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Journal articles on the topic 'TAIEX index'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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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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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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11

Pan, Chung-Lien, and Yu-Chun Pan. "The Index and Stock Price Synchronicity: Evidence from Taiwan." E3S Web of Conferences 198 (2020): 04029. http://dx.doi.org/10.1051/e3sconf/202019804029.

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Research on stock synchronization has always been a topic of concern to scholars and investors. In the past, the focus was mainly on equity concentration, foreign shareholding, audit quality, and other issues, not including indexes. This article uses the monthly data of the Taiwan Stock Exchange Capital Weighted Stock Index (TAIEX) to solve the problem of the index and stock synchronization. And use the technical theory of the gray system to solve the small sample and uncertain problem. The discovery of the synchronization between these indexes and stock prices may provide investors with suffi

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12

Wang, Yaw-Huei, and Yu-Jen Hsiao. "The Impact of Non-trading Periods on the Measurement of Volatility." Review of Pacific Basin Financial Markets and Policies 13, no. 04 (2010): 607–20. http://dx.doi.org/10.1142/s0219091510002098.

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Based upon the theory of the "arrival of news", the main purpose of this paper is to investigate the impact of non-trading periods on the measurement of volatility for the S&P 500, FTSE 100, and TAIEX indices. Using an adaptation of the GJR (1,1) model, we find that both weekday holiday periods and half-day trading periods have significant impacts on the estimation of volatility for the S&P 500 and FTSE 100 indices. On the other hand, weekends have significant impacts for the TAIEX index. Our findings imply that for the UK and US markets, much less relevant information is produced duri

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13

CHEN, I. CHUN, HUNG-JUNG CHEN, and HSEN-CHE TSENG. "PERSISTENCE PROBABILITY ANALYZED ON THE TAIWAN STOCK MARKET." International Journal of Modern Physics B 23, no. 22 (2009): 4713–26. http://dx.doi.org/10.1142/s0217979209053175.

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We report a numerical study of the Taiwan stock market, in which we used three data sources: the daily Taiwan stock exchange index (TAIEX) from January 1983 to May 2006, the daily OTC index from January 1995 to May 2006, and the one-min intraday data from February 2000 to December 2003. Our study is based on numerical estimates of persistence exponent θp, Hurst exponent H2, and fluctuation exponent h2. We also discuss the results concerning persistence probability P(t), qth-order price–price correlation function Gq(t), and qth-order normalized fluctuation function fq(t) among these indices.

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14

Huang, Han Ching, Yong Chern Su, and Wei-Shen Chen. "U.S. Quantitative Easing Policy Effect on TAIEX Futures Market Efficiency." Applied Economics and Finance 4, no. 4 (2017): 94. http://dx.doi.org/10.11114/aef.v4i4.2494.

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This paper examines the market efficiency of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Futures after the announcement of Quantitative Easing (QE) policy. Order imbalance is used to explore the relationship between return and order imbalance. We find that under the unconditional OLS model, lagged order imbalances almost have no significantly positive predictive power for current return. Nonetheless, on the trading day after the announcement of QE 1 policy, one-minute interval data show that the lagged order imbalance has predictive power for current return. Under the

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15

Su, Zhi-Yuan, and Yeng-Tseng Wang. "An Investigation into the Multifractal Characteristics of the TAIEX Stock Exchange Index in Taiwan." Journal of the Korean Physical Society 54, no. 4 (2009): 1385–94. http://dx.doi.org/10.3938/jkps.54.1385.

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16

Wang, Jying-Nan, Hung-Chun Liu, and Lu-Jui Chen. "On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index." International Journal of Economics and Finance 9, no. 9 (2017): 133. http://dx.doi.org/10.5539/ijef.v9n9p133.

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This paper aims to propose four volatility measures: The first is the GARCH model advocated by Bollerslev (1986); the second is the GARCHVIX model which extends the GARCH model by including the volatility index (VIX) as explanatory variable for volatility; the last two are HS20D and HS252D, which represent the historical volatilities generated by traditional rolling window technique with 20- and 252-day historical index returns data, respectively. We examine the price information on VIX to improve the predictive performance of GARCH model for valuing TAIEX stock index call options (TXO) over t

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17

Tsai, Ming-Chi, Ching-Hsue Cheng, and Meei-Ing Tsai. "A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index." Symmetry 11, no. 12 (2019): 1474. http://dx.doi.org/10.3390/sym11121474.

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Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to

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18

Singh, Pritpal, Gaurav Dhiman, Sen Guo, et al. "A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset." Modern Physics Letters A 34, no. 25 (2019): 1950201. http://dx.doi.org/10.1142/s0217732319502018.

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The supremacy of quantum approach is able to provide the solutions which are not practically feasible on classical machines. This paper introduces a novel quantum model for time series data which depends on the appropriate length of intervals. In this study, the effects of these drawbacks are elaborately illustrated, and some significant measures to remove them are suggested, such as use of degree of membership along with mid-value of the interval. All these improvements signify the effective results in case of quantum time series, which are verified and validated with real-time datasets.

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19

Hsieh, Heng-Hsing, and Kathleen Hodnett. "Application Of Resilient Long-Short Strategies For Taiwanese Hedge Funds." International Business & Economics Research Journal (IBER) 11, no. 6 (2012): 621. http://dx.doi.org/10.19030/iber.v11i6.7017.

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This paper proposes a 4-step approach in developing resilient long-short hedge fund strategies for stocks traded on the Taiwan Stock Exchange. The two hedge fund strategies developed in this research, namely the Hybrid Neutral Fund and the Defense Fund, outperform their respective benchmarks on a risk-adjusted basis over the examination period from 01 January 2000 to 31 December 2011. In addition, both hedge fund strategies efficiently mitigate the drawdowns during turbulent times. The total returns on the TAIEX Index (the market proxy) is found to be a mean-variance inefficient portfolio over

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20

Chen, Nien Ping, Meng Rong Li, Tsung Jui Chiang Lin, Young Shiuan Lee, and Daniel Wei Chung Miao. "Applications of linear ordinary differential equations and dynamic system to economics - an example of Taiwan stock index TAIEX." International Journal of Dynamical Systems and Differential Equations 7, no. 2 (2017): 95. http://dx.doi.org/10.1504/ijdsde.2017.085824.

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21

Li, Meng Rong, Tsung Jui Chiang Lin, Young Shiuan Lee, Daniel Wei Chung Miao, and Nien Ping Chen. "Applications of linear ordinary differential equations and dynamic system to economics - an example of Taiwan stock index TAIEX." International Journal of Dynamical Systems and Differential Equations 7, no. 2 (2017): 95. http://dx.doi.org/10.1504/ijdsde.2017.10006751.

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22

Javedani Sadaei, Hossein, and Muhammad Hisyam Lee. "Multilayer Stock Forecasting Model Using Fuzzy Time Series." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/610594.

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After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data

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23

Wu, Mu-En, and Wei-Ho Chung. "Empirical Evaluations on Momentum Effects of Taiwan Index Futures via Stop-Loss and Stop-Profit Mechanisms." International Journal of Information Technology & Decision Making 18, no. 02 (2019): 629–48. http://dx.doi.org/10.1142/s0219622019500056.

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The Efficient-Market Hypothesis (EMH) is one of the important theories in financial markets. Under this hypothesis, developing a robust profitable strategy is infeasible because the market price fluctuates immediately following any new information and is thus unpredictable. However, many empirical studies have shown that certain trading strategies in the financial markets are profitable, and the Momentum Strategy is one of the major strategies among them. With four momentum strategies, this paper uses the real-world data points (intra-day data of one-minute time frame) for back-testing the Tai

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24

Chen, Tai-Liang, Ching-Hsue Cheng, and Jing-Wei Liu. "A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index." International Journal of Information Technology & Decision Making 18, no. 06 (2019): 1967–87. http://dx.doi.org/10.1142/s0219622019500421.

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Stock forecasting technology is always a popular research topic because accurate forecasts allow profitable investments and social change. We postulate, based on past research, three major drawbacks for using time series in forecasting stock prices as follows: (1) a simple time-series model provides insufficient explanations for inner and external interactions of the stock market; (2) the variables of a time series behave in strict stationarity, but economic time-series are usually in a nonlinear or nonstationary state and (3) the forecasting factors of multivariable time-series are selected b

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25

Chen, Qi-an, Dan Wang, and Mingyong Pan. "Multivariate Time-VaryingG-HCopula GARCH Model and Its Application in the Financial Market Risk Measurement." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/286014.

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Taking full advantage of the strengths ofG-Hdistribution, Copula function, and GARCH model in depicting the return distribution of financial asset, we construct the multivariate time-varyingG-HCopula GARCH model which can comprehensively describe “asymmetric, leptokurtic, and heavy-tail” characteristics, the time-varying volatility characteristics, and the extreme-tail dependence characteristics of financial asset return. Based on the conditional maximum likelihood estimator and IFM method, we propose the estimation algorithm of model parameters. Using the quantile function and simulation meth

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26

Wu, Chung Min, Sheng Chun Chou, and Horng Twu Liaw. "Applying Clustering Algorithms to Construct a Stock Trend Decision Model." Applied Mechanics and Materials 311 (February 2013): 81–86. http://dx.doi.org/10.4028/www.scientific.net/amm.311.81.

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The stock market is a well-developed and mature market. Nevertheless, it is not immune to international financial market changes, where volatility has reigned in recent years. Investors who misgauge stock trends can suffer dramatic losses. Accurate identification of market trends can still achieve outstanding performance and has become a major investor concern. This paper proposes a new stock price trend clustering model using a genetic algorithm to search for optimal investment strategies. Daily stock prices and trading volume data from the Taiwan stock exchange weighted index (TAIEX) was use

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27

NAN, GUOFANG, SHUAIYIN ZHOU, JISONG KOU, and MINQIANG LI. "HEURISTIC BIVARIATE FORECASTING MODEL OF MULTI-ATTRIBUTE FUZZY TIME SERIES BASED ON FUZZY CLUSTERING." International Journal of Information Technology & Decision Making 11, no. 01 (2012): 167–95. http://dx.doi.org/10.1142/s0219622012500083.

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Fuzzy time series has been applied to forecast various domain problems because of its capability to deal with vagueness and incompleteness inherent in data. However, most existing fuzzy time series models cannot cope with multi-attribute time series and remain too subjective in the partition of the universe of discourse. Moreover, these models do not consider the trend factor and the corresponding external time series, which are highly relevant to target series. In the current paper, a heuristic bivariate model is proposed to improve forecasting accuracy, and the proposed model applies fuzzy c

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28

Guan, Hongjun, Zongli Dai, Shuang Guan, and Aiwu Zhao. "A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation." Entropy 21, no. 5 (2019): 455. http://dx.doi.org/10.3390/e21050455.

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In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous da

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29

Jane, Chuen Jiuan. "Mechanical Selection System of Stock Portfolio Based on Grey Relational Analysis Attribute Reduction Mechanism." Applied Mechanics and Materials 311 (February 2013): 73–77. http://dx.doi.org/10.4028/www.scientific.net/amm.311.73.

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A stock portfolio selection system based on a Grey Relational Analysis (GRA) model, a Fuzzy C-Means (FCM) clustering scheme and Rough Set (RS) theory is proposed. In the proposed approach, 53 financial indices are collected automatically for each stock item every quarter and a GRA model is used to consolidate these indices into six predetermined financial ratios (Grey Relational Grades (GRGs)). The GRGs of the stock items are then clustered using a FCM scheme and the resulting cluster indices are processed using RS theory to identify the lower approximate set within the stock system. The stock

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30

Zhao, Aiwu, Junhong Gao, and Hongjun Guan. "Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement." Symmetry 12, no. 6 (2020): 954. http://dx.doi.org/10.3390/sym12060954.

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The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positi

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31

Wang, Janchung, and Hsinan Hsu. "Price Expectation and the Pricing of Stock Index Futures: Evidence from Developed and Emerging Markets." Review of Pacific Basin Financial Markets and Policies 09, no. 04 (2006): 639–60. http://dx.doi.org/10.1142/s0219091506000884.

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This study examines how well the pricing model of Hsu and Wang (2004) explains the behavior of stock index futures prices for the developed markets (such as the S&P 500 index futures market) and the emerging markets (such as the Taiwan Futures Exchange (TAIFEX) Taiwan stock index futures market). It also compares the pricing performance of three alternative pricing models of stock index futures: the cost of carry model, the Hemler and Longstaff (1991) model, and the Hsu–Wang model. Overall, the Hsu–Wang model provides significantly better pricing performance than that of the cost of carry

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32

Chou, Jian‐Hsin, and Hong‐Fwu Yu. "A stochastic process approach in setting the appropriate margin level for the TAIFEX stock index futures." Managerial Finance 32, no. 11 (2006): 886–902. http://dx.doi.org/10.1108/03074350610703830.

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PurposeThe main purpose of this paper is to compute the appropriate margin level for the stock index futures traded on the Taiwan Futures Exchange (TAIFEX) and, then, to examine the appropriateness of the real margin requirement set by the TAIFEX.Design/methodology/approachThis paper develops a new approach assuming the future's prices follow a geometric Brownian motion process. Compared with the extreme value theory that has been intensively used to determine the appropriate futures margin levels, one of the advantages of the present model is no need to specify the frequency at which extremes

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33

Wang, Janchung. "Short Selling and Index Arbitrage Profitability: Evidence from the SGX MSCI and TAIFEX Taiwan Index Futures Markets." Emerging Markets Finance and Trade 46, no. 5 (2010): 48–66. http://dx.doi.org/10.2753/ree1540-496x460504.

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Ouyang, Leqian, and Daming You. "Measurement Method of Economic Difference of Export-oriented Green Innovation Economy in Coastal Cities Based on Taier Index." Journal of Coastal Research 93, sp1 (2019): 817. http://dx.doi.org/10.2112/si93-115.1.

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DIAZ, JOHN FRANCIS. "DYNAMIC VOLATILITY SPILLOVERS ACROSS THE EXTENDED GREATER CHINA REGION STOCK MARKETS." Global Economy Journal, May 19, 2021, 2150003. http://dx.doi.org/10.1142/s2194565921500032.

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The study investigates volatility transmissions of major financial market indices found in the Extended Greater China Region (EGCR): the Shanghai Stock Exchange Index (SSEI), the Hang Seng Stock Exchange Index (HSEI), the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), and the Singapore Stock Exchange Index (STI). This paper utilizes three Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models to determine variance persistence in the EGCR. The MGARCH approach applies the Baba–Engle–Kraft–Kroner (BEKK) model and the dynamic conditional correlat

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36

Yang, Tzu-Yi, and Yu-Tai Yang. "Are There any Regular Variations in the Taiwan Stock Market: A Case Study of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX)." Accounting and Finance Research 4, no. 1 (2015). http://dx.doi.org/10.5430/afr.v4n1p172.

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37

Yen, Peter Tsung-Wen, and Siew Ann Cheong. "Using Topological Data Analysis (TDA) and Persistent Homology to Analyze the Stock Markets in Singapore and Taiwan." Frontiers in Physics 9 (March 4, 2021). http://dx.doi.org/10.3389/fphy.2021.572216.

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In recent years, persistent homology (PH) and topological data analysis (TDA) have gained increasing attention in the fields of shape recognition, image analysis, data analysis, machine learning, computer vision, computational biology, brain functional networks, financial networks, haze detection, etc. In this article, we will focus on stock markets and demonstrate how TDA can be useful in this regard. We first explain signatures that can be detected using TDA, for three toy models of topological changes. We then showed how to go beyond network concepts like nodes (0-simplex) and links (1-simp

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