Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies

Dedi, Lidija; Yavas, Burhan F.

doi:10.1080/23322039.2016.1266788

Cited by 55 publications

(49 citation statements)

References 34 publications

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“…Table 4, the coefficient is used to calculate the reaction of the volatility at any given time against the market; so, volatility is sensitive to market events (Alexander, 2001). According to Dedi (2016), the ARCH parameter of α usually ranges from 0.05 (for a relatively stable market) and around 0.1 (for an anxious market). In other words, α measures the extent of the shock on the feedback generated volatility today following period, and αβ + measures the rate at which these effects die over time.…”

Section: Resultsmentioning

confidence: 99%

“…The sum of alpha α and beta β is less from one for all countries, and this shows that the process of return is stationary (Alexander, 2008). According to Dedi (2016), long term (cumulative) effect of past shocks on returns is measured by the GARCH parameter , β which usually ranges between 0.85 and 0.98. Thus, Table 4 shows that Singapore has the highest persistence and Thailand is the lowest.…”

Section: Based On the Estimation Of Garch Model Inmentioning

confidence: 99%

“…According to Dedi (2016), to see the size of the volatility persistence, the sum of the ARCH and GARCH coefficient values must be used. If that number is closer to one that means the shock effect fades very slowly.…”

Section: Based On the Estimation Of Garch Model Inmentioning

confidence: 99%

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The volatility model of the ASEAN Stock Indexes

Singagerda

Septarina

Sanusi

2019

Investment Management and Financial Innovations

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This research study examines the characteristics of the Association of Southeast Asian Nations (ASEAN) volatility of stock indexes. The following models are used in this research: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH), Glosten Jaganathan Runkle Generalized Autoregressive Conditional Heteroscedasticity (GJR-GARCH), and Multifractal Model of Asset Return (MMAR). The research also used the data from the ASEAN country members’ (the Philippines, Indonesia, Malaysia, Singapore, and Thailand) stock indexes for the period from January 2002 until 31 January 2016 to determine the suitable model.Meanwhile, the results of the MMAR parameter showed that the returns of the countries have a characteristic called long-term memory. The authors found that the scaling exponents are associated with the characteristics of the specific markets including the ASEAN member countries and can be used to differentiate markets in their stage of development. Finally, the simulated data are compared with the original data by scaling function where most of the stock markets of the selected ASEAN countries have long-term memory with the scaling behavior of information asymmetry. Some of the countries such as the Philippines and Indonesia have their own alternative models using GARCH and EGARCH due to the possibility of leverage. Generally, MMAR is the best model for use in ASEAN market, because this model considered Hurst exponent as a parameter of long-term memory that indicates persistent behavior.

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Section: Resultsmentioning

confidence: 99%

Section: Based On the Estimation Of Garch Model Inmentioning

confidence: 99%

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The volatility model of the ASEAN Stock Indexes

Singagerda

Septarina

Sanusi

2019

Investment Management and Financial Innovations

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“…The results indicate bidirectional mean and volatility spillovers between two rivals and their ally (the US), but only in the short run. Dedi and Yavas (2016) examine the linkages between equity market return and volatility spillovers and the corresponding country's market (Germany, the UK, China, Russia and Turkey). The results indicate significant co-movement of returns among the sample, with Russia and Turkey exhibiting the highest volatility, while the UK and China have Financial Integration of the lowest volatility spillover.…”

Section: Introductionmentioning

confidence: 99%

“… See Alikhanov (2013),Carsamer (2016),Choudhry (2004),Peng et al (2017),Dedi and Yavas (2016) andJoshi (2011).…”

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confidence: 99%

The Paradigm Shift in the Pakistan Stock Exchange’s Financial Integration Post-FTA and CPEC

Wahid

Mumtaz²

2018

LJE

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This paper examines whether regional connectivity causes return and volatility spillovers and the co-movement of stock exchanges to shift from international to regional markets. Using the China-Pakistan free trade agreement (FTA) of 2006 and the China-Pakistan Economic Corridor (CPEC) agreement to represent events of regional connectivity, we test this proposition based on data for two regional stock exchanges (the Pakistan Stock Exchange and Shenzhen Stock Exchange) and two global markets (the FTSE 100 and Nasdaq). We divide the convergence and co-integration of the stock markets into three phases: overall sample (2001–17), pre-FTA and post-FTA, and pre-CPEC and post-CPEC. Applying a GARCH (1, 1) model, co-integration, Granger causality and seasonality, we find that regional connectivity causes return and volatility spillovers and co-movements in the Pakistan Stock Exchange to shift from international markets to regional markets.

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Dynamic return and volatility spillovers among S&P 500, crude oil, and gold

Balcılar

Özdemir

2019

Int. J Fin Econ

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This paper examines the return and volatility spillover effects among S&P 500, crude oil and gold by employing the spillover index of Diebold and Yilmaz (2012). Monthly realized volatility and return series covering the period from January 1986 to August 2018 are used to examine the return and volatility spillovers. Our findings indicate a bi-directional return and volatility spillover among these assets. The full sample empirical evidence is consistent with the structure in which oil plays a central role in the information transmission mechanism. The role of oil and gold as a safe haven has changed over time in financial and non-financial economic turbulence time-span. Commodity market financialization has decreased the effectiveness of adding commodities to portfolios after 2002.

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Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies

Abstract: This paper investigates linkages among equity market returns and volatility spillovers in the following countries: Germany,

Cited by 55 publications

References 34 publications

The volatility model of the ASEAN Stock Indexes

The volatility model of the ASEAN Stock Indexes

The Paradigm Shift in the Pakistan Stock Exchange’s Financial Integration Post-FTA and CPEC

Dynamic return and volatility spillovers among S&P 500, crude oil, and gold

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