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Journal articles

Academic literature on the topic 'TAIEX index'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "TAIEX index"

1

Chen, Shen-Yuan, Ching-Chung Lin, Pin-Huang Chou, and Dar-Yeh Hwang. "A Comparison of Hedge Effectiveness and Price Discovery between TAIFEX TAIEX Index Futures and SGX MSCI Taiwan Index Futures." Review of Pacific Basin Financial Markets and Policies 05, no. 02 (2002): 277–300. http://dx.doi.org/10.1142/s0219091502000791.

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This article uses daily data from July 21, 1998 to July 31, 2000 to examine the hedging effectiveness, price behavior, and lead-lag relationship of SGX MSCI Taiwan index futures and TAIFEX TAIEX futures. By applying the Bayesian approach using Gibbs sampler, we find that TAIFEX index futures has a better hedging performance. A variance ratio test reveals that mean reversion and negative correlation of returns exist in SGX index futures. Only TAIFEX TAIEX futures is cointegrated with TAIEX spot. The uni-directional Granger causality between the two futures markets and spot market are from SGX t

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2

Lin, Ching-Chung, Shen-Yuan Chen, Dar-Yeh Hwang, and Chien-Fu Lin. "Does Index Futures Dominate Index Spot? Evidence from Taiwan Market." Review of Pacific Basin Financial Markets and Policies 05, no. 02 (2002): 255–75. http://dx.doi.org/10.1142/s021909150200078x.

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By utilizing vector error correction model (VECM) and EGARCH model, this article uses 5-minute intraday data to examine the interaction of return and volatility between Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the newly introduced TAIEX futures. VECM model shows that there exists bi-directional Granger causality between index spot and index futures markets, but spot market plays a more important role in price discovery. The results of impulse response function and information share indicate that most of the price discovery happens in index spot market. The evidence

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3

Ji, Deng-Yuan, Cheng-Few Lee, and Hsiao-Yin Chen. "Forecast Performance of the Taiwan Weighted Stock Index." Review of Pacific Basin Financial Markets and Policies 18, no. 03 (2015): 1550017. http://dx.doi.org/10.1142/s0219091515500174.

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This research introduces the following to establish a Taiwan Weighted stock index (TAIEX) prediction model: intervention analysis integrated into the ARIMA–GARCH model, error correction model (ECM), intervention analysis integrated into the transfer function model, the simple average combination forecasting model, and the minimum error combination forecasting model. The results show that intervention analysis integrated into the transfer function model yields a more accurate prediction model than ECM and intervention analysis integrated into the ARIMA–GARCH model. The minimum error combination

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4

Huang, Chia-Hsing, and Liang-Chun Ho. "The relationship between the bio-energy concept stocks in Taiwan and the international stock markets." Corporate Ownership and Control 5, no. 4 (2008): 437–43. http://dx.doi.org/10.22495/cocv5i4c5p3.

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This research explores the relationship among the bio-energy company stock index in Taiwan, TAIEX, DJI, Nikkei 225 and SSE composite index for a period from January 1, 2005 to March 11, 2008. Test results indicate two things are noteworthy: 1. Granger causality tests show that the interaction between the bio-energy company stock index in Taiwan and TAIEX is one-way only; however, that between the bio-energy company stock index in Taiwan and DJI is two-way. 2. According to the results of variance decompositions, though TAIEX has the highest explanation power; nevertheless, the explanation stren

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5

Chien, Yung Chieh. "An Empirical Study on Factors Influencing Taiwans Future Basis of Securities Market." Advanced Materials Research 798-799 (September 2013): 869–72. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.869.

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The results of this research indicate that among Taiex futures (ticker symbol: TX), Electronic Sector index futures (ticker symbol: TE), and Finance Sector Index futures (ticker symbol: TF); market interest rate, volatility, and liquidity all have significant influences on the basis of the same period. Furthermore, when VAR is used to examine if these three factors are leading indicators of basis; the results indicate that only the basis of Taiex futures is significantly affected by these three factors. In addition, this research also discovers that basis is affected by previous one or two per

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6

Lin, Tsung-Jui, Meng-Rong Li, and Yong Lee. "Taiex Index Option Model by Using Nonlinear Differential Equation." Mathematical and Computational Applications 19, no. 1 (2014): 78–92. http://dx.doi.org/10.3390/mca19010078.

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7

Cheng, Ching Hsue, Jing Wei Liu, and Tzu Hsuan Lin. "Multi-Factor Fuzzy Time Series Model Based on Stock Volatility for Forecasting Taiwan Stock Index." Advanced Materials Research 211-212 (February 2011): 1119–23. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.1119.

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Fuzzy time series have in recent years drawn many scholars' attention due to their ability can handle the time series data with incomplete, imprecise and ambiguous pattern. However, most traditional time series models employed only single variable (stock index) in forecasting, yet ignored some factors that would also affect the stock volatility. Therefore, this paper proposes a novel forecasting model using multi-factor fuzzy time series model to forecast TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index). Multi-factor fuzzy time series model is composed of three main components

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8

Cheng, Ching-Hsue, and Liang-Ying Wei. "Volatility model based on multi-stock index for TAIEX forecasting." Expert Systems with Applications 36, no. 3 (2009): 6187–91. http://dx.doi.org/10.1016/j.eswa.2008.07.020.

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9

Francis Diaz, John, Peh Ying Qian, and Genevieve Liao Tan. "Variance Persistence in the Greater China Region: A Multivariate GARCH Approach." LAHORE JOURNAL OF ECONOMICS 23, no. 2 (2018): 49–68. http://dx.doi.org/10.35536/lje.2018.v23.i2.a3.

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This paper utilizes three Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) models to determine variance persistence in the Greater China region from 2009 to 2014. The first approach applies the Baba, Engle, Kraft and Kroner (BEKK) model and shows that the Shanghai Stock Exchange Composite Index (SSEI), Taiwan Capitalization Weighted Stock Index (TAEIX) and the Hang Seng Stock Index (HSEI) stock returns are all functions of their lagged covariances and lagged cross-product innovations. The second MGARCH approach applies two methodologies, namely, dynamic conditional c

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10

Huang, Wen-Tso, and Cheng-Chang Lu. "An enhanced absorbing Markov chain model for predicting TAIEX Index Futures." Communications in Statistics - Theory and Methods 47, no. 1 (2017): 133–46. http://dx.doi.org/10.1080/03610926.2017.1300281.

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