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研究生:王信文
研究生(外文):Shinn-Wen Wang
論文名稱:投資組合管理:資產最佳配置、保本與套利之模型規劃
論文名稱(外文):Portfolio Management: Asset Allocation Planning Model for Optimization, Insurance and Arbitrage
指導教授:陳安斌陳安斌引用關係
指導教授(外文):An-Pin Chen
學位類別:博士
校院名稱:國立交通大學
系所名稱:資訊管理所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:185
中文關鍵詞:均異模型風險值投組保險Black-Scholes模型套利模糊多目標
外文關鍵詞:Mean-Variance modelValue-at-RiskPortfolio InsuranceBlack-Scholes FormulaArbitrageFuzzy Multiobjective
相關次數:
  • 被引用被引用:4
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  • 收藏至我的研究室書目清單書目收藏:9
本論文針對投資組合管理的四項主題進行研究。在積極型投資組合研究中,以均異效率組合為基礎的各種投組最適配置規劃方法,於跨國資產配置中並未考慮當期市場聯動行為、資產間領先-落後期數與投資者的理性預期對於投組規劃的影響,造成資訊不對稱下的前期資產最適配置在後期的績效表現出現落差。為處理上述問題,在第四章中,本文提出一種結合情境分析方法與均異最適規劃法的跨國投資組合規劃模型,該模型透過跨國投資組合內的價先資產與價後資產間的聯動情境分類,導入均異最適規劃模型中,藉以於當期調整於前期求取的投組風險-報酬結構且估計合理之期望報酬水準,作為後期投組資產配置的基礎,期以獲得為符合預期而修正後的投組效率前緣,在資訊不對稱的環境下增加獲取超額報酬之機會。實證結果顯示,與傳統的均異投組規劃進行比較,所提出之情境-均異模型具有使得投組效率前緣依理性預期調整與減少求取風險矩陣資料量與計算成本降低等優點。
相對地,在保守型投資組合研究中,以CPPI以及TIPP為基礎的投組保險策略,該模型中的乘值因子的設定影響其保險期間內的績效表現甚鉅,但目前並無系統性的設定方法。因此本研究提出風險值資產配置保險策略模型,其為以風險值為基礎所推演之動態調整策略模型;該模型利用風險值估計方法以動態調整投組保險策略的乘值因子,達到提升上方報酬捕獲率與強化下方風險規避之目的。研究結果顯示所提模型具有最大化極小報酬的能力,並證明其符合理想投資組合保險策略模型之特性,在實務應用亦具有可行性與有效性。由實證結果中發現,在各型市場趨勢下,可顯著地擊敗主要標竿指數並優於其它傳統之投資組合保險策略;尤其面對市場跳空情境下,風險值資產配置保險策略模型可提供較傳統投組保險策略為佳的保本效果,同時降低破底機率。本研究成果除對理論之推展創新具貢獻外,尤其在面對時值全球性市場向下動盪修正趨勢之金融環境下,對於諸如保本基金、退休基金、平衡式基金與相關之保守型基金等,以資產配置保險策略為基礎之操作經理人或機構績效之提升,具明顯而實質之助益。
另者,在非線性資產積極型投組規劃方面,本研究所提出的是智慧型套利模型。Black-Scholes選擇權定價模型目前廣泛地被應用於各式選擇權合約之設計與交易操作、資產評價與企業價值估計等領域。但由於該理論定價模型的六大假設,使得在實務環境下產生諸多未盡考量之現象,若能充份考慮該現象於模型中,則可創造許多超額報酬的機會。本研究結合在傳統的Black-Scholes模型中尚未考慮,但影響顯著的隱含波幅偏態效用,以及選擇權商品和標的物價格間跳動級距的差異效果,透過基因類網以建立兩階段選擇權套利模型。實證結果顯示所提出的套利模型優於以Black-Scholes為基礎之傳統套利方法並適行於各式選擇權市場。智慧型套利模型有助於選擇權定價模型在實務環境下考慮其它影響評價的變因,提升理論模型與實務應用的配合度,同時強化選擇權市場交易之合理性與效率性。
最後,針對投資組合的績效評估,本研究比較滿意度水準(aspiration level)指標與傳統夏普指標的異同處。該部分的實證研究,是以投資組合決策為核心,主要重點乃在於找出最佳的投資組合;以傳統的均異模型為基礎的處理方式多半是以利潤最大或風險最小為決策目標。但在實際的金融市場中,尚必須考量諸多具有不確定性特質的問題。本部分研究所建構的組合決策分析模式裡,經由各輸入要因歷史的時間序列資料中,不僅考慮獲利最大化,且同時追求風險最小化。研究中利用模糊均異法則作為投資組合決策之分析工具,藉以在滿足報酬最大化、風險最小化的多目標決策前提下,建構模糊多目標投組模型,以找出組合中各要因的最佳配置量;其中,解的評價方式為將目標函數轉換成模糊歸屬函數,使其滿意水準滿足度為最大。研究中以全球主要股價指數為實證案例,比較滿意度水準指標與夏普指標的差異性。
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.
Chapter 1 Introduction 1
1.1 Motivation and Objective 1
1.1.1 The rational expectation of MV efficient portfolio 2
1.1.2 Risk control model for portfolio insurance strategy 5
1.1.3 BSF-Based arbitrage model via genetic-based neural network 6
1.1.4 Fuzzy multiobjective portfolio optimization model 8
1.2 Research Methodology 9
1.2.1 Scenario-based MV model 9
1.2.2 value-at-risk asset allocation insurance strategy 10
1.2.3 genetic-based neural network arbitrage model 11
1.2.4 Fuzzy multiobjective optimal programming model 11
1.3 The framework of research 12
1.4 Summary of Results 13
1.5 Thesis organized 15
Chapter 2 Literature Reviews 18
2.1 Linkage of international equity market and MV efficient frontier 18
2.2 Value-at-risk and portfolio insurance 20
2.3 Volatility Skew based Arbitrage Portfolio 21
2.3.1 Volatility Skew Analysis 21
2.3.2 Modeling of Genetic-based neural networks 23
2.4 Investment decision-making and fuzzy multiobjectuve optimization 26
Discussion 27
Chapter 3 Fundamentals of Theoretical Model in Asset Allocation 28
3.1 Linkage of asset price in international efficient portfolio 28
3.1.1 The testing procedure for linkage on previous-perio 28
3.1.2 Analyzing the continuity of trend on next-period─ estimating by time series model 30
3.1.3 Scenario approach and MV model 35
3.2 Portfolio insurance strategy 39
3.2.1 Fundamental of portfolio insurance 39
3.2.2 Value-at-risk model (VaR) 41
Discussion 44
Chapter 4 A New Programming Methodology 45
for Linear Asset Allocation 45
—- the heterogeneous expectations- based MV Efficient Portfolio 45
4.1 Modeling process of SMVM 45
4.2 SMVM method 49
4.2.1 SMVM-I model 50
4.2.2 SMVM-II model 52
4.2.3 MV model 53
4.3 Extended SMVM model 53
4.3.1 Single factor portfolio model 53
4.3.2 Multi-factor portfolio model 54
4.4 Value- at- Risk (VaR)- based SMVM model 55
Discussion 56
Chapter 5 A New Dynamic Portfolio Insurance Model 58
for Asset Allocation 58
─Value-at-Risk Control- based Portfolio Insurance Model 58
5.1 The value-at-risk based portfolio insurance model 58
5.2 Verification of Rubinstein’s idea portfolio insurance properties of VALIS 63
5.3 The property of floor- breaking moderation effect in VALIS 64
5.4 The setting and adjustment of investor risk Preference 66
Discussion 66
Chapter 6 Modeling Arbitrage Portfolio for Nonlinear Asset Allocation 68
--the Volatility Skew-based Artificial Options Valuation Model 68
6.1 Genetic-based Neural Networks (GANN) 68
6.2 Two-phase arbitrage model 70
6.2.1 Phase-I in arbitrage model: Construction of Genetic-based Neural Networks Model while taking in Consideration of Smile Behavior of Volatility 70
6.2.2 Phase-II of arbitrage model: taking in consideration of modeling of tick price movement limitation effect 74
Discussion 76
Chapter 7 Aspiration level analysis for trade-off of risk-returns 78
7.1 Fuzzy multiobjective asset allocation model 78
7.2 Fuzzy linear transformation 79
Discussion 82
Chapter 8 Empirical study of asset allocation 83
8.1 Empirical study and analysis 83
8.1.1 The analysis of linkage phenomenon of leading-price/lagging-price asset in multinational portfolio─ VAR causality test 83
8.1.2 Case study I 84
8.1.3 Case study II 98
8.2 Empirical study of value-at-risk control based asset allocation 105
8.2.1 Simulate random normal return 106
8.2.2 Empirical study of Taiwan weighted stock price index using portfolio insurance model 111
8.2.3 Evidence from PanPacific Markets 115
8.2.4 Evidence from the Japan/Hong Kong/Singapore composite stock price index 118
8.3 Empirical study of arbitrage portfolio 120
8.4 The analysis of GANN-based arbitrage portfolio via volatility skew 128
8.5 Empirical study of fuzzy multiobjective efficient portfolio 132
Summary 133
Chapter 9 Conclusions and Future Works 134
9.1 Scenario-based mean-variance efficient portfolio 134
9.2 Value-at-risk based portfolio insurance model 137
9.3 GNN-based arbitrage portfolio in consideration of volatility skew 138
9.4 Fuzzy multiobjective programming with aspiration level of portfolio 139
Reference 140
Akerlof, G.A. (1970), “The Market for Lemons,” Quarterly Journal of Economics.
Andrews, C., Ford, D. & Mallison, K. (1986), “The Design of Index Fund and Alternative Methods of Replication”, The Investment Analysis, Vol. 82, pp. 116-123.
Arrow, K.J. (1970), ”Essays in the Theory of Risk Bearing,” Amsterdam: North-Holland.
Arshanapolli, B., Doukas, J. & Lang, L. (1995), “Pre and Post-October 1987 Stock Market Linkage between U.S. and Asian Markets,” Pacific-Basin Finance, Vol. 3, pp. 57-73.
Azoff, E. M. (1994), “Neural Network Time Series Forecasting of Financial Markets,” John Wiley & Sons Ltd.
Baillie, R.T. & Degennaro, R. (1990), “Stock Returns and Volatility,” Journal of Financial and Quantitative Analysis, Vol. 25, pp. 203-214.
Bansal, A., Kauffman, R.J. & Weitz, R.R. (1993), “Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: a Business Value Approach,” Journal of Management Information Systems, Vol. 10, No. 1, pp. 11-32.
Bellman, R.E. & Zadeh, L.A. (1970), “Decision Making in a Fuzzy Environment,” Management Science, Vol. 17, pp. 141-164.
Bera, A.K. & Higgins, M.L. (1993), “A Survey of Arch Models- Properties, Estimation and Testing,” The Journal of Economic Surveys, Vol. 7, No. 4.
Black, F. (1988), “Simplifying Portfolio Insurance for Corporate Pension Plans,” Journal of Portfolio Management,” pp. 33-37.
Black, F. & Scholes, M. (1997), “Black-Scholes and Beyond, Option Pricing Model”, IRWIN.
Black, F. & Jones, (1987), “Constant-Proportion Portfolio Insurance,” Journal of Portfolio Management, pp. 48-51.
Black, F. & Scholes, M. (1973), “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy 81, pp. 637-659.
Bollerslev, T. (1986),” Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, Vol. 52, pp.5-59.
Bollerslev, T., Chou, R.Y. & Kroner, K.F. (1992), “Arch Modeling in Finance─A Review of the Theory and Empirical Evidence,” The Journal of Econometrics, Vol. 52.
Bollerslev, T., Engle, R.F. & Nelson, D.B. (1994), “Arch Model in Handbook of Econometrics IV,” eds Engle, R.F. & McFadden, D.C. Amsterdam: Elsrier Science, pp.2959-3038.
Bollerslev, T., Engle, R.F. & Woodridge, J.M. (1988), “A Capital Asset Pricing Model with Time-Varying Covariances,” Journal of Political Economy, Vol. 96, pp. 116-131.
Bookstaber, R. & Langsam, J.A. (1988), “Portfolio Insurance Trading Rules,” Journal of Future Market, pp. 15-31.
Brennan, M.J. & Schwartz, E.S. (1973), “The Pricing of Equity-Linked Life Insurance Policies with an Asset Value Guarantee,” Journal of Financial Economics, Vol. 3, pp. 195-213.
Brennan, M.J. & Solanki, R. (1981), “Optimal Portfolio Insurance,” The Journal of Financial and Quantitative Analysis, Vol. 16, pp. 279-300.
Brennan, M.J. & Schwartz, E.S. (1988), “Time-Invariant Portfolio Insurance Strategies,” The Journal of Finance, Vol. XLIII, No. 2.
Box, G.P. & Jenkins, G.M. (1976), ”Time Series Analysis: Forecasting and Control, Revised Edition, San Francisco: Holden-Day.
Bulsari, A.B. & Saxen, H. (1993), “A Recurrent Neural Networks for Time-Series Modeling,” Artificial Neural Networks and Genetic Algorithms,” Proceedings of the International Conference in Innsbruck, Austria, pp. 285-291.
Canina, L. & Figlewski, S. (1993), “The Review of Financial Studies, Vol.6, No. 3.
Chance, D.M. (2000), “An Introduction to Derivatives,” Harcourt Asia Pte Ltd.
Charles, J.C. & Tie, S. (1997), “Implied Volatility Skews and Stock Index Skewness and Kurtosis Implied by S&P 500 Index Option Prices,” The Journal of Derivatives.
Chen, A.P. & Wang, S.W. (2001), “Theory Modeling of Integrates Hedging Strategies on Derivatives,” Proceeding of the Conference of the Theory and Practice of Finance and Risk Management, pp. 127-135.
Chen, A.P. & Wang, S.W. (2001), “A Novel Genetic-Neural-Based Model in Options Arbitrage via Implied Volatility Skews Effect─Modeling and Evidence in Taiwan Options Market,” Intelligent Data Analysis. (Accept)
Chen, A.P. and Wang, S.W. (2001), “Integrating Scenario-Based Approaches with Mean-Variance Model in Optimizing Global Assets Allocation,” working paper.
Chen, A.P. and Wang, S.W. (2001), ”Theory Modeling and Empirical Evidence for Value-at-Risk Assets Allocation Insurance Strategies─Case Study in PanPacific Markets,” working paper.
Chen, A.P. & Wang, S.W. (2002), “How to Make Efficient Portfolios Successfully? A New Heterogeneous Expectations- based Mean-Variance Model in Optimizing Global Asset Allocation,” working paper.
Chen, A.P. & Wang, S.W. (2002), “The Comparisons of Black-Scholes Model and Genetic-based Neural Networks while Hedging and Speculation with Constrains in Incomplete Markets,” working paper.
Chen, A.P. & Wang, S.W. (2002), “The Black-Scholes Model with Tick Price Movement Limitation and Volatility Skew in Closed-Form,” working paper.
Chen, A.P. & Wang, S.W. (2002), “The Comparisons among Value-at-Risk, Extreme Value and Artificial Intelligence model in Estimating Multiplier of Portfolio Insurance Strategies,” working paper.
Cheung, Y. & Mak, S. (1992), “The International Transmission of Stock Market Fluctuation between the Developed Markets and Asian-Pacific Markets,” Applied Financial Economics, Vol. 2, pp. 43-47.
Chowdhury, A.R. (1994), ”Stock Market Interdependencies: Evidence from the Asian NIEs,” Journal of Macroeconomics, Vol. 16, No. 4, pp. 629-651.
Chriss, N. (1996) ,”Black-Scholes and Beyond: Moden Options Pricing, Irwin: Professional Publishing, Burr Ridge, Illinois.
Cochrane, D. & Orcutt, G.H. (1949), ”Application of Least Squares Regression to Relationships Containing Autocorrelated Error Terms,” Journal of American Statistical Association, Vol. 44, pp. 32-61.
Cox, J.C., Ross, S.A. & Rubinstein, M. (1979), “Option Pricing: A Simplified Approach,” Journal of Financial Economics 7, pp. 229-63.
Curram, S.P. & Mingers, J. (1994), “Neural networks, decision tree induction and discriminate analysis: an empirical comparison,” Journal of Operational Research Society, Vol. 45, No. 4, pp. 440-450.
Dasgupta, C.G., Dispensa, G.S. & Ghose, S. (1994), “Comparing the Predictive Performance of a Neural Network Model with Some Traditional Market Response Models,” International Journal of Forecasting, Vol. 10, pp. 35-244.
Deboeck, Guido, J. (1994), “Trading on the Edge- Neural, Genetic, and Fuzzy Systems for Chaotic Financial Market,” John Wiley & Sons, Inc.
DeBondt, W.D. & Thaler, R. (1985), “Does the Stock Market Overreact? ,” Journal of Finance.
Derman, E. & Kani, I. (1994), “Riding on a Smile,” Risk 7, No. 2, pp. 9-32.
Derman, E., Kani I. & Chriss, N. (1996), “Implied Trinomial Trees of the Volatility Smile,” The Journal of Derivatives.
Dickey, D. & Wayne, A.F. (1979),“Distribution of the Estimates for Autoregressive Time Series with a Unit Root”, Journal of the American Statistical Association, Vol.74, pp. 427-431.
Dickey, D. & Wayne, A.F. (1981), “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,”Econometrica, Vol.49, pp.1057-1072.
Duan, J.C. (1995), “The Garch Option Pricing Model,” Mathematical Finance, Vol. 5, No. 1.
Duffie, D. & Dan, J. (1997),” An Overview of Value at Risk,” Journal of Derivatives, pp. 7-48.
Dufresne, Pierre, C., Keirstead W. & Ross, M.P. (1999), “Martingale Pricing─A Do-It-Yourself Guide to Deriving Black-Scholes,” Equity Derivatives-Applications in Risk Management and Investment.
Dupire, B. (1994), “Pricing with a Smile”, Risk 7, No 1, pp.18-20.
Edvinsson, Leif & Malone, M.S. (2000), “Intellectual Capital,” HarperCollins Publishers, Inc., USA.
Eisenberg, L. & Jarrow, R. (1994), “Option Pricing with Random Volatilities in Complete Market,” The Review of Quantitative Finance and Accounting,” Vol. 4.
Engle, R.F. (1982), “Autoregressive Condititional Heteroscedasicity with Estimates of the Variance of UK Inflation,” Econometrica 50, pp. 987-1008.
Elton, E.J., Gruber, M.J. (1997), “Modern Portfolio Theory, 1950 to date”, Journal of Banking & Finance, Vol. 21, Issue 11~12, pp.1743-1759.
Elston, J.A., Hastie, J.D. & Squires, D. (1999), “Market Linkages between the U.S. and Japan: An Application to the Fisheries Industry,” Japan and the World Economy, Vol. 11, pp. 517-530.
Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflations,” Econometrica, Vol.50, pp. 987-1007.
Engle, R.F., Lilien, D.M. & Robins, R.P. (1987), “Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model,” Econometrica 55, pp. 391-407.
Estep, & kritzman, (1988), “Time-Invariant Portfolio Protection,” Journal of Finance.
Etzioni, S.E. (1986), “Rebalance Disciplines for Portfolio Insurance,” The Journal of Portfolio Management, pp. 59-62.
Eun, C.S., & Shim, S. (1989), “International Transmission of Stock Market Fluctuation between the Developed Markets and the Asian-Pacific Markets,” Applied Financial Economics, Vol. 2, pp. 43-47.
Garcia, C.B. & Gould, F.J. (1987), “An Empirical Study of Portfolio Insurance,” Financial Analysis Journal, pp. 44-54.
Gardner, E.S. (1985), “Exponential Smoothing: The State of the Art,” Journal of Forecasting, Vol. 4, No. 1, pp. 1-38.
Gately, E. (1996), “Neural Networks for Financial Forecasting,” John Wiley & Sons, Inc.
Gavridis, M. (1998), “Modeling with High Frequency Data: A Growing Interest for Financial Economists and Fund Managers,” Nonlinear Modeling of High Frequency Financial Time Series, Published by John Wiley Sons Ltd.
Gemmill, G. & Saflekos, A. (2000), “How Useful is Implied Distributions? Evidence from Stock Index Options,” The Journal of Derivatives.
Gen, Mitsuo & Cheng, Runwei (1997), “Genetic Algorithms and Engineering Design,” John Wiley & Sons, Inc, pp1-41.
Group of Thirty, (1993), “Derivatives: Practices and Principles,” New York.
Grubel, H.G. (1968), “Internationally Diversified Portfolios: Welfare Gains and Capital Flows,” American Economic Review, pp. 1299-1314.
Grubel, Herbert, G. & Fadner, K. (1971), ”The Interdependence of International Equity Markets,” Journal of Finance, pp. 89-94.
Hakansson, N.H. (1970), “Optimal Investment and Consumption Strategies Under Risk for a Class of Utility Functions,” Econometrica, Vol. 38, No. 5, pp. 587-607.
Haug, E.G. (1998), “The Complete Guide to Option Pricing Formulas,” McGraw-Hill.
Heston, S.L. (1993), “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options,” The Review of Financial Studies, Vol. 6, No. 2.
Ho, L.C., Burridge, P., Cadle, John and Theobald, Michael (2000), “Value-at —risk: Applying the extreme value approach to Asian Markets in the recent Financial Turmoil,” Pacific-Basin Finance Journal, Vol. 8, pp.249-275.
Holt, C.C. (1957), “Forecasting Trends and Seasonal by Exponentially Weighted Moving Averages,” O.N.R. Memorandum, No. 52, Carnegie Institute of Technology.
Hull, J.C. (1997), “Options Futures and Other Derivatives,” Prentice-Hall International.
Isreal, S. (1985), “International Equity Markets and the Investment Horizon,” Financial Management, pp. 80-84.
Jeon, B. N. & Furstenberg, G.E. (1990), “Growing International Co-Movement in Stock Price Indexes,” Quarterly Review of Economics and Business, Vol. 30, pp. 15-30.
Jorion, P. (1997), “Value at risk: The New Benchmark for Controlling Market Risk,” The McGraw-Hill Company: Chicago.
Jarrow, R. & Rudd, A. (1982), “Approximate Option Valuation for Arbitrary Stochastic Processes,” Journal of Financial Econpomics, Vol. 10, pp. 347-369.
Jarrow, R. and Rudd, A. (1983), “Option Pricing, Dow Jones-Irwin Publishing, Homewood, Illinois.
J.P. Morgan (1996): Technical Report. 4th Edition, Morgan Guaranty Trust Company: New York.
Kimoto, K., Asakawa, K., Yoda, M. & Takeoka, M. (1990), “Stock market prediction system with modular neural network,” Proceedings of the IEEE International Joint Conference on Neural Networks(IJCNN), San Diego, Vol. 1, pp. 1-6.
King, B. (1996), “Market and Industry Factors in Stock Price Behavior,” Journal of Business, pp. 139-140.
Koehler, A.B. & Murphree, E.S. (1986), “A Comparison of the AIC and BIC on Empirical Data,” Sixth International Symposium on Forecasting, Paris.
Konno, H. (1990), “Piecewise Linear Risk Functions and Portfolio Optimization,” Journal of the Operations Research Society of Japan, Vol. 33, pp. 129-156.
Konno, H. & Yamazaki, H. (1991), “Mean-Absolute Portfolio Optimization Model and It’s Applications to Tokyo Stock Market,” Management Science, Vol. 37, pp. 519-531.
Konno, H. & Suzuki, K. (1992), “A Fast algorithm for solving large scale mean-variance models by compact factorization of covariance matrices,” Journal of the Operations Research Society of Japan, Vol. 35, pp. 93-104.
Konno, H. & Kobayashi, K. (1994), “A Stock-Bond Integrated Portfolio Optimization Model,” Technical Report, Department of Industrial Engineering and Management, Tokyo Institute of Technology, pp. 94-4.
Konno, H. & Kobayashi, K. (1997), “An Integrated Stock-Bond Portfolio Optimization Model,” Journal of Economic Dynamics and Control, pp. 1427- 1444.
Leland, H.E. (1980), “Who Should buy Portfolio Insurance?, “ The Journal of Finance, pp.581-597.
Leland, H.E. (1985), “Option Pricing and Replication with Transaction Costs,” Journal of Finance, Vol. 40, pp. 1283-1301.
Lessard, R.D. (1973), ”International Portfolio Diversification: A Multivariate Analysis for a Group of Latin American Countries,” Journal of Finance, pp. 619-633.
Levy, H. & Sarnat, M. (1979), "Devaluation Risk and the Portfolio Analysis of International Investment,” International Capital Markets, Vol. 1, pp. 177-206.
Liu, Y.A., Pan, M.S. & Shieh, C.P. (1998), “International Transmission of Stock Price Movements: Evidence from US and Five Asian-Pacific Markets,” Journal of Economics and Finance”, Vol. 22, No. 1, pp. 59-69.
Ljung, G.M. & Box, G.P. (1978), ”On a Measure of Lack of Fit in Time Series Models, Biometrika, Vol. 65, pp. 297-303.
Longerstaly, J. & More, L. (1995), “Introduction to RiskMetricsTM. 4th Edition,” Morgan Guaranty Trust Company: New York.
Lyuu, Y.D. (1999), “Financial Engineering and Computation: Principles, Mathematics, Algorithms,” unpublished paper.
Markowitz, H.M. (1952), "Foundation of Portfolio Theory," Journal of Finance, pp. 71-91.
Markowitz, H.M. (1959), “Portfolio Selection: Efficient Diversification of Investments,” Wiley, New York.
Markowitz, H.M. (1988), “Mean Variance Analysis in Portfolio Choice and Capital Markets,” Oxford: Basil Blackwell.
Markowitz, H.M. & Perold A. (1981), “Portfolio Analysis with Scenarios and Factors,” Journal of Finance, Vol. 36, pp. 871-877.
Marquze, L., Hill, T., Worthley, R. & Remus, W. (1991), “Neural network models as an alternative to regression,” Proceedings of the IEEE 24th Artificial Hawaii International Conference on Systems Sciences, Vol.6, pp. 129-135.
Mcdonal, J.G. (1973), ”French Mutual Fund Performance: Evaluation of Internationally Diversified Portfolios,” Journal of Finance, pp. 1161-1180.
Merton, R.C. (1990), “Continuous Time Finance,” Oxford: Blackwell.
Merton, R.C. (1973), “Theory of Rational Option Pricing,” Journal of Economics and Management Science, Vol.4, pp. 141-183.
Merton, R.C. (1973), “The Relationship between Put and Call Prices: Comment,” Journal of Finance, Vol. 28, pp. 183-184.
Meade, N. & Salkin, G.R. (1989), “Index Fund-Construction and Performance Measurement”, Journal of Operational Research Society, Vol. 41, pp. 871-879.
Mizunuma, H., Matsuda, H. & Watada, J. (1996), “Decision Making in Management Based on Fuzzy Mean-Variance Analysis”, Journal of Japan Society for Fuzzy Theory and Systems, Vol. 8, No. 5, pp. 854-860.
Neil, A.C. (1997), “Black-Scholes and Beyond- Option Pricing Models,” IRWIN Professional Publishing.
Nelson, D.B. (1990a), “Stationary and Persistence in the Garch (1,1) Model,” Econometric Theory, Vol. 6, pp. 318-340.
Nelson, D.B. (1990b), “Arch Models as Diffusion Approximations,” Journal of Econometrics, Vol. 45, pp. 7-38.
Nelson, D.B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, Vol. 59, pp. 34-70.
Panton, D.B., Lessig, V.P. & Joy, O.M. (1976),” Comovement of International Equity Markets: A Taxonomic Approach,” Journal of Financial and Quantitative Analysis, pp. 415-432.
Rendleman, R. & Brien, T. (1990), “The Effect of Volatility, Misestimation, …,” .
Perold, A.F. & Shape, W.F. (1988), “Dynamic Strategies for Assets Allocation,” Financial Analysts Journal, pp. 16-27.
Philippatos, G.C., Christofi, A. & Christofi, P. (1983), ”The Inter-Temporal Stability of International Stock Market Relationships: Another View,” Financial Management, pp. 63-69.
Ripley, D.M. (1973), “Systematic Elements in the Linkage of National Stock Market Indices,” The Review of Economic and Statistic, Vol. 55, pp. 356-361.
Risk Magazine (1992), “From Black-Scholes to Black Holes-New Frontiers in Options,” Risk Magazine Ltd.
Robert, L., Goodrish, L. & Stellwagen, E.A. (2000), “Applied Statistical Forecasting,” BFS, Inc.
Rosenberg, J.V. (2000), “Implied Volatility Functions: A Reprise,” The Journal of Derivatives, pp. 51-65.
Roy, A.D. (1985), “Safety First and Holding of Assets,” Econometrica, Vol. 20, pp. 431-439.
Rubinstein, M.E. & Leland, H. (1981), “Replicating Options with Positions in Stock and Cash,” Financial Analysis Journal.
Rubinstein, M.E. (1994), ”Implied Binomial Trees,” Journal of Finance, Vol. 69, pp. 771-818.
Rudd, A. (1987), “Business Risk and Investment Risk,” Investment Management Review, pp. 19-27.
Rudd, A. & Rosenberg, B. (1979), “Realistic Portfolio Optimization,” TIMS Studies in Management Science: Portfolio Theory, No.11, Amsterdam: North Holland Press, pp. 21-46.
Rudd, A., (1980), “Optional Selection of Passive Portfolio”, Financial Management, pp. 56-57.
Samuelson, P.A. (1969), “Lifetime Portfolio Selection by Dynamic Stochastic Programming, ”Review of Economic Studies, Vol. 51, No. 3, pp. 239-246.
Satyajit, D. (1988), ”Risk Management and Financial Derivatives- A Guide to the Mathematics,” The McGraw-Hill Company: Chicago.
Sharpe, W. (1987), “Integrated Asset Allocation,” Financial Analysis.
Shefrin, H. & Statman, M. (1985), “The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence,” Journal of Finance.
Shiller, R.J. (2000), “Irrational Exuberance,” Princeton University Press.
Sharpe, W.F. (1970), ”Portfolio Theory and Capital Markets,” New York: McGraw-Hill.
Spence, A.M. (1973), “Job Market Signaling,” Journal of Economic Letter.
Stein, E.M. & Jeremy, C. S. (1991), “Stock Price Distributions with Stochastic Volatility: An Analytic Approach,” The Review of Financial Studies,” Vol. 4, No. 4.
Stiglitz, J.E. & Rothschild, M. (1976), “Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information,” Quarterly Journal of Economics.
Stuart, A. & Ord, J.K. (1987), “Kendall’s Advanced Theory of Statistics, New York: Oxford University Press.
Tam, K. & Kiang, M. (1992), “Managerial Applications of Neural Networks: the Case of Bank Failure Predictions,” Management Science, Vol. 38, No. 7, pp. 926-947.
Treynor, (1997), “Risk Management and Financial Derivatives”, McGraw-Hill.
Wang, S.W. & Chen, A.P. (2001), “Arch Series Models in Uncertainty- Theorem and Empirical Study,” The 3rd Conference of Statistics and Probability.
Watson, J. (1978), “A Study of Possible Gains from International Investment,” Journal of Business Finance and Accounting, pp. 195-205.
Watson, J. (1980),” The Stationary of Inter-Country Correlation Coefficients: A Note,” Journal of Business Finance and Accounting, pp. 297 ~ pp. 303.
Walter, C. & Lopez, J.A. (2000), “Is Implied Correlation Worth Calculating? Evidence from Foreign Exchange Options,” The Journal of Derivatives, Spring.
Wong, F. & Lee, D. (1993), “A Hybrid Neural Network for Stock Selection,” Proceedings of the 2nd Annual International Conference on Neural Networks.
Wong, F., Tan, P. & Zhang, X. (1992), “Neural Networks, Genetic Algorithms and Fuzzy Logic for Forecasting,” Proceedings of the 3rd International Conference on Advanced Trading Applications on Wall Street and Worldwide.
Zenios, S. & Kang, P. (1993), “Mean Absolute deviation portfolio optimization for mortgage-backed securities,” Annals of Operations Research 45, pp. 433-450.
Zhang, L.H. (1998), “Global Asset Allocation with Multi-risk Considerations,” The Journal of Investing, pp. 7-14.
Zue, Y. & Kavee, R.C. (1988), “Performance of Portfolio Insurance Strategies,” Journal of Portfolio Management, pp. 48-54.
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