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in Thailand Derivative Market during the Year 2011-2015

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* Master’s Degree Students, Financial Management Major, Faculty of Business Adminsitration, Rajamangala University of Technology Phra Nakhon

** Lecturer, Faculty of Business Adminsitration, Rajamangala University of Technology Phra Nakhon

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Abstract

This study examined Factors Affecting Price’s Fluctuation of the SET50 Index Futures in Thailand Derivative Market during the Year 2011 – 2015. The objective was to study all of the factors affecting SET50 Index Futures’s fluctuation. According to daily closing price data of SET50 Index Future used for computing continuous return, the period before the maturity and the volume of SET50 Index Futures were calculated the fluctuation by using GARCH (1,1).

The results show that the data and fluctuation in the past affected the data and fluctuation in the present by the same direction, considered by the coefficient in ARCH and GARCH term of GARCH Model (1,1). The period before the maturity affected the return rate by the opposite direction, which means the return rates had much more fluctuation when the period of time closed to the contract maturity. It showed that the coefficient of the period before the maturity was quite less than the one of ARCH and GARCH. Therefore, the fluctuation of return rate in the present was possibly depended on data and the period of fluctuation in the past more than the period before the maturity.

The remainder of the study can give information to the investors that they should be concerned not only about data and fluctuation in the past but the period before the maturity of SET50 index futures, which may affect the forecast of the SET50 index futures price for Hedge management. Thailand Future Exchange should be considered to adjust proper risk margin in accordance with fluctuation rate in a period of time.

Key words: Fluctuation of the SET50 Index Futures, Thailand Derivatives Market. The SET50 Index Futures řšœÎµ

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ܪ_଴ : Homoscedasticity ܪ_ଵ: Heteroskedasticity

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ܪ_଴ǣ ߩ ൌ Ͳ ܪ_ଵǣ ߩ ് Ͳ

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114 •Ÿ‘˜Ÿ‘wŸ‘‹žÖ¢«“ÝwŸ‘}ž†wŸ‘ ™Ÿ•¡‰Ÿ“ž™Ÿ˜Ÿ‘zŸ

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ܪ_଴: X ŤnŗoÁžÈœ­µÁ®˜»…°ŠY (X does not Granger Cause Y) ܪ_ଵ: X ÁžÈœ­µÁ®˜»…°ŠY (X does Granger Cause X) ¨³ ܪ_଴ : YŤnŗoÁžÈœ­µÁ®˜»…°ŠX (Y does not Granger Cause X)

ܪ_ଵ: Y ÁžÈœ­µÁ®˜»…°ŠX (Y does Granger Cause X)

Ž¹ÉŠ™oµŸ¨„µ¦š—­°­¤¤˜·“µœ—´Š„¨nµª¥°¤¦´ܪ_଴ ­—Šªnµ˜´ªÂž¦š¸Éš—­°Å¤n¤¸‡ªµ¤­´¤¡´œ›r¦³®ªnµŠ„´œ

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ܴ_௧ ൌ ߙ_଴൅ ߚ_ଵܴ_௧ିଵ ൅ ߝ_௧

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ߙ_଴ šœ ‡nµ­´¤ž¦³­·š›·Í…°Š‡nµ‡Šš¸É

ߙ_௜ šœ ‡nµ¡µ¦µ¤·Á˜°¦r…°Š…o°¤¼¨Á„¸É¥ª„´‡ªµ¤Ÿ´œŸªœÄœ°—¸˜

ߚ_௜ šœ ‡nµ­´¤ž¦³­·š›·Í…°Š˜´ªÂž¦°·­¦³ ߝ_௧ šœ ‡nµ‡ªµ¤‡¨µ—Á‡¨ºÉ°œ

݄_௧ šœ ‡nµ‡ªµ¤Âž¦ž¦ªœÂ¤¸ÁŠºÉ°œÅ… ÁžÈœ˜´ªÂšœ‡ªµ¤Ÿ´œŸªœ…°Š

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ߝ_௧ିଵ^ଶ šœ ‡nµ‡ªµ¤Âž¦ž¦ªœš¸É‡¨µ—Á‡¨ºÉ°œÄœ°—¸˜(ARCH TERM)

݄_௧ିଵ šœ ‡nµ‡ªµ¤Âž¦ž¦ªœÄœ°—¸˜(GARCH TERM)

ܾ_ଵ šœ ‡nµ­´¤ž¦³­·š›·Í…°Š¦³¥³Áª¨µ„n°œ‡¦„ε®œ—

݉_௧ šœ ¦³¥³Áª¨µ„n°œ‡¦„ε®œ—…°Š­´µ

ܾ_ଶ šœ ‡nµ­´¤ž¦³­·š›·Í…°Šž¦·žž ¤µ–„µ¦Žº Ê°…µ¥…°Š­´µ ݍ_௧ šœ ž¦·¤µ–„µ¦Žº Ê°…µ¥…°Š­´µ

şŸ¨„µ¦«¹„¬µ

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•Ÿ‘˜Ÿ‘wŸ‘‹žÖ¢«“ÝwŸ‘}ž†wŸ‘ ™Ÿ•¡‰Ÿ“ž™Ÿ˜Ÿ‘zŸ 115

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Maximum 0.0693 241,942 300

Minimum -0.0641 0 1

Std.Dev 0.0131 29,946.62 57.7107

Skewness -0.1596 2.8262 3.6870

Kurtosis 5.9144 12.3933 15.6199

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11 •Ÿ‘˜Ÿ‘wŸ‘‹žÖ¢«“ÝwŸ‘}ž†wŸ‘ ™Ÿ•¡‰Ÿ“ž™Ÿ˜Ÿ‘zŸ

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‡ªµ¤ÁºÉ°¤´Éœ¦o°¥¨³ 95

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Coefficient Prob

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ARCH (1) 0.0796 0.0000***

GARCH (1) 0.9129 0.0000***

DAY -2.57E-08 0.0140***

VOLUME -1.13E-11 0.5865

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Q(36)

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27.664 24.439

0.839 0.928 LM ARCH Test

F-statistic 0.9714 0.3245

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Granger Causality Tests

RETURN does not Granger Cause DAY

F-statistic 0.2029 0.8164

DAY does not Granger Cause RETURN

F-statistic 3.1668 0.0425***

VOLUME does not Granger Cause RETURN

F-statistic 0.1242 0.8164

RETURN does not Granger Cause Volume

F-statistic 1.1993 0.3017

VOLUME does not Granger Cause DAY

F-statistic 0.9580 0.3840

DAY does not Granger Cause Volume

F-statistic 0.0771 0.9258

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°œ—r¡¨´¨··ÉŠ

Anderson, Ronald W. & Jean-Pierre Danthine. (1983).The Time Pattern of Hedging and the Volatility of Futures Prices. Review of Economic Studies, 50 (2), 249-266.

Barucci & Roberto R.(2002). On measuring volatility and the GARCH forecasting performance. Journal of International Financial Markets, Institutions and Money, 12, 183-200.

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Feng, W., & Chuan-zhe, L. (2008).Determinants of the Volatility of Futures Markets Price Returns: The Case of Chinese Wheat Futures.International Conference on Management Science & Engineering (15th).

Long Beach, CA.

Samuelson, P. A. (1965). Proof That Properly Anticipated Prices Fluctuate Randomly. Industrial Management Review, 6 (2), 41-49.