Output Growth, Inflation and Interest Rate on Stock Return and Volatility: The Predictive Power

Poon, Wai Ching; Tong, Gee-Kok

doi:10.2139/ssrn.1605883

Cited by 1 publication

(2 citation statements)

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“…Earlier studies involving predictability of stock returns and volatility for these three economies have either used univariate models or multivariate models comprising of macroeconomic, financial and international variables as predictors. In this regard, see for example the following recent studies, and the references cited therein: Jarrett (2008), Poon and Tong (2010), Jais et al, (2012), Tarazi and Gallato (2012), Tsai (2012), Zakaria and Shamsuddin (2012), Choi et al, (2013), Gebka and Wohar (2013), and Han et al (2015). Specifically, Jarrett (2008) analyzed the effects of daily dummies, trade volumes and trade values on stock returns for Hong Kong and Japan stock markets.…”

Section: Introduction and Related Literaturementioning

confidence: 99%

“…Specifically, Jarrett (2008) analyzed the effects of daily dummies, trade volumes and trade values on stock returns for Hong Kong and Japan stock markets. Poon and Tong (2010) estimated the effects of output growth, inflation and interest rate on stock return and volatility for Malaysia, India, Korea and the Philippines. Jais et al…”

Section: Introduction and Related Literaturementioning

confidence: 99%

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The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea

Balcılar

Gupta

Kim

et al. 2019

International Review of Economics & Finance

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Highlights• We analyze the predictability of stock return and its volatility of Hong Kong, Malaysia and South Korea • We base on the measure of domestic and global economic policy uncertainties (EPU) • Linear Granger causality tests fail to find evidence of predictability from EPU • A nonparametric causality-in-quantiles test, however, finds strong evidence of causality • Nonparametric test is found to be more robust relative to the standard linear causality test. AbstractThis paper analyzes whether we can predict stock return and its volatility of Hong Kong, Malaysia and South Korea based on measures of domestic and global (China, the European Area, Japan, and the US) economic policy uncertainties (EPU). While, linear Granger causality tests fail to find evidence of predictability, barring the case of South Korean EPU predicting its own stock returns, when we use a nonparametric causality-in-quantiles test, strong evidence of causality is detected from the EPUs for stock return volatility of Malaysia, and both returns and volatility at certain parts of the conditional distributions for South Korea. There is no evidence of predictability from domestic and global EPUs for return and volatility of the Hong Kong stock market. Given the statistical evidence of nonlinearity in our data set, we consider the results from the nonparametric test as more robust relative to the standard linear causality test. * We would like to thank anonymous referees for many helpful comments. However, any remaining errors are solely ours.

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