臺灣博碩士論文加值系統

本研究主要兩個研究目的在於檢視不同到期日選擇權價性之間之影響，並且找出機構法人對於選擇權價性之影響。為了達成此目的，本研究使用累積15分鐘之選擇權交易量期日內時間序列資料。在本研究中，選擇權價性依照契約到期日的不同分為近月、次近月以及遠月。交易人型態則分為國內法人、散戶以及外資三種類型。
本研究使用向量自我迴歸與多變量GARCH兩個模型進行不同到期日之選擇權價性之分析。實證結果顯示，交易選擇權遠月契約不會影響選擇權近月契約。交易選擇權遠月契約之投資人是為了預測未來的趨勢而非短期之利潤。另一方面，選擇權近月契約之係數不論正或負，選擇權近月契約對於其他兩種契約皆有顯著之影響。這顯示出交易短天期契約會影響長天期契約之價格進而改變交易人的交易行為。此外，在不同交易人之交易量為多變量GARCH模型中之外生變數後，散戶交易量之係數在變異數和共變異數方程式為呈現負值。然而另外兩種交易人型態則呈現相反之影響，其中國內法人其交易量係數為正，而外資其交易量係數則為負。換句話說，國內法人之交易行為會擴大選擇權市場之波動，而外資之交易行為則會穩定選擇權市場之波動。國內法人與外資的差異在於資訊的持有程度，並且隱喻著散戶的交易行為會和外資的交易行為一致。

The two main goals of this study are to examine the relationship between different time-expiration options moneyness with intraday data and to find out the impact of institutional investors on the options moneyness. To achieve this purpose, this study adopts the intraday time-series data which the trading volume is accumulated within fifteen minutes frequency. In this study, the options moneyness data can be classified by different expiration date into nearby, second-nearby and deferred contracts. The types of traders are divided into three categories, including domestic institutional investors, individual investors and foreign investors.
The method that this study uses to analyze the relationships between options moneyness of different expiration date is vector autoregression (VAR) model and the multivariate GARCH (MGARCH) model. The empirical results of this study imply that the behavior of trading options of deferred contracts would not affect to the options of nearby contracts. Trading deferred contracts for investors is one way to expect the future trend rather than the short-term profits. On the other hand, the moneyness of nearby contracts exist a significant influences to two other types of options moneyness in spite of the coefficients of moneyness of nearby contracts are positive or negative. It represents that the trading short-dated contracts affects the option price of long-dated contracts and then to change the investors trading behavior. In addition, after incorporating the trading volume of different types of traders as exogenous variables in MGARCH model, the trading volume coefficients of individual investors are negative in the variance and covariance equations, but the other two types of traders which are domestic institutional investors and foreign investors exhibit opposite influences with positive trading volume coefficient of domestic institutional investors and negative trading volume coefficient of foreign investors. In other words, the trading behavior of domestic institutional investors spreads the volatility of options market while the trading behavior of foreign investors steadies the volatility of options market. The difference of domestic institutional investors and foreign investors results from the information holding by them and implies that the trading behavior of individual investors may be consistent with the foreign investors.

Contents
1. Introduction 1
1.1 Background 1
1.2 Motivation 3
2. Literature review 6
2.1 The influence in options market 6
2.2 The method to analyze time serious data 9
3. Methodology 11
3.1 Moneyness 11
3.2 Vector autoregression 12
3.3 The GARCH model process 13
3.3.1 ARCH model 13
3.3.2 Univariate GARCH model 14
3.3.3 Multivariate GARCH model 15
4. Empirical Results 21
4.1 Data and description statistics 21
4.1.1 TAIFEX market condition 21
4.1.2 Data processing 22
4.1.3 Descriptive statistics 23
4.1.4 Unit-Root test 24
4.2 Vector Autoregression Analysis 27
4.3 Granger causality Analysis 32
4.4 Impulse Responses Analysis 34
4.5 Variance Decomposition Analysis 40
4.6 Multivariate GARCH Analysis 44
4.6.1 MGARCH analysis without exogenous 44
4.6.2 MGARCH analysis with exogenous 51
5. Conclusion 63
Reference 65



Figures
Figure 1-1 Global futures and options trading 2
Figure 1-2 Global listed derivatives volume by region 2
Figure 1-3 The total trading volume on options contracts in TAIFEX 3
Figure 1-4 The trading volume of different type of traders. 3
Figure 4-1 Impulse response – Call options moneyness with buy side 37
Figure 4-2 Impulse response – Put options moneyness with buy side 39
Figure 4-3 Variance volatility of nearby contracts moneyness with call options 57
Figure 4-4 Variance volatility of second-nearby contracts moneyness with call options 57
Figure 4-5 Variance volatility of deferred contracts moneyness with call options 58
Figure 4-6 Variance volatility of nearby contracts moneyness with put options 61
Figure 4-7 Variance volatility of second-nearby contracts moneyness with put options 61
Figure 4-8 Variance volatility of deferred contracts moneyness with put options 62



Tables
Table 1 Descriptive statistics – Call/Put options moneyness with Buy side 25
Table 2 Unit Root test – Call/Put options moneyness with Buy side 26
Table 3 Lag criterion – Call/Put options moneyness with Buy side 29
Table 4 Vector autoregression estimates – Call options with Buy side 30
Table 5 Vector autoregression estimates – Put options with Buy side 31
Table 6 VAR Granger Causality – Call/Put options moneyness 33
Table 7 Impulse response – Call options moneyness with Buy side 36
Table 8 Impulse response – Put options moneyness with Buy side 38
Table 9 Variance Decomposition – Call options moneyness with Buy side 42
Table 10 Variance Decomposition – Put options moneyness with Buy side 43
Table 11 Mean equation of MGARCH estimation – Call options with buy side 47
Table 12 Variance equation of MGARCH estimation – Call options with buy side 48
Table 13 Mean equation of MGARCH estimation – Put options with buy side 49
Table 14 Variance equation of MGARCH estimation – Put options with buy side 50
Table 15 Mean equation of MGARCH estimation with exogenous – Call options with buy side 55
Table 16 Variance equation of MGARCH estimation with exogenous– Call options with buy side 56
Table 17 Mean equation of MGARCH estimation with exogenous – Put options with buy side 59
Table 18 Variance equation of MGARCH estimation with exogenous– Put options with buy side 60

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