatility spillovers from advanced economies to emerging economies is clearly important for portfolio investment and risk management. This paper investigates volatility spillovers from the US equity market to stock markets of ASEAN economies. We use the augmented EGARCH model with the ICSS algorithm to control for the excessive volatility break over an extended period. We document a significant volatility spillover from the US to ASEAN equity markets and this finding is vital to investors. The paper has strong practical importance because an accurate prediction of volatility spillovers in international equity markets is crucial for mitigating portfolio risk.

Modelling volatility spillovers from the US equity market to ASEAN stock markets

Government cost and firm value: Evidence from Vietnam

This article examines the role of co-skewness and co-kurtosis in explaining portfolio excess returns utilizing time-series and Fama–Macbeth cross-sectional regression methods in the context of an emerging market. The sample consists of listed firms in Vietnam stock market covering the period from September 2011 to December 2016. This paper reports that co-skewness and co-kurtosis are not important in explaining stock returns in Vietnam stock market. More importantly, we find that market risk premium is the most important factor while other popular factors such as SMB, HML and UMD have minor impact on stock returns. This finding is crucial in identifying factors significantly influencing stock returns in emerging equity markets. This paper also supports the proposition that findings from advanced markets might not be able to generalize into the context of emerging markets. The finding has direct implications for portfolio analysis and risk management.

Thi Tuan Anh Tran

Papers

Modelling volatility spillovers from the US equity market to ASEAN stock markets

Government cost and firm value: Evidence from Vietnam

Higher-order comoments and asset returns: evidence from emerging equity markets