Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage

博士 === 國立交通大學 === 資訊管理所 === 90 === This thesis examines four issues in asset allocation. The research on active portfolio, in which the traditional asset allocation method based on mean-variance efficient portfolio when forming portfolios does not take into account the linkage behavior, lead-lags or...

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Main Authors: Shinn-Wen Wang, 王信文
Other Authors: An-Pin Chen
Format: Others
Language:zh-TW
Published: 2002
Online Access:http://ndltd.ncl.edu.tw/handle/88374835827649723976
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description 博士 === 國立交通大學 === 資訊管理所 === 90 === This thesis examines four issues in asset allocation. The research on active portfolio, in which the traditional asset allocation method based on mean-variance efficient portfolio when forming portfolios does not take into account the linkage behavior, lead-lags or rational expectations of investors. That caused the unexpected result of performance on next-period while using allocations from the previous period in asymmetric information situations. To attack this bottleneck, we propose a multinational portfolio allocation model that integrates the mean-variance optimization with a scenario approach, which derives the linkage scenario classifications of assets in multinational portfolio in order to estimate rationally the expected return and risk matrix on next-period by modifying the previous-period periods so as to reallocate the weights of assets, thus increasing the opportunity to earn profits in an asymmetrical information situation. By using the Morgan Stanley country indices of global markets as the empirical evidence of portfolio content, we show that the proposed scenario- based mean variance model can serve as an efficient frontier to correspond with rational expectation of investors, decrease the data samples for calculating risk matrix. In addition, there will be less computation cost to solve mean-variance optimization. In contrast, the research on passive portfolio consists of linear assets, in which the original portfolio insurance model based on constant proportion portfolio insurance (CPPI) and time-invariant portfolio protection strategies (TIPP) strategies. The multiplier factor of CPPI or TIPP model significantly influences the performance of insurance. However no systematic tuning method has been presented to date. Thus, we propose a value-at-risk based asset allocation insurance model (VALIS), which is a novel dynamic strategy derived from the theorem of value- at-risk control. For this, we derive a dynamic tuning model for the multiplier in portfolio insurance strategies, considering of estimation of value-at-risk. The proposed model also improves the capability of capturing upside profits and enhances the ability to avoid downside losses. This research shows that the VALIS model seems a Min-Max style insurance strategy and demonstrates that this model fits in with the concept of portfolio insurance properties proposed by Rubinstein. Simulations and empirical study show the proposed model is superior to conventional portfolio insurance strategies such as buy and hold, constant-mix, the fixed multiplier CPPI and TIPP. Furthermore, the VALIS model also decreases the probability of failure to insurance. This research would contribute to the innovation of CPPI portfolio insurance based models and would be very helpful to passive investors or foundations for managing portfolios in the real world market, especially for the Asian markets or others in financial turmoil. Furthermore, this research on active portfolio, we proposed an intelligent arbitrage model. The Black-Scholes options pricing formula is widely applied in various options contracts, including contract design, trading, assets evaluation, and enterprise valuation, etc. However, this theoretical model is bounded by the influences of phenomenon caused in real world considerations by six unreasonable assumptions. Therefore, if we take into account the phenomenon of linkage behavior soundly, the opportunity to gain excess return would be created. This research combines both the remarkable effects caused by implied volatility smile (or skew), and discrepancy of both the underlying and derivative tick price movement limitation to form a two-phase options arbitrage model using genetic-based neural network. Evidence from the plain vanilla options in Taiwan indicates that the proposed model is superior to the original Black-Scholes based arbitrage model and is suitable to be applied to various options market in practice. The proposed model would help to coordinate the theoretical model and real world considerations. Finally, for performance evaluation, we compare the aspiration level index with the Sharpe ratio, emphasizing their differences. We begin with portfolio decision-making for which is placed on how to obtain an optimal solution under given circumstance, maximizing returns or minimizing risk. However, in real situations of management under uncertainty risk must be considered to make decisions. In this part, a decision-making method for portfolios is proposed to both maximize returns, and also minimize the risk of portfolios. The fuzzy mean-variance technique employed here is used to analyze the maximum return under minimum risk in market using the proposed model, called the fuzzy multi-objective portfolio (FMOP) model. It does so by setting up optimal weights for each of the portfolio factors. Aspiration level is represented in FMOP model using fuzzy membership functions to obtain feasible solutions, which are evaluated by the vague aspiration level of investors’ decisions. The cases study of global portfolio demonstrates the difference between the aspiration level index and Sharpe ratio.
author2 An-Pin Chen
author_facet An-Pin Chen
Shinn-Wen Wang
王信文
author Shinn-Wen Wang
王信文
spellingShingle Shinn-Wen Wang
王信文
Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage
author_sort Shinn-Wen Wang
title Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage
title_short Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage
title_full Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage
title_fullStr Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage
title_full_unstemmed Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage
title_sort portfolio management: asset allocation planning model for optimization, insurance and arbitrage
publishDate 2002
url http://ndltd.ncl.edu.tw/handle/88374835827649723976
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spelling ndltd-TW-090NCTU03960332016-06-27T16:09:00Z http://ndltd.ncl.edu.tw/handle/88374835827649723976 Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage 投資組合管理:資產最佳配置、保本與套利之模型規劃 Shinn-Wen Wang 王信文 博士 國立交通大學 資訊管理所 90 This thesis examines four issues in asset allocation. The research on active portfolio, in which the traditional asset allocation method based on mean-variance efficient portfolio when forming portfolios does not take into account the linkage behavior, lead-lags or rational expectations of investors. That caused the unexpected result of performance on next-period while using allocations from the previous period in asymmetric information situations. To attack this bottleneck, we propose a multinational portfolio allocation model that integrates the mean-variance optimization with a scenario approach, which derives the linkage scenario classifications of assets in multinational portfolio in order to estimate rationally the expected return and risk matrix on next-period by modifying the previous-period periods so as to reallocate the weights of assets, thus increasing the opportunity to earn profits in an asymmetrical information situation. By using the Morgan Stanley country indices of global markets as the empirical evidence of portfolio content, we show that the proposed scenario- based mean variance model can serve as an efficient frontier to correspond with rational expectation of investors, decrease the data samples for calculating risk matrix. In addition, there will be less computation cost to solve mean-variance optimization. In contrast, the research on passive portfolio consists of linear assets, in which the original portfolio insurance model based on constant proportion portfolio insurance (CPPI) and time-invariant portfolio protection strategies (TIPP) strategies. The multiplier factor of CPPI or TIPP model significantly influences the performance of insurance. However no systematic tuning method has been presented to date. Thus, we propose a value-at-risk based asset allocation insurance model (VALIS), which is a novel dynamic strategy derived from the theorem of value- at-risk control. For this, we derive a dynamic tuning model for the multiplier in portfolio insurance strategies, considering of estimation of value-at-risk. The proposed model also improves the capability of capturing upside profits and enhances the ability to avoid downside losses. This research shows that the VALIS model seems a Min-Max style insurance strategy and demonstrates that this model fits in with the concept of portfolio insurance properties proposed by Rubinstein. Simulations and empirical study show the proposed model is superior to conventional portfolio insurance strategies such as buy and hold, constant-mix, the fixed multiplier CPPI and TIPP. Furthermore, the VALIS model also decreases the probability of failure to insurance. This research would contribute to the innovation of CPPI portfolio insurance based models and would be very helpful to passive investors or foundations for managing portfolios in the real world market, especially for the Asian markets or others in financial turmoil. Furthermore, this research on active portfolio, we proposed an intelligent arbitrage model. The Black-Scholes options pricing formula is widely applied in various options contracts, including contract design, trading, assets evaluation, and enterprise valuation, etc. However, this theoretical model is bounded by the influences of phenomenon caused in real world considerations by six unreasonable assumptions. Therefore, if we take into account the phenomenon of linkage behavior soundly, the opportunity to gain excess return would be created. This research combines both the remarkable effects caused by implied volatility smile (or skew), and discrepancy of both the underlying and derivative tick price movement limitation to form a two-phase options arbitrage model using genetic-based neural network. Evidence from the plain vanilla options in Taiwan indicates that the proposed model is superior to the original Black-Scholes based arbitrage model and is suitable to be applied to various options market in practice. The proposed model would help to coordinate the theoretical model and real world considerations. Finally, for performance evaluation, we compare the aspiration level index with the Sharpe ratio, emphasizing their differences. We begin with portfolio decision-making for which is placed on how to obtain an optimal solution under given circumstance, maximizing returns or minimizing risk. However, in real situations of management under uncertainty risk must be considered to make decisions. In this part, a decision-making method for portfolios is proposed to both maximize returns, and also minimize the risk of portfolios. The fuzzy mean-variance technique employed here is used to analyze the maximum return under minimum risk in market using the proposed model, called the fuzzy multi-objective portfolio (FMOP) model. It does so by setting up optimal weights for each of the portfolio factors. Aspiration level is represented in FMOP model using fuzzy membership functions to obtain feasible solutions, which are evaluated by the vague aspiration level of investors’ decisions. The cases study of global portfolio demonstrates the difference between the aspiration level index and Sharpe ratio. An-Pin Chen 陳安斌 2002 學位論文 ; thesis 185 zh-TW