GARCH Model Estimation Using Estimated Quadratic Variation

Galbraith, John W.; Zinde‐Walsh, Victoria; Zhu, Jingmei

doi:10.1080/07474938.2014.956629

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…These models have large number of parameters and needs to enforce limitations, and suggested model volatility as undetected element. On the other hand, GARCH family models are quite widely used till now (e.g., Galbraith et al 2015;Bentes 2015;Johnk, Soydemir 2015;Andreou, Werker 2015;etc.). Salinger's study (1992) discusses the appropriate methodology for calculating the impact of events on the value of the shares of the company.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Analysis of Stock Returns and Volatility: Evidence From Asian Stock Markets

Ahmad

Ahmed

Vveinhardt

et al. 2016

Technological and Economic Development of Economy

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The objective of this research isto measure and examine volatilities among important stock markets of Asia and to ascertain a causal relation between volatility and stock returns. For this purpose six markets KSE100 (Karachi, Pakistan), BSE Sensex (Mumbai, India), NIKKEI 225 (Tokyo, Japan), Hang Seng (Hong Kong), Shanghai Stock Exchange (SSE) (Shanghai, China) and KOSPI (Seoul, South Korea) were considered. Stock market indices comprise of daily data from the period January 2002 to December 2009. The graphical representation of time series shows the preliminary examination of stock behaviors. The analysis shows the high correlation and heteroskedastic trend (volatility) among the stock markets in selected time period. After preliminary analysis the formal descriptive method of mean, standard deviation and coefficient of variation have been applied for measuring and ranking purposes. The results show that KOSPI has the highest average annual return of 12.67% and followed by BSE with 11.61%, whereas, KSE 100 has the least annual average returns of 9.31%. The highest volatility coefficient of 3.097 has been observed in Hang Seng (Hong Kong) followed by 2.87 in Nikkei (Tokyo). However, the KSE 100 observed the lowest volatility coefficient of 2.078. Bartlett’s test is applied for the inferential analysis to investigate whether the equality of volatility is the same in each market return. Finally, GARCH (1, 1) model is applied which concludes a significant ARCH (1) and GARCH (1) effects and confirms all markets’ returns are statistically significant since p < 0.01 and their Long Run Average Variances (LRAV) range from 1.52% to 2.54% for KSE100 Index and Shanghai Stock Exchange respectively.

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