On the basis of the price, volume and volatility of underlying stocks, this article empirically investigates the impact of 273 Taiwan call warrants on underlying stocks. Discussions by the market risk, depth, tightnes...On the basis of the price, volume and volatility of underlying stocks, this article empirically investigates the impact of 273 Taiwan call warrants on underlying stocks. Discussions by the market risk, depth, tightness and liquidity, changes on underlying stocks due to warrants issuance, are investigated. In this study, the CAPM is applied for evaluating the market risk, the Kyle model for the market depth, the averaged best five bid-ask spread for the market tightness and the averaged turnover rate for the market liquidity. The empirical results indicate that the most significant influence is the market liquidity, the market tightness next; the market risk and market depth are non-significant.展开更多
The aim of the paper is to provide some evidences on relationships among the degree of financial integration, stock exchange markets, and volatility of national market returns. In this paper, the authors employ correl...The aim of the paper is to provide some evidences on relationships among the degree of financial integration, stock exchange markets, and volatility of national market returns. In this paper, the authors employ correlation and cluster analyses in order to investigate the impact of stock exchange consolidation on volatility of market returns, in terms of a financial integration between involved stock exchanges before and after the merger. By using the Generalized Autoregressive Conditional Heteroskedasticity （GARCH） （1.1） model, the authors test the change in volatilities of national stock exchange markets involved in the following stock exchange integration case studies： Euronext, Bolsasy Mercados Espanoles （BME）, and Swedish-Finnish financial services company （OMX）. These three case studies are considered as completed cases of market consolidation, where the data are available enough to conduct the current research. By using daily data of national returns of engaged European stock markets from 1995 to 2007, the paper investigates the influence of stock exchange consolidation on volatility of national stock market returns. The obtained results confirm the gradual decrease of volatility in each of the integrated stock markets. However, the level of decrease in terms of volatility depends on economic characteristics of each engaged market and its degree of integration with other financial services. The results of correlation and cluster analyses confirm that stock operators have created significantly non-official integration links through cross-memberships and cross-listings even before the consolidations. Thus, the mergers among stock exchanges can be considered as the rational consequences of the high internal co-movements between involved markets. Furthermore, stock exchange markets with strong non-official integration links show an immediate decrease of volatility after the merger, meanwhile for others, it takes several years before the volatility can decrease as markets should reach the full integration.展开更多
Taking the evolution process of TFT-LCD industry as an example,this paper applied history-friendly model to analyze the effect of technology innovation and learning,and market demand growth and fluctuation on the evol...Taking the evolution process of TFT-LCD industry as an example,this paper applied history-friendly model to analyze the effect of technology innovation and learning,and market demand growth and fluctuation on the evolution of production organization pattern in strategic emerging industries.Our research indicates that:(1) when market demand maintains linear growth,continuous technology innovation capabilities of vertically integrated enterprises in leading position of an industry are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(2) when market demand is in cyclical fluctuation,the technology learning capabilities of specialized enterprises in catch-up position are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(3) when market demand growth is under cyclical fluctuation,if the relative gap between technology innovation capabilities of vertically integrated enterprises and technology learning capabilities of specialized enterprises remains constant,the phase when industry cycle moves from trough to plateau is the best time window for specialized enterprises to catch up with and overtake vertically integrated enterprises.Hence,policy design supporting the development of strategic emerging industries should give full consideration to factors like market demand environment and technology innovation and learning capabilities of domestic enterprises.展开更多
The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original fin...The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components （i.e., approximation and details） using the maximum overlap discrete wavelet transform （MODWT）. The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average （ARIMA） with feedforward neural network （FNN）. ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.展开更多
This paper aims to contribute to the literature on the explanatory power of behavior models with heterogeneous agents. The authors present a new nonlinear structural stock market model which is a nonlinear determinist...This paper aims to contribute to the literature on the explanatory power of behavior models with heterogeneous agents. The authors present a new nonlinear structural stock market model which is a nonlinear deterministic process buffeted by dynamic noise. An exogenous noise is introduced to the model with the assumption of IID normal innovations of the fundamental value in order to investigate how noisy dynamics interacts with deterministic process. The market is composed of two typical trader types： the rational fundamentalists and the boundedly rational traders governed by greed and fear. The interaction between noise and deterministic element determines the evolution process of the system as key parameters are changed. The authors find the model is able to generate time series that exhibit dynamical and statistical properties closely resembling those of the S＆：P500 index, such as volatility clustering, fat tails （leptokurtosis）, autocorrelation in square and absolute return, larger amplitude, crashes and bubbles. The authors also investigate the nonlinear dependence structure in our data. The results indicate that the GARCH-type model cannot completely account for all nonlinearity in our simulated market, which is thus consistent with the results from real markets. It seems that the nonlinear structural model is more powerful to give a satisfied explanation to market behavior than the traditional stochastic approach.展开更多
文摘On the basis of the price, volume and volatility of underlying stocks, this article empirically investigates the impact of 273 Taiwan call warrants on underlying stocks. Discussions by the market risk, depth, tightness and liquidity, changes on underlying stocks due to warrants issuance, are investigated. In this study, the CAPM is applied for evaluating the market risk, the Kyle model for the market depth, the averaged best five bid-ask spread for the market tightness and the averaged turnover rate for the market liquidity. The empirical results indicate that the most significant influence is the market liquidity, the market tightness next; the market risk and market depth are non-significant.
文摘The aim of the paper is to provide some evidences on relationships among the degree of financial integration, stock exchange markets, and volatility of national market returns. In this paper, the authors employ correlation and cluster analyses in order to investigate the impact of stock exchange consolidation on volatility of market returns, in terms of a financial integration between involved stock exchanges before and after the merger. By using the Generalized Autoregressive Conditional Heteroskedasticity （GARCH） （1.1） model, the authors test the change in volatilities of national stock exchange markets involved in the following stock exchange integration case studies： Euronext, Bolsasy Mercados Espanoles （BME）, and Swedish-Finnish financial services company （OMX）. These three case studies are considered as completed cases of market consolidation, where the data are available enough to conduct the current research. By using daily data of national returns of engaged European stock markets from 1995 to 2007, the paper investigates the influence of stock exchange consolidation on volatility of national stock market returns. The obtained results confirm the gradual decrease of volatility in each of the integrated stock markets. However, the level of decrease in terms of volatility depends on economic characteristics of each engaged market and its degree of integration with other financial services. The results of correlation and cluster analyses confirm that stock operators have created significantly non-official integration links through cross-memberships and cross-listings even before the consolidations. Thus, the mergers among stock exchanges can be considered as the rational consequences of the high internal co-movements between involved markets. Furthermore, stock exchange markets with strong non-official integration links show an immediate decrease of volatility after the merger, meanwhile for others, it takes several years before the volatility can decrease as markets should reach the full integration.
基金Financial support from Key Program of National Social Sciences Foundation of China(Grant No.10AJL008)is gratefully acknowledged
文摘Taking the evolution process of TFT-LCD industry as an example,this paper applied history-friendly model to analyze the effect of technology innovation and learning,and market demand growth and fluctuation on the evolution of production organization pattern in strategic emerging industries.Our research indicates that:(1) when market demand maintains linear growth,continuous technology innovation capabilities of vertically integrated enterprises in leading position of an industry are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(2) when market demand is in cyclical fluctuation,the technology learning capabilities of specialized enterprises in catch-up position are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(3) when market demand growth is under cyclical fluctuation,if the relative gap between technology innovation capabilities of vertically integrated enterprises and technology learning capabilities of specialized enterprises remains constant,the phase when industry cycle moves from trough to plateau is the best time window for specialized enterprises to catch up with and overtake vertically integrated enterprises.Hence,policy design supporting the development of strategic emerging industries should give full consideration to factors like market demand environment and technology innovation and learning capabilities of domestic enterprises.
基金supported by the Humanities and Social Sciences Youth Foundation of the Ministry of Education of PR of China under Grant No.11YJC870028the Selfdetermined Research Funds of CCNU from the Colleges’Basic Research and Operation of MOE under Grant No.CCNU13F030+1 种基金China Postdoctoral Science Foundation under Grant No.2013M530753National Science Foundation of China under Grant No.71390335
文摘The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components （i.e., approximation and details） using the maximum overlap discrete wavelet transform （MODWT）. The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average （ARIMA） with feedforward neural network （FNN）. ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.
基金This research is supported by MEXT Global COE Program （Kyoto University）, National Natural Science Foundation of China under Grant No.71001036 and No. 71171186, Main Direction Program of Chinese Academy of Sciences KACX1-YW-0906, and the Scientific Research Fund of Hunan Provincial Education Department under Grant No. 10A082.
文摘This paper aims to contribute to the literature on the explanatory power of behavior models with heterogeneous agents. The authors present a new nonlinear structural stock market model which is a nonlinear deterministic process buffeted by dynamic noise. An exogenous noise is introduced to the model with the assumption of IID normal innovations of the fundamental value in order to investigate how noisy dynamics interacts with deterministic process. The market is composed of two typical trader types： the rational fundamentalists and the boundedly rational traders governed by greed and fear. The interaction between noise and deterministic element determines the evolution process of the system as key parameters are changed. The authors find the model is able to generate time series that exhibit dynamical and statistical properties closely resembling those of the S＆：P500 index, such as volatility clustering, fat tails （leptokurtosis）, autocorrelation in square and absolute return, larger amplitude, crashes and bubbles. The authors also investigate the nonlinear dependence structure in our data. The results indicate that the GARCH-type model cannot completely account for all nonlinearity in our simulated market, which is thus consistent with the results from real markets. It seems that the nonlinear structural model is more powerful to give a satisfied explanation to market behavior than the traditional stochastic approach.
