Speed of Price Adjustment towards Market Efficiency: Evidence from Emerging Countries

Abstract

The speed with which stock markets adjust to information and news flow into asset prices is of importance to investors, regulators and policymakers. In this article, we provide a simple and uniform empirical framework involving the use of a volatility measure to compare the speeds of adjustment in index prices in response to all available market information. The stock indices of 23 major emerging economies are compared with 10 mature stock indices from developed countries with reference to the speed of their price adjustments. We find that the index prices of developed countries adjust faster when compared to those of emerging countries. Our findings are independent of any GARCH specification and are also robust to potential mistakes in the model specification because we make use of a fully empirical bootstrap procedure to compute the standard errors. We also rank the countries in terms of the speed of index price adjustment. The results show that the random walk effect is generic and exists in all price indices.

Excess Volatility in Bitcoin: Extreme Value Volatility Estimation

“…The first set of EV volatility estimators are known as the ‘method of moments’ (MM) estimators and the other set of estimators are ‘maximum likelihood’ (ML) estimators. The ML estimators are considered less advantageous in comparison with the MM estimators due to the complexity of the joint density functions and for being non-expressive in the closed form (Kayal & Maheswaran, 2018). Among the different MM estimators, the EV estimator RS suggested by Rogers and Satchell (1991) is appealing because it estimates the unconditional variance and is unbiased for any value of drift.…”

“…Then we compare it with open-to-close volatility which reflects the price movements over the full trading hours. In the absence of overreactions and instant price adjustment, both measures of volatility will not differ and their ratio should be unity under perfect condition (Kayal & Maheswaran, 2018). If the ratio goes above unity, then the volatility reflects a significant sign of overreaction in price changes of Bitcoin.…”

Demographic Analysis of Residents’ Support for Tourism Development in the UAE: A Bayesian Structural Equation Modeling Multigroup Approach

“…Liao, Lin, and Hsieh (2019) suggest that future research could reveal more relevant factors that exert an influence on indigenous tourism satisfaction, so as to enrich the studies on indigenous tourism development from the perspective of residents. Responding to calls for further research into the perspective of residents on the influence of tourism in different countries (Pham and Vogt 2019), as well as recommendations for future research to account for further classifications of residents, such as ethnicity (Asian and Western, Arabs and non-Arabs, or Muslim and non-Muslim), the authors chose the UAE (Kayal and Maheswaran 2018), an emerging country (Kayal and Maheswaran 2018) with unique traits outlined below, and more specifically two cities that are recognized as tourist destinations: Abu Dhabi, the capital city of the UAE, and Dubai, one of the world’s most prestigious and popular tourist attractions. In addition, the UAE boasts an ideal mix of locals/expatriates, allowing for cross-group comparison.…”

The reliability of geometric Brownian motion forecasts of S&P500 index values

“…Using GBM, Liu et al (2020) and Tie et al (2018) study optimal pairs trading rules. Using Brownian motion conditions, Kayal and Maheswaran (2018) and Kayal and Mondal (2020) study the speed of price adjustments in emerging countries and the Indian stock market, respectively. Chang (2020) uses a variant of the GBM approach to investigate high-frequency time-series features of log returns and volatility of Chinese stocks usually found to be almost symmetric, highly leptokurtic, and non-Gaussian.…”