Price and volatility spillovers in Scandinavian stock markets

Booth, G. Geoffrey; Martikainen, Teppo; Tse, Yiuman

doi:10.1016/s0378-4266(97)00006-x

Cited by 221 publications

(109 citation statements)

References 29 publications

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“…3) Furthermore, the results of this study agree with those of Booth's et al (1997) that the volatility in the four markets indicate that ill news are stronger than good news. The research on the returns and the volatility of secondary effects in the Nordic markets dictate the enactment of a common Nordic stock market, to create the fourth bigger stock market in Europe after the ones of London, Paris and Frankfurt.…”

supporting

confidence: 89%

“…2) The results of this study indicate that every one of these markets are described better from model ARMA(0,1)-GARCH-Μ(1,1) with t-Student distribution, unlike Booth's et al (1997) study which uses the EGARCH asymmetric model, and Hyytinen's (1999) which uses the TGARCH asymmetric model for Sweden and GARCH symmetric models for Finland and Norway.…”

mentioning

confidence: 76%

“…1) The past studies of researching volatility and the relationship between the Nordic markets used data from 1988 to 1994Booth et al (1997, while Hyytinen (1999) used weekly data and only for three markets (Finnish, Norwegian and Swedish market) from 1983 to 1997. The expansion of data in this study and the conclusions analysis are important for investors who want to diversify their portfolios.…”

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confidence: 99%

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Forecasting Volatility Stock Return: Evidence from the Nordic Stock Exchanges

Dritsakis¹,

Savvas²

2017

IJEF

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The purpose of this study is to explore the volatility and secondary effects in the four Nordic stock exchanges of Norway: Oslo Børs Linked all-share index AXLT Denmark: OMX Copenhagen 20, Sweden: OMX Stockholm 30 and Finland: OMX Helsinki 25. Keeping in mind that there is an ARCH effect in the returns of the four stock exchanges, we move on to the evaluation to the evaluation of models ARCH (q), GARCH (p, q) και GARCH-M (p, q). Evaluating the parameters became possible through the use of the maximum likelihood method using the BHHH algorithm of (Berndt et al., 1974) and the three distributions (normal, t-Student, and the Generalized normal distribution GED). The results of this study indicate model ARMA(0,1)-GARCH-Μ(1,1) with t-student distribution as the appropriate one to describe the returns of the all Nordic stock exchanges except that of Sweden, where model ARMA(0,3)-GARCH-Μ(1,1) describes it best. Lastly, for forecasting the models ARMA(0,1)-GARCH-Μ(1,1) and ARMA(0,3)-GARCH-Μ(1,1) of the current stock exchanges we use both the dynamic and static process. The results of this study indicate that the static process forecasts better than the corresponding dynamic.

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confidence: 89%

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confidence: 76%

mentioning

confidence: 99%

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Forecasting Volatility Stock Return: Evidence from the Nordic Stock Exchanges

Dritsakis¹,

Savvas²

2017

IJEF

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“…Considering the volatility spillovers across markets, the important studies in the existing literature are of Hamao, Masulis and Ng (1990), Koutmos and Booth (1995) and Lin, Engle and Ito (1994) for US, UK and Japanese Stock markets and Booth, Martikainen and Tse (1997) and Christofi and Pericli (1999) in other international stock markets. Most studies in the literature have used different variants of GARCH models to study the volatility spillovers between markets.…”

Section: Process Of Volatility Spilloversmentioning

confidence: 99%

An Examination of Price Discovery and Volatility Spillovers of Crude Oil in Globally Linked Commodity Markets

Sehgal¹,

Berlia

Ahmad³

2013

IJEF

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This paper examines the price discovery and volatility spillovers between spot and futures as well as futures prices of three strategically linked oil markets viz., ICE, MCX and NYMEX from 05 February, 2006 to 15 October, 2012. The results confirm the long-run relationship between futures and spot prices in each market, futures prices lead spot prices in the price discovery process. Analysing the futures prices, we find that ICE is the most dominant futures trading platform followed by NYMEX and MCX in price discovery process. Thus, MCX an emerging market platform seems to act like a satellite market vis-à-vis international platforms. The volatility spillover results suggest that there is a long-term spillover from ICE to MCX and from MCX to NYMEX. The volatility information seems to flow from NYMEX to ICE. The GARCH-CCC & DCC model results confirm both cross market and with in market co-movements which become weak during the crisis period and tend to become stronger during the stable period. The study provides relevant implications for policy makers and market traders. The outcome of this study contributes to commodity market literature especially relating to information transmission between strategically linked markets.

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“…Theodossiou and Lee (1993) come up with a multivariate GARCH model to analyze US, UK, Japan, Canada and Germany stock markets, and reported the existence of mean spillovers from the US to other markets. Likewise, Booth, Martikainen, and Tse (1997) employed multivariate GARCH model to explore the volatility spillover dynamics among Scandinavian financial markets. In this article, we apply a multivariate GARCH model with the BEEK framework, which was proposed by R. F. Engle and Kroner (1995).…”

Section: Introductionmentioning

confidence: 99%

Time-varying Return and Volatility Spillover among EAGLEs Stock Markets: A Multivariate GARCH Analysis

Umer¹,

Coşkun²,

Kiracı³

2018

j.financ.econ.res

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This study investigates the presence of return and volatility spillover across EAGLEs stock markets, namely China, India, Indonesia, Russia, Brazil, Turkey and Mexico. A multivariate GARCH DCC and BEKK frameworks are employed by classifying the total sample (i.e. from January 2002 to February 2017) into three sub-periods according to the 2008 Global financial crisis. The result shows a significant and positive spillover effect among stock markets in the pre-crisis and post-crisis periods. The transmission of spillover from external markets intensely influenced by US stock market. Furthermore, strong inter-connection and channel of spread observed among EAGLEs stock market during the post-crisis period.

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Price and volatility spillovers in Scandinavian stock markets

Cited by 221 publications

References 29 publications

Forecasting Volatility Stock Return: Evidence from the Nordic Stock Exchanges

Forecasting Volatility Stock Return: Evidence from the Nordic Stock Exchanges

An Examination of Price Discovery and Volatility Spillovers of Crude Oil in Globally Linked Commodity Markets

Time-varying Return and Volatility Spillover among EAGLEs Stock Markets: A Multivariate GARCH Analysis

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