overview of quantitative finance and risk management: past, present, and future frontier by dr....

Overview of Quantitative Overview of Quantitative Finance and Risk Finance and Risk

Management: Past, Present, Management: Past, Present, and Future Frontierand Future Frontier

ByByDr. Cheng-Few LeeDr. Cheng-Few Lee

Distinguished Professor, Rutgers University, USADistinguished Professor, Rutgers University, USAEditor, Review of Quantitative Finance and Editor, Review of Quantitative Finance and

AccountingAccountingEditor, Review of Pacific Basin Financial Markets and Editor, Review of Pacific Basin Financial Markets and

PoliciesPolicies

Outline Outline A.A. IntroductionIntroduction

B.B. Theoretical Framework of FinanceTheoretical Framework of Finance

C.C. Investment, Dividend, Financing, and Production Investment, Dividend, Financing, and Production PoliciesPolicies

D.D. Research Methods in Quantitative Finance and Risk Research Methods in Quantitative Finance and Risk ManagementManagement

E.E. Summary and Concluding RemarksSummary and Concluding Remarks

Appendix A: Appendix A: Stochastic Dominance And Capital-Stochastic Dominance And Capital-Structure Analysis Structure Analysis

Appendix B: Brief Table of the contents of the Appendix B: Brief Table of the contents of the Handbook of Quantitative Finance and Risk Handbook of Quantitative Finance and Risk ManagementManagement

Overview of Quantitative Finance Overview of Quantitative Finance and Risk Management: Past, and Risk Management: Past, Present, and Future FrontierPresent, and Future Frontier

AbstractAbstract

Based upon theoretical framework of Based upon theoretical framework of finance, policy framework of finance, and finance, policy framework of finance, and research methods of quantitative finance research methods of quantitative finance and risk management, this paper will and risk management, this paper will reviews, and discusses the overview of i) reviews, and discusses the overview of i) portfolio theory and investment analysis, ii) portfolio theory and investment analysis, ii) options and option pricing theory, and iii) options and option pricing theory, and iii) risk management. In addition, research risk management. In addition, research topics in quantitative finance and risk topics in quantitative finance and risk management will be suggested.management will be suggested.

A. IntroductionA. Introduction The main purpose of this paper is to review theoretical The main purpose of this paper is to review theoretical

framework of finance, alternative policies used in finance, and framework of finance, alternative policies used in finance, and research methods in quantitative finance and risk management. research methods in quantitative finance and risk management. Based upon theoretical framework of finance, policy framework of Based upon theoretical framework of finance, policy framework of finance, and research methods of quantitative finance and risk finance, and research methods of quantitative finance and risk management, this paper will reviews, and discusses the overview of management, this paper will reviews, and discusses the overview of i) portfolio theory and investment analysis, ii) options and option i) portfolio theory and investment analysis, ii) options and option pricing theory, and iii) risk management. In addition, research pricing theory, and iii) risk management. In addition, research topics in quantitative finance and risk management will be topics in quantitative finance and risk management will be suggested.suggested.

The main purpose of section B is to discuss the important finance The main purpose of section B is to discuss the important finance theories. We first discuss discounted cash-flow valuation theory theories. We first discuss discounted cash-flow valuation theory (classical financial theory). Secondly we discuss Modigliani and (classical financial theory). Secondly we discuss Modigliani and Miller (M and M) valuation theory. Thirdly we discuss Markowitz Miller (M and M) valuation theory. Thirdly we discuss Markowitz portfolio theory. We then move on to the discussion of the capital portfolio theory. We then move on to the discussion of the capital asset pricing model (CAPM). The arbitrage pricing theory is asset pricing model (CAPM). The arbitrage pricing theory is discussed following the CAPM. Finally, the option pricing theory discussed following the CAPM. Finally, the option pricing theory and futures valuation and hedging will be discussed.and futures valuation and hedging will be discussed.

A. IntroductionA. Introduction The purpose of section C is to discuss the interaction between investment, The purpose of section C is to discuss the interaction between investment,

financing, and dividends policy of the firm. A brief introduction of the financing, and dividends policy of the firm. A brief introduction of the policy framework of finance in provided in Section C.1. Section C.2 policy framework of finance in provided in Section C.1. Section C.2 discusses the interaction between investment and dividends policy. Section discusses the interaction between investment and dividends policy. Section C.3 discusses the interaction between dividends and financing policy. C.3 discusses the interaction between dividends and financing policy. Section C.4 discusses the interaction between investment and financing Section C.4 discusses the interaction between investment and financing policy. Section C.5 discusses the implications of financing and investment policy. Section C.5 discusses the implications of financing and investment interactions for capital budgeting. Section C.6 discusses the implications of interactions for capital budgeting. Section C.6 discusses the implications of different policies on the beta coefficients. The conclusion is presented in different policies on the beta coefficients. The conclusion is presented in Section C.7.Section C.7.

The main purpose of section D is to discuss important quantitative The main purpose of section D is to discuss important quantitative methods used to do the research in quantitative finance and risk methods used to do the research in quantitative finance and risk management. We first discuss statistics theory and methods. Secondly we management. We first discuss statistics theory and methods. Secondly we discuss econometric methods. Thirdly we discuss mathematics. Finally we discuss econometric methods. Thirdly we discuss mathematics. Finally we discuss other methods such as operation research, stochastic process, discuss other methods such as operation research, stochastic process, computer science and technology, entropy, and fuzzy set theory. computer science and technology, entropy, and fuzzy set theory.

Finally, the results of this paper will be briefly summarized. In Finally, the results of this paper will be briefly summarized. In addition, future research direction in both quantitative finance and risk addition, future research direction in both quantitative finance and risk management will be discussed in detail.management will be discussed in detail.

B1. DISCOUNTED CASH-FLOW VALUATION THEORYB1. DISCOUNTED CASH-FLOW VALUATION THEORY BOND VALUATIONBOND VALUATION PerpetuityPerpetuity Term BondsTerm Bonds

COMMON-STOCK VALUATIONCOMMON-STOCK VALUATIONB2.B2. M AND M THEORY AND OPTIMAL CAPITAL STRUCTURE M AND M THEORY AND OPTIMAL CAPITAL STRUCTURE M and M TheoryM and M Theory Optimal Capital Structure TheoryOptimal Capital Structure Theory

B3. Markowitz Portfolio TheoryB3. Markowitz Portfolio Theory Traditional Portfolio Theory and MethodTraditional Portfolio Theory and Method Programming Models for Portfolio SelectionProgramming Models for Portfolio Selection

B4. CAPITAL ASSET PRICING MODEL (CAPM)B4. CAPITAL ASSET PRICING MODEL (CAPM) Static CAPMStatic CAPM Dynamic CAPMDynamic CAPM

B5. Arbitrage Pricing TheoryB5. Arbitrage Pricing Theory Ross’s Arbitrage Model SpecificationRoss’s Arbitrage Model Specification

B. Theoretical Framework of B. Theoretical Framework of FinanceFinance

B. Theoretical Framework of B. Theoretical Framework of FinanceFinance

B6. OPTION PRICING THEORY B6. OPTION PRICING THEORY Binomial OPMBinomial OPM Black and Scholes OPMBlack and Scholes OPM CEV OPMCEV OPM Other OPMOther OPMB7. Futures Valuation and HedgingB7. Futures Valuation and Hedging

FUTURES MARKETS: OVERVIEWFUTURES MARKETS: OVERVIEWTHE VALUATION OF FUTURES CONTRACTSTHE VALUATION OF FUTURES CONTRACTSThe Arbitrage ArgumentThe Arbitrage ArgumentInterest CostsInterest CostsCarrying CostsCarrying CostsSupply and Demand EffectsSupply and Demand EffectsThe Effect of Hedging DemandThe Effect of Hedging DemandHEDGING CONCEPTS AND STRATEGIESHEDGING CONCEPTS AND STRATEGIESHedging Risks and CostsHedging Risks and CostsThe Johnson Minimum-Variance Hedge StrategyThe Johnson Minimum-Variance Hedge StrategyThe Howard-D’Antonio Optimal Risk-Return Hedge StrategyThe Howard-D’Antonio Optimal Risk-Return Hedge Strategy

B8. Alternative Risk AnalysisB8. Alternative Risk Analysis TheoryTheory ApplicationApplication

C. Investment, Dividend, Financing, and C. Investment, Dividend, Financing, and Production PoliciesProduction Policies

C1. INVESTMENT AND DIVIDEND INTERACTIONSC1. INVESTMENT AND DIVIDEND INTERACTIONSInternal FinancingInternal FinancingExternal FinancingExternal Financing

C2. INTERACTIONS BETWEEN DIVIDEND AND FINANCING POLICIESC2. INTERACTIONS BETWEEN DIVIDEND AND FINANCING POLICIESCost of Equity Capital and Dividend PolicyCost of Equity Capital and Dividend Policy11

Default Risk and Dividend PolicyDefault Risk and Dividend PolicyC3. INTERACTIONS BETWEEN FINANCING AND INVESTMENT DECISIOC3. INTERACTIONS BETWEEN FINANCING AND INVESTMENT DECISIO

NSNS Risk-free Debt CaseRisk-free Debt Case Risky Debt CaseRisky Debt Case

C4. IMPLICATIONS OF FINANCING AND INVESTMENT INTERACTIONS FC4. IMPLICATIONS OF FINANCING AND INVESTMENT INTERACTIONS FOR CAPITAL BUDGETINGOR CAPITAL BUDGETINGEquity-Residual MethodEquity-Residual MethodAfter-Tax, Weighted-Average, Cost-of-Capital MethodAfter-Tax, Weighted-Average, Cost-of-Capital MethodThe Arditti-Levy method is most similar to the after-tax weighted-average cost-The Arditti-Levy method is most similar to the after-tax weighted-average cost-

of-capital of-capital Arditti and Levy MethodArditti and Levy MethodMyers Adjusted-Present-Value MethodMyers Adjusted-Present-Value Method

C. Investment, Dividend, Financing, and C. Investment, Dividend, Financing, and Production PoliciesProduction Policies

C5. IMPACTS OF DIFFERENT POLICIES ON THE BETA C5. IMPACTS OF DIFFERENT POLICIES ON THE BETA COEFFICIENT COEFFICIENT

Impact of Financing Policy on Beta Coefficient DeterminationImpact of Financing Policy on Beta Coefficient Determination

Impact of Production Policy on Beta Coefficient DeterminationImpact of Production Policy on Beta Coefficient Determination

Impact of Dividend Policy on Beta Coefficient DeterminationImpact of Dividend Policy on Beta Coefficient Determination

D. Research Methods in D. Research Methods in Quantitative Quantitative Finance and Risk Management Finance and Risk Management

D1. StatisticsD1. Statistics

Binomial distribution Multinomial distributionBinomial distribution Multinomial distribution

Normal distributionNormal distribution Log-normal distribution Log-normal distribution

Non-central Chi-square distributionNon-central Chi-square distribution

Factor analysis Discriminant analysisFactor analysis Discriminant analysis

Bayesian inferenceBayesian inference Stochastic dominance Stochastic dominance

Characteristics function Spectrum analysisCharacteristics function Spectrum analysis

MLE MethodMLE Method Quasi-MLE Method Quasi-MLE Method

OthersOthers

D. Research Methods in Quantitative D. Research Methods in Quantitative Finance and Risk Management Finance and Risk Management

D2. EconometricsD2. Econometrics

Linear regression models Time series modelingLinear regression models Time series modeling

Multiple equations models Generalized methods of momentsMultiple equations models Generalized methods of moments

Panel data models Panel data models ARM model ARM model

GARCH analyses GARCH analyses Defensive forecasting Defensive forecasting

Spline-GARCHSpline-GARCH Dynamic econometric loss Dynamic econometric loss

Robust logistic regression OthersRobust logistic regression Others

D. Research Methods in Quantitative D. Research Methods in Quantitative Finance and Risk Management Finance and Risk Management

D3. MathematicsD3. MathematicsEquilibrium analysisEquilibrium analysisOptimization problemsOptimization problemsDynamic analysisDynamic analysisItô calculusItô calculusOrdinary differential equation (ODE)Ordinary differential equation (ODE)Fuzzy set theoryFuzzy set theory

OthersOthers

D4. Other Research MethodsD4. Other Research MethodsOperation researchOperation researchMonte Carlo Markov Chain (MCMC) methodMonte Carlo Markov Chain (MCMC) methodEntropy theoryEntropy theoryComputer Science and TechnologyComputer Science and Technology

E. Summary and Concluding E. Summary and Concluding RemarksRemarks

Alternative finance theories, different decision policies, and research Alternative finance theories, different decision policies, and research methodologies are three ingredients for theoretical research in methodologies are three ingredients for theoretical research in quantitative finance and risk management. In this paper, we have quantitative finance and risk management. In this paper, we have reviewed these three ingredients in detail. In addition, accounting, reviewed these three ingredients in detail. In addition, accounting, finance, and market information are important in doing empirical finance, and market information are important in doing empirical research in quantitative finance and risk management.research in quantitative finance and risk management.

Our main concluding remarks related to quantitative finance can Our main concluding remarks related to quantitative finance can be presented as follows:be presented as follows:

1.1. Quantitative finance is one of the most popular research subjects for Quantitative finance is one of the most popular research subjects for both academicians and practitioners. both academicians and practitioners.

2.2. Derivatives are popular financial instruments until financial crisis Derivatives are popular financial instruments until financial crisis occurred in 2008. occurred in 2008.

3.3. Risk management becomes more important after financial crisis Risk management becomes more important after financial crisis occurred in 2008. occurred in 2008.

4.4. Information, theory, policy, and methodology are important ingredients Information, theory, policy, and methodology are important ingredients for the research of Quantitative Finance and Risk Management.for the research of Quantitative Finance and Risk Management.

AppendixAppendix A A: : STOCHASTIC DOMINANCE AND ITS APPLICATIONS TO STOCHASTIC DOMINANCE AND ITS APPLICATIONS TO CAPITAL-STRUCTURE ANALYSIS WITH DEFAULT RISKCAPITAL-STRUCTURE ANALYSIS WITH DEFAULT RISK

A.1A.1 INTRODUCTIONINTRODUCTION

A.2A.2 CONCEPTS AND THEOREMS OF CONCEPTS AND THEOREMS OF

STOCHASTIC STOCHASTIC DOMINANCEDOMINANCE

A.3A.3 STOCHASTIC-DOMINANCE APPROACH TO STOCHASTIC-DOMINANCE APPROACH TO

INVESTIGATING THE CAPITAL-STRUCTURE INVESTIGATING THE CAPITAL-STRUCTURE

PROBLEM WITH DEFAULT RISKPROBLEM WITH DEFAULT RISK

A.4A.4 SUMMARYSUMMARY

APPENDIX B.APPENDIX B. Brief Table of the contents of the Brief Table of the contents of the Handbook of Quantitative Finance and Risk ManagementHandbook of Quantitative Finance and Risk Management

PREFACEPREFACE

List of ContributorsList of Contributors

Part I – Overview of Quantitative Finance and Risk Management Part I – Overview of Quantitative Finance and Risk Management ResearchResearch

ByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAAlice C. Lee, State Street Corp., USAAlice C. Lee, State Street Corp., USAJohn Lee, Center for PBBEF Research, USAJohn Lee, Center for PBBEF Research, USA

Chapter A. Theoretical Framework of FinanceChapter A. Theoretical Framework of Finance

Chapter B. Investment, Dividend, Financing, and Production Policies: Theory Chapter B. Investment, Dividend, Financing, and Production Policies: Theory and Implicationsand Implications

Chapter C. Research Methods of Quantitative Finance and Risk ManagementChapter C. Research Methods of Quantitative Finance and Risk Management


Part II –Portfolio Theory and Investment AnalysisPart II –Portfolio Theory and Investment AnalysisIntroductionIntroductionChapter 1Chapter 1 Foundation of Portfolio TheoryFoundation of Portfolio Theory

ByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAAlice C. Lee, State Street Corp., USAAlice C. Lee, State Street Corp., USAJohn Lee, Center for PBBEF Research, USAJohn Lee, Center for PBBEF Research, USA

Chapter 2Chapter 2 Risk-Aversion, Capital Asset Allocation, and Markowitz Portfolio-Selection ModelRisk-Aversion, Capital Asset Allocation, and Markowitz Portfolio-Selection ModelByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USAHong-Yi Chen, Rutgers University, USAHong-Yi Chen, Rutgers University, USA

Chapter 3Chapter 3 Capital Asset Pricing Model and Beta ForecastingCapital Asset Pricing Model and Beta ForecastingByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USADonald H. Wort, California State University East Bay, USADonald H. Wort, California State University East Bay, USA

Chapter 4Chapter 4 Index Models for Portfolio SelectionIndex Models for Portfolio SelectionByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USADonald H. Wort, California State University East Bay, USADonald H. Wort, California State University East Bay, USA


Chapter 5Chapter 5 Sharpe Measure, Treynor Measure and Optimal Portfolio SelectionSharpe Measure, Treynor Measure and Optimal Portfolio SelectionByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAHong-Yi Chen, Rutgers University, USAHong-Yi Chen, Rutgers University, USAJessica Mai, Rutgers University, USAJessica Mai, Rutgers University, USA

Chapter 6Chapter 6 The Creation and Control of Speculative Bubbles in a Laboratory SettingThe Creation and Control of Speculative Bubbles in a Laboratory SettingByByJames S. Ang, Florida State University, USAJames S. Ang, Florida State University, USADean Diavatopoulos, Villanova University, USADean Diavatopoulos, Villanova University, USAThomas V. Schwarz, Grand Valley State University, USAThomas V. Schwarz, Grand Valley State University, USA

Chapter 7Chapter 7 Portfolio Optimization Models and Mean-Variance Spanning Tests Portfolio Optimization Models and Mean-Variance Spanning Tests ByByWei-Peng Chen, Shih Hsin University, TaiwanWei-Peng Chen, Shih Hsin University, TaiwanHuimin Chung, National Chiao Tung University, TaiwanHuimin Chung, National Chiao Tung University, TaiwanKeng-Yu Ho, National Taiwan University, Taiwan Keng-Yu Ho, National Taiwan University, Taiwan Tsui-Ling Hseu, National Chiao Tung University, Taiwan Tsui-Ling Hseu, National Chiao Tung University, Taiwan

Chapter 8Chapter 8 Combining Fundamental Measures for Stock SelectionCombining Fundamental Measures for Stock SelectionByByKenton K. Yee, Columbia Business School, USAKenton K. Yee, Columbia Business School, USA


Chapter 9Chapter 9 On Estimation Risk and Power Utility Portfolio Selection On Estimation Risk and Power Utility Portfolio Selection ByByRobert R. Grauer, Simon Fraser University, CanadaRobert R. Grauer, Simon Fraser University, CanadaFrederick C. Shen, Coventree Inc, CanadaFrederick C. Shen, Coventree Inc, Canada

Chapter 10Chapter 10 International Portfolio Management: Theory and MethodInternational Portfolio Management: Theory and MethodByByWan-Jiun Paul Chiou, Shippensburg University, USAWan-Jiun Paul Chiou, Shippensburg University, USACheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USA

Chapter 11Chapter 11 The Le Châtelier Principle in the Markowitz Quadratic Programming Investment Model: A CThe Le Châtelier Principle in the Markowitz Quadratic Programming Investment Model: A Case of World Equity Fund Market ase of World Equity Fund Market ByByChin W. Yang, Clarion University of Pennsylvania, USAChin W. Yang, Clarion University of Pennsylvania, USAKen Hung, Texas A&M International University, USAKen Hung, Texas A&M International University, USA

Jing Cui, Clarion University of Pennsylvania, USAJing Cui, Clarion University of Pennsylvania, USA

Chapter 12Chapter 12 Risk-Averse Portfolio Optimization via Stochastic Dominance ConstraintsRisk-Averse Portfolio Optimization via Stochastic Dominance Constraints ByBy

Darinka Dentcheva, Stevens Institute of Technology, USADarinka Dentcheva, Stevens Institute of Technology, USA Andrzej Ruszczynski, Rutgers University, USAAndrzej Ruszczynski, Rutgers University, USA

Chapter 13Chapter 13 Portfolio AnalysisPortfolio AnalysisByByJack Clark Francis, Baruch College, USAJack Clark Francis, Baruch College, USA


Chapter 14Chapter 14 Portfolio Theory, CAPM, and Performance MeasuresPortfolio Theory, CAPM, and Performance MeasuresByByLuis Ferruz, University of Zaragoza, SpainLuis Ferruz, University of Zaragoza, SpainFernando Gómez-Bezares, University of Deusto, SpainFernando Gómez-Bezares, University of Deusto, SpainMaría Vargas, University of Zaragoza, SpainMaría Vargas, University of Zaragoza, Spain

Chapter 15Chapter 15 Intertemporal Equilibrium Models, Portfolio Theory and the Capital Asset Pricing ModelIntertemporal Equilibrium Models, Portfolio Theory and the Capital Asset Pricing ModelByByStephen J. Brown, New York University, USAStephen J. Brown, New York University, USA

Chapter 16Chapter 16 Persistence, Predictability and Portfolio PlanningPersistence, Predictability and Portfolio PlanningByByMichael J. Brennan, University of California at Los Angeles, USAMichael J. Brennan, University of California at Los Angeles, USAYihong Xia, Wharton School, USAYihong Xia, Wharton School, USA

Chapter 17Chapter 17 Portfolio Insurance Strategies – Review of Theory and Empirical StudiesPortfolio Insurance Strategies – Review of Theory and Empirical StudiesByByLan-chih Ho, Central Bank of the Republic of China, TaiwanLan-chih Ho, Central Bank of the Republic of China, TaiwanJohn Cadle, University of Birmingham, U.K.John Cadle, University of Birmingham, U.K.Michael Theobald, University of Birmingham, U.K.Michael Theobald, University of Birmingham, U.K.

Chapter 18Chapter 18 Security Market Microstructure: The Analysis of a Non-Frictionless MarketSecurity Market Microstructure: The Analysis of a Non-Frictionless Market ByBy

Reto Francioni, Deutsche Bank, USAReto Francioni, Deutsche Bank, USASonali Hazarika, Baruch College, USASonali Hazarika, Baruch College, USAMartin Reck, Deutsche Bank, USAMartin Reck, Deutsche Bank, USARobert A. Schwartz, Baruch College, USARobert A. Schwartz, Baruch College, USA


Part III – Options and Option Pricing TheoryPart III – Options and Option Pricing Theory

IntroductionIntroduction

Chapter 19Chapter 19 Option Strategies and Their ApplicationsOption Strategies and Their ApplicationsByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJohn Lee, Center for PBBEF Research, USAJohn Lee, Center for PBBEF Research, USAWei-Kang ShihWei-Kang Shih, Rutgers University, USA, Rutgers University, USA

Chapter 20Chapter 20 Option Pricing Theory and Firm ValuationOption Pricing Theory and Firm ValuationByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USAJoseph E. Finnerty, University of Illinois at Urbana-Champaign, USAWei-Kang Shih, Rutgers University, USAWei-Kang Shih, Rutgers University, USA

Chapter 21Chapter 21 Applications of the Binomial Distribution to Evaluate Call OptionsApplications of the Binomial Distribution to Evaluate Call OptionsByByAlice C. Lee, State Street Corp., USAAlice C. Lee, State Street Corp., USAJohn Lee, Center for PBBEF Research, USA John Lee, Center for PBBEF Research, USA Jessica Mai, Rutgers University, USAJessica Mai, Rutgers University, USA

Chapter 22Chapter 22 Multinomial Option Pricing ModelMultinomial Option Pricing ModelByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJack C. Lee, National Chiao Tung University, TaiwanJack C. Lee, National Chiao Tung University, Taiwan


Chapter 23Chapter 23 Two Alternative Binomial Option Pricing Model Approaches to Derive Black-Scholes OptioTwo Alternative Binomial Option Pricing Model Approaches to Derive Black-Scholes Option Pricing Modeln Pricing Model

ByBy Cheng-Few Lee, Rutgers University, USACheng-Few Lee, Rutgers University, USA Carle Shu Ming Lin, Rutgers University, USACarle Shu Ming Lin, Rutgers University, USA

Chapter 24Chapter 24 Normal, Lognormal Distribution and Option Pricing ModelNormal, Lognormal Distribution and Option Pricing ModelByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJack C. Lee, National Chiao Tung University, TaiwanJack C. Lee, National Chiao Tung University, TaiwanAlice C. Lee, State Street Corp., USAAlice C. Lee, State Street Corp., USA

Chapter 25Chapter 25 Bivariate Normal Option Pricing ModelsBivariate Normal Option Pricing ModelsByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAAlice C. Lee, State Street Corp., USAAlice C. Lee, State Street Corp., USAJohn Lee, Center for PBBEF Research, USAJohn Lee, Center for PBBEF Research, USA

Chapter 26Chapter 26 Displaced Log Normal and Lognormal American Option Pricing: A ComparisonDisplaced Log Normal and Lognormal American Option Pricing: A ComparisonBy By Ren-Raw Chen, Rutgers University, USARen-Raw Chen, Rutgers University, USACheng-Few Lee, Rutgers University, USACheng-Few Lee, Rutgers University, USA

Chapter 27Chapter 27 Itô’s Calculus and the Derivation of the Black-Scholes Option-Pricing ModelItô’s Calculus and the Derivation of the Black-Scholes Option-Pricing Model ByBy George Chalamandaris, Athens University of Economics and Business, GreeceGeorge Chalamandaris, Athens University of Economics and Business, Greece

A.G. Malliaris, Loyola University Chicago, USAA.G. Malliaris, Loyola University Chicago, USA


Chapter 28Chapter 28 Constant Elasticity of Variance (CEV) Option Pricing Model: Integration and Detailed DerivaConstant Elasticity of Variance (CEV) Option Pricing Model: Integration and Detailed Derivation (reprint)tion (reprint)ByByYing Lin Hsu, National Chung Hsing University, TaiwanYing Lin Hsu, National Chung Hsing University, TaiwanT. I. Lin, National Chung Hsing University, TaiwanT. I. Lin, National Chung Hsing University, TaiwanCheng Few Lee, Rutgers University, USA and National Chiao Tung University, TaiwanCheng Few Lee, Rutgers University, USA and National Chiao Tung University, Taiwan

Chapter 29Chapter 29 Stochastic Volatility Option Pricing ModelsStochastic Volatility Option Pricing ModelsByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAJack C. Lee, National Chiao Tung University, TaiwanJack C. Lee, National Chiao Tung University, Taiwan

Chapter 30Chapter 30 Derivations and Applications of Greek Letters – Review and IntegrationDerivations and Applications of Greek Letters – Review and Integration ByBy

Hong-Yi Chen, Rutgers University, USAHong-Yi Chen, Rutgers University, USACheng-Few Lee, Rutgers University, USACheng-Few Lee, Rutgers University, USAWeikang Shih, Rutgers University, USAWeikang Shih, Rutgers University, USA

Chapter 31Chapter 31 A Further Analysis of Convergence Rate and Pattern of the A Further Analysis of Convergence Rate and Pattern of the Binomial ModelsBinomial ModelsByBySan-Lin Chung, National Taiwan University, TaiwanSan-Lin Chung, National Taiwan University, TaiwanPai-Ta Shih, National Taiwan University, TaiwanPai-Ta Shih, National Taiwan University, Taiwan

Chapter 32Chapter 32 Estimating Implied Probabilities From Option Prices and the UnderlyingEstimating Implied Probabilities From Option Prices and the UnderlyingByByBruce Mizrach, Rutgers University, USABruce Mizrach, Rutgers University, USA


Chapter 33Chapter 33 Are Tails Fat Enough to Explain SmileAre Tails Fat Enough to Explain Smile ByBy

Ren-Raw Chen, Rutgers University, USARen-Raw Chen, Rutgers University, USA Oded Palmon, Rutgers University, USAOded Palmon, Rutgers University, USA John Wald, Pennsylvania State University, USAJohn Wald, Pennsylvania State University, USA

Chapter 34Chapter 34 Option Pricing and Hedging Performance under Stochastic Volatility and Stochastic Interest RateOption Pricing and Hedging Performance under Stochastic Volatility and Stochastic Interest Ratess

ByByGurdip Bakshi, University of Maryland, USAGurdip Bakshi, University of Maryland, USACharles Cao, Penn State University, USACharles Cao, Penn State University, USAZhiwu Chen, Yale University, USAZhiwu Chen, Yale University, USA

Chapter 35Chapter 35 Application of the Characteristic Function in Financial ResearchApplication of the Characteristic Function in Financial ResearchByByH.W. Chuang, National Taiwan University, Taiwan H.W. Chuang, National Taiwan University, Taiwan Ying-Lin Hsu, National Chung Hsing University, TaiwanYing-Lin Hsu, National Chung Hsing University, TaiwanCheng-Few Lee, Rutgers University, USACheng-Few Lee, Rutgers University, USA

Chapter 36Chapter 36 Asian OptionsAsian Options By By Itzhak Venezia, Hebrew University, IsraelItzhak Venezia, Hebrew University, Israel

Chapter 37Chapter 37 Numerical Valuation of Asian Options with Higher Moments in the Underlying Distribution Numerical Valuation of Asian Options with Higher Moments in the Underlying Distribution ByByKehluh Wang, National Chiao Tung University, TaiwanKehluh Wang, National Chiao Tung University, TaiwanMing-Feng Hsu,Tatung University, TaiwanMing-Feng Hsu,Tatung University, Taiwan


Chapter 38Chapter 38 The Valuation of Uncertain Income Streams and the Pricing of Options (Reprint)The Valuation of Uncertain Income Streams and the Pricing of Options (Reprint)ByByMark Rubinstein, University of California Berkley, USAMark Rubinstein, University of California Berkley, USA

Chapter 39Chapter 39 Binomial OPM, Black-Scholes OPM and Their Relationship: Decision Tree and Microsoft ExBinomial OPM, Black-Scholes OPM and Their Relationship: Decision Tree and Microsoft Excel Approachcel ApproachByByJohn Lee, Center for PBBEF Research, USAJohn Lee, Center for PBBEF Research, USA

Part IV - Risk ManagementPart IV - Risk Management


Chapter 40Chapter 40 Combinatorial Methods for Constructing Credit Risk RatingsCombinatorial Methods for Constructing Credit Risk RatingsByByAlexander Kogan, Rutgers University, USAAlexander Kogan, Rutgers University, USAMiguel A. Lejeune, George Washington University, USAMiguel A. Lejeune, George Washington University, USA

Chapter 41Chapter 41 The Structural Approach to Modeling Credit RiskThe Structural Approach to Modeling Credit Risk ByBy

Jingzhi Huang, Pennsylvania State University, USAJingzhi Huang, Pennsylvania State University, USA

Chapter 42Chapter 42 An Empirical Investigation of the Rationales for Integrated Risk-Management BehaviorAn Empirical Investigation of the Rationales for Integrated Risk-Management Behavior ByBy Michael S. Pagano, Villanova University, USAMichael S. Pagano, Villanova University, USA


Chapter 43Chapter 43 Copula, Correlated Defaults and Credit VaRCopula, Correlated Defaults and Credit VaRBy By Jow-Ran Chang, National Tsing Hua University, TaiwanJow-Ran Chang, National Tsing Hua University, TaiwanAn-Chi Chen, KGI Securities Co. Ltd., TaiwanAn-Chi Chen, KGI Securities Co. Ltd., Taiwan

Chapter 44Chapter 44 Unspanned Stochastic Volatilities and and Interest Rate Derivatives PricingUnspanned Stochastic Volatilities and and Interest Rate Derivatives Pricing ByBy Feng Zhao, Rutgers University, USAFeng Zhao, Rutgers University, USA

Chapter 45Chapter 45 Catastrophic Losses and Alternative Risk Transfer InstrumentsCatastrophic Losses and Alternative Risk Transfer InstrumentsByByJin-Ping Lee, Feng Chia University, TaiwanJin-Ping Lee, Feng Chia University, TaiwanMin-Teh Yu, Providence University, TaiwanMin-Teh Yu, Providence University, Taiwan

Chapter 46 Chapter 46 A Real Option Approach to the Comprehensive Analysis of Bank Consolidation ValuesA Real Option Approach to the Comprehensive Analysis of Bank Consolidation ValuesByByChuang-Chang Chang, National Central University, TaiwanChuang-Chang Chang, National Central University, TaiwanPei-Fang Hsieh, National Central University, TaiwanPei-Fang Hsieh, National Central University, TaiwanHung-Neng Lai, National Central University, TaiwanHung-Neng Lai, National Central University, Taiwan

Chapter 47Chapter 47 Dynamic Econometric Loss Model: A Default Study of US Subprime MarketsDynamic Econometric Loss Model: A Default Study of US Subprime MarketsByByC.H. Ted Hong, Beyondbond, Inc., USAC.H. Ted Hong, Beyondbond, Inc., USA


Chapter 48Chapter 48 The Effect of Default Risk on Equity Liquidity: Evidence Based on the Panel Threshold ModThe Effect of Default Risk on Equity Liquidity: Evidence Based on the Panel Threshold Modelel

ByByHuimin Chung, National Chiao Tung University, TaiwanHuimin Chung, National Chiao Tung University, TaiwanWei-Peng Chen, Shih-Hsin University, Taiwan Wei-Peng Chen, Shih-Hsin University, Taiwan Yu-Dan Chen, National Chiao Tung University, TaiwanYu-Dan Chen, National Chiao Tung University, Taiwan

Chapter 49Chapter 49 Put option approach to determine bank risk premiumPut option approach to determine bank risk premiumByByDar-Yeh Huang, National Taiwan University, TaiwanDar-Yeh Huang, National Taiwan University, TaiwanFu-Shuen Shie, National Taiwan University, TaiwanFu-Shuen Shie, National Taiwan University, TaiwanWei-Hsiung Wu, National Taiwan University, TaiwanWei-Hsiung Wu, National Taiwan University, Taiwan

Chapter 50Chapter 50 Keiretsu Style Main Bank Relationships, R&D Investment, Leverage, and Firm Value: EvideKeiretsu Style Main Bank Relationships, R&D Investment, Leverage, and Firm Value: Evidence from Japanese Listed Companiesnce from Japanese Listed Companies

ByBy Hai-Chin Yu, Chung Yuan University, TaiwanHai-Chin Yu, Chung Yuan University, Taiwan Chih-Sean Chen, Chung Yuan University, TaiwanChih-Sean Chen, Chung Yuan University, Taiwan Der-Tzon Hsieh, National Taiwan University, Taiwan Der-Tzon Hsieh, National Taiwan University, Taiwan

Chapter 51Chapter 51 On the Feasibility of LadderingOn the Feasibility of LadderingByByJoshua Ronen, New York University, USAJoshua Ronen, New York University, USABharat Sarath, Baruch College, USABharat Sarath, Baruch College, USA


Chapter 52Chapter 52 Stock Returns, Extreme Values, and Conditional Skewed DistributionStock Returns, Extreme Values, and Conditional Skewed DistributionByBy

Thomas C. Chiang, Drexel University, USAThomas C. Chiang, Drexel University, USAJiandong Li, Central University of Finance and Economics, P.R. ChinaJiandong Li, Central University of Finance and Economics, P.R. China

Chapter 53Chapter 53 Capital Structure in Asia and CEO EntrenchmentCapital Structure in Asia and CEO EntrenchmentByByKin Wai Lee, Nanyang Technological University, SingaporeKin Wai Lee, Nanyang Technological University, SingaporeGillian Hian Heng Yeo, Nanyang Technological University, SingaporeGillian Hian Heng Yeo, Nanyang Technological University, Singapore

Chapter 54Chapter 54 A Generalized Model for Optimum Futures Hedge Ratio A Generalized Model for Optimum Futures Hedge Ratio ByByCheng-Few Lee, Rutgers University, USACheng-Few Lee, Rutgers University, USAJang-Yi Lee, Tunghai University, TaiwanJang-Yi Lee, Tunghai University, TaiwanKehluh Wang, National Chiao-Tung University, TaiwanKehluh Wang, National Chiao-Tung University, TaiwanYuan-Chung Sheu, National Chiao-Tung University, TaiwanYuan-Chung Sheu, National Chiao-Tung University, Taiwan

Chapter 55Chapter 55 The Sensitivity of Corporate Bond Volatility to Macroeconomic AnnouncementsThe Sensitivity of Corporate Bond Volatility to Macroeconomic AnnouncementsByBy

Nikolay Kosturov, University of Oklahoma, USANikolay Kosturov, University of Oklahoma, USA Duane Stock, University of Oklahoma, USADuane Stock, University of Oklahoma, USA

Chapter 56Chapter 56 Raw Material Convenience Yields and Business Cycle Raw Material Convenience Yields and Business CycleByByChang-Wen Duan, Tamkang University, TaiwanChang-Wen Duan, Tamkang University, TaiwanWilliam T. Lin, Tamkang University, TaiwanWilliam T. Lin, Tamkang University, Taiwan


Chapter 57Chapter 57 Alternative Methods to Determine Optimal Capital Structure: Theory and ApplicationAlternative Methods to Determine Optimal Capital Structure: Theory and ApplicationByBySheng-Syan Chen, National Taiwan University, TaiwanSheng-Syan Chen, National Taiwan University, Taiwan

Cheng-Few Lee, Rutgers University, USA and National Chiao Tung University, TaiwanCheng-Few Lee, Rutgers University, USA and National Chiao Tung University, TaiwanHan-Hsing Lee, National Chiao Tung University, TaiwanHan-Hsing Lee, National Chiao Tung University, Taiwan

Chapter 58Chapter 58 Actuarial mathematics and its applications in quantitative financeActuarial mathematics and its applications in quantitative financeByByCho-Jieh Chen, University of Alberta, CanadaCho-Jieh Chen, University of Alberta, Canada

Chapter 59Chapter 59 The Prediction of Default With Outliers--Robust Logistic RegressionThe Prediction of Default With Outliers--Robust Logistic RegressionByByChung-Hua Shen, National Taiwan University, TaiwanChung-Hua Shen, National Taiwan University, TaiwanYi-Kai Chen, National University of Kaohsiung, TaiwanYi-Kai Chen, National University of Kaohsiung, TaiwanBor-Yi Huang, Shih Chien University, TaiwanBor-Yi Huang, Shih Chien University, Taiwan

Chapter 60Chapter 60 Term Structure of Default-Free and Defaultable Securities: Theory and EvidenceTerm Structure of Default-Free and Defaultable Securities: Theory and EvidenceByByHai Lin, Xiamen University, ChinaHai Lin, Xiamen University, ChinaChunChi Wu, University of Missouri, USAChunChi Wu, University of Missouri, USA

Chapter 61Chapter 61 Liquidity Risk and Arbitrage Pricing Theory (Reprint)Liquidity Risk and Arbitrage Pricing Theory (Reprint)ByByUmut Cetin, Technische University Wein, USAUmut Cetin, Technische University Wein, USARobert A. Jarrow, Cornell University, USARobert A. Jarrow, Cornell University, USAPhilip Protter, Cornell University, USAPhilip Protter, Cornell University, USA


Chapter 62Chapter 62 An integrated Model of Debt Issuance, Refunding, and Maturity (Reprint)An integrated Model of Debt Issuance, Refunding, and Maturity (Reprint)ByByManak C. Gupta, Temple University, USAManak C. Gupta, Temple University, USAAlice C. Lee, State Street Corp., USAAlice C. Lee, State Street Corp., USA

Part V – Theory, Methodology and ApplicationsPart V – Theory, Methodology and Applications


Chapter 63Chapter 63 Business Models: Applications to Capital Budgeting, Equity Value and Return AttributiBusiness Models: Applications to Capital Budgeting, Equity Value and Return Attributionon

ByByThomas S. Y. Ho, Thomas Ho Company, Ltd, USAThomas S. Y. Ho, Thomas Ho Company, Ltd, USASang Bin Lee, Hanyang University, KoreaSang Bin Lee, Hanyang University, Korea

Chapter 64Chapter 64 Dividends vs. Reinvestments in Continuous Time: A More General ModelDividends vs. Reinvestments in Continuous Time: A More General ModelByByRen-Raw Chen, Rutgers University, USARen-Raw Chen, Rutgers University, USABen LoganBen LoganOded Palmon, Rutgers University, USAOded Palmon, Rutgers University, USALarry Shepp, Rutgers University, USALarry Shepp, Rutgers University, USA


Chapter 65Chapter 65 Segmenting financial services market: An Empirical Study of Statistical and Non-parametric Segmenting financial services market: An Empirical Study of Statistical and Non-parametric MethodsMethodsByByKenneth Lawrence, New Jersey Institute of Technology, USAKenneth Lawrence, New Jersey Institute of Technology, USADinesh Pai, Rutgers University, USADinesh Pai, Rutgers University, USARonald Klimberg, St. Joseph’s University, USARonald Klimberg, St. Joseph’s University, USAStephen Kudbya, New Jersey Institute of Technology, USAStephen Kudbya, New Jersey Institute of Technology, USASheila Lawrence, Rutgers University, USASheila Lawrence, Rutgers University, USA

Chapter 66Chapter 66 Spurious Regression and Data Mining in Conditional Asset Spurious Regression and Data Mining in Conditional Asset Pricing ModelsPricing ModelsByByWayne Ferson, University of Southern California, USAWayne Ferson, University of Southern California, USASergei Sarkissian, McGill University, USASergei Sarkissian, McGill University, USATimothy Simin, Pennsylvania State University, USATimothy Simin, Pennsylvania State University, USA

Chapter 67 Chapter 67 Issues Related with the Errors-In-Variables Problems in Asset Pricing TestsIssues Related with the Errors-In-Variables Problems in Asset Pricing TestsByByDongcheol Kim, Korea University Business School, KoreaDongcheol Kim, Korea University Business School, Korea

Chapter 68Chapter 68 MCMC Estimation of Multiscale Stochastic Volatility ModelsMCMC Estimation of Multiscale Stochastic Volatility ModelsByByGerman Molina, Statistical and Applied Mathematical Sciences Institute, USAGerman Molina, Statistical and Applied Mathematical Sciences Institute, USAChuan-Hsiang Han, National Tsing Hua University, TaiwanChuan-Hsiang Han, National Tsing Hua University, TaiwanJean-Pierre Fouque, University of California, USAJean-Pierre Fouque, University of California, USA


Chapter 69 Chapter 69 Regime Shifts and the Term Structure of Interest RatesRegime Shifts and the Term Structure of Interest RatesByByChien-Chung Nieh, Tamkang University, TaiwanChien-Chung Nieh, Tamkang University, TaiwanShu Wu, The University of Kansas, USAShu Wu, The University of Kansas, USAYong Zeng, The University of Missouri at Kansas City, USAYong Zeng, The University of Missouri at Kansas City, USA

Chapter 70 Chapter 70 ARM Processes and Their Modeling and Forecasting MethodologyARM Processes and Their Modeling and Forecasting MethodologyByByBenjamin Melamed, Rutgers Business School, USABenjamin Melamed, Rutgers Business School, USA

Chapter 71Chapter 71 Alternative Econometric Methods for Information-based Equity-selling MechanismsAlternative Econometric Methods for Information-based Equity-selling MechanismsByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAYi Lin Wu, National Tsing Hua University, TaiwanYi Lin Wu, National Tsing Hua University, Taiwan

Chapter 72Chapter 72 Implementation Problems and Solutions in Stochastic Volatility Models of the Heston TypeImplementation Problems and Solutions in Stochastic Volatility Models of the Heston TypeByByJia-Hau Guo, Soochow University, TaiwanJia-Hau Guo, Soochow University, TaiwanMao-Wei Hung, National Taiwan University, TaiwanMao-Wei Hung, National Taiwan University, Taiwan

Chapter 73Chapter 73 Revisiting Volume Versus GARCH Effects Using Univariate and Bivariate GARCH Models: Revisiting Volume Versus GARCH Effects Using Univariate and Bivariate GARCH Models: Evidence from US Stock MarketsEvidence from US Stock MarketsByByZhuo Qiao, University of Macau, China Zhuo Qiao, University of Macau, China Wing-Keung Wong, Hong Kong Baptist University, Hong KongWing-Keung Wong, Hong Kong Baptist University, Hong Kong


Chapter 74Chapter 74 Application of Fuzzy Set Theory to Finance Research: Method and ApplicationApplication of Fuzzy Set Theory to Finance Research: Method and ApplicationByByShin-Yun Wang, National Dong Hwa University, TaiwanShin-Yun Wang, National Dong Hwa University, TaiwanCheng-Few Lee, Rutgers University, USACheng-Few Lee, Rutgers University, USA

Chapter 75Chapter 75 Hedonic Regression Analysis in Real Estate Markets: A PrimerHedonic Regression Analysis in Real Estate Markets: A PrimerByByBen J. Sopranzetti, Rutgers University, USABen J. Sopranzetti, Rutgers University, USA

Chapter 76Chapter 76 Numerical Solutions of Financial Partial Differential EquationsNumerical Solutions of Financial Partial Differential EquationsByByGang Nathan Dong, Rutgers University, USAGang Nathan Dong, Rutgers University, USA

Chapter 77Chapter 77 A Primer on the Implicit Financing Assumptions of Traditional Capital Budgeting ApproacheA Primer on the Implicit Financing Assumptions of Traditional Capital Budgeting Approaches s ByByIvan Brick, Rutgers University, USAIvan Brick, Rutgers University, USADaniel Weaver, Rutgers University, USADaniel Weaver, Rutgers University, USA

Chapter 78Chapter 78 Determinants of Flows into U.S. Based International Mutual FundsDeterminants of Flows into U.S. Based International Mutual FundsBy By

Dilip K. Patro, Office of the Comptroller of the Currency, USADilip K. Patro, Office of the Comptroller of the Currency, USA

Chapter 79Chapter 79 Predicting prices using defensive forecastingPredicting prices using defensive forecastingByByGlenn Shafer, Rutgers University, USAGlenn Shafer, Rutgers University, USASam Ring, Rutgers University, USASam Ring, Rutgers University, USA


Chapter 80Chapter 80 Range Volatility Models and Their Applications in FinanceRange Volatility Models and Their Applications in FinanceByByRay Y Chou, Academia Sinica, TaiwanRay Y Chou, Academia Sinica, TaiwanHeng-chih Chou, Ming Chuan University, TaiwanHeng-chih Chou, Ming Chuan University, TaiwanNathan Liu, National Chiao Tung University, TaiwanNathan Liu, National Chiao Tung University, Taiwan

Chapter 81 Examining the Impact of US IT Stock Market on Other IT Stock MarketsChapter 81 Examining the Impact of US IT Stock Market on Other IT Stock Markets By By Zhuo Qiao, University of Macau, China Zhuo Qiao, University of Macau, China Venus Khim-Sen Liew, Universiti Malaysia Sabah, MalaysiaVenus Khim-Sen Liew, Universiti Malaysia Sabah, Malaysia Wing-Keung Wong, Hong Kong Baptist University, Hong KongWing-Keung Wong, Hong Kong Baptist University, Hong Kong Chapter 82Chapter 82 Application of Alternative ODE in Finance and EconomicsApplication of Alternative ODE in Finance and Economics ResearchResearch

ByByCheng Few Lee, Rutgers University, USA Cheng Few Lee, Rutgers University, USA Junmin Shi, Rutgers University, USAJunmin Shi, Rutgers University, USA

Chapter 83Chapter 83 Application of Simultaneous Equation in Finance ResearchApplication of Simultaneous Equation in Finance Research

ByByCarl R. Chen, University of Dayton, USACarl R. Chen, University of Dayton, USACheng Few Lee, Rutgers University, USA Cheng Few Lee, Rutgers University, USA

Chapter 84Chapter 84 The Fuzzy Set and Data mining Applications in Accounting and FinanceThe Fuzzy Set and Data mining Applications in Accounting and FinanceByByWikil Kwak, University of Nebraska at Omaha, USAWikil Kwak, University of Nebraska at Omaha, USAYong Shi, University of Nebraska at Omaha, USA and Chinese Academy of Sciences, ChinaYong Shi, University of Nebraska at Omaha, USA and Chinese Academy of Sciences, ChinaCheng-few Lee, Rutgers University, USACheng-few Lee, Rutgers University, USA


Chapter 85Chapter 85 Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns (Reprint)and High Frequency Index Returns (Reprint)By By Bevan J. Blair, Ingenious Asset Management, UKBevan J. Blair, Ingenious Asset Management, UKSer-Huang Poon, University of Manchester, UK Ser-Huang Poon, University of Manchester, UK Stephen J. Taylor, Lancaster University, UKStephen J. Taylor, Lancaster University, UK

Chapter 86 Detecting Structural Instability in Financial Time SeriesChapter 86 Detecting Structural Instability in Financial Time SeriesByByDerann Hsu, University of Wisconsin – MilwaukeeDerann Hsu, University of Wisconsin – Milwaukee

Chapter 87Chapter 87 The Instrument Variable Approach to Correct For Endogeneity in Finance The Instrument Variable Approach to Correct For Endogeneity in FinanceBy By

Chia-Jane Wang, Manhattan College, USAChia-Jane Wang, Manhattan College, USA

Chapter 88Chapter 88 Bayesian Inference of Financial Models Using MCMC AlgorithmsBayesian Inference of Financial Models Using MCMC AlgorithmsByByXianghua Liu, Rutgers University, USAXianghua Liu, Rutgers University, USALiuling Li, Rutgers University, USALiuling Li, Rutgers University, USAHiroki Tsurumi, Rutgers University, USAHiroki Tsurumi, Rutgers University, USA

Chapter 89Chapter 89 On Capital Structure and Entry DeterrenceOn Capital Structure and Entry DeterrenceByBy

Fathali Firoozi, University of Texas at San Antonio, USAFathali Firoozi, University of Texas at San Antonio, USADonald Lien, University of Texas at San Antonio, USADonald Lien, University of Texas at San Antonio, USA


Chapter 90Chapter 90 VAR Models: Estimation, Inferences and ApplicationsVAR Models: Estimation, Inferences and ApplicationsByByYangru Wu, Rutgers University, USAYangru Wu, Rutgers University, USAXing Zhou, Rutgers University, USAXing Zhou, Rutgers University, USA

Chapter 91Chapter 91 Signalling Models and Product Market Games in Finance: Do We Know What We Know?Signalling Models and Product Market Games in Finance: Do We Know What We Know?ByByKose John, New York University, USAKose John, New York University, USAAnant Sunderam, Tuck School, USAAnant Sunderam, Tuck School, USA

Chapter 92Chapter 92 Estimation of Short- and Long-Term VaR for Long-Memory Stochastic Volatility ModelsEstimation of Short- and Long-Term VaR for Long-Memory Stochastic Volatility ModelsByByHwai-Chung Ho, Academia Sinica and National Taiwan University, TaiwanHwai-Chung Ho, Academia Sinica and National Taiwan University, TaiwanFang-I Liu, National Taiwan University, TaiwanFang-I Liu, National Taiwan University, Taiwan

Chapter 93Chapter 93 Time Series Modeling and Forecasting of the Volatilities of Asset ReturnsTime Series Modeling and Forecasting of the Volatilities of Asset ReturnsByByTze Leung Lai, Stanford University, USATze Leung Lai, Stanford University, USAHaipeng Xing, SUNY at Stony Brook, USAHaipeng Xing, SUNY at Stony Brook, USA

Chapter 94Chapter 94 Listing effects and the private company discount in bank acquisitionsListing effects and the private company discount in bank acquisitionsByBy

Atul Gupta, Bentley University, USAAtul Gupta, Bentley University, USALalatendu Misra, University of Texas at San Antonio, USALalatendu Misra, University of Texas at San Antonio, USA


Chapter 95Chapter 95 An ODE Approach for the Expected Discounted Penalty at Ruin in a jump-diffusion model An ODE Approach for the Expected Discounted Penalty at Ruin in a jump-diffusion model (Reprint)(Reprint)

ByByCheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USAYu-Ting Chen, National Chao Tung University, TaiwanYu-Ting Chen, National Chao Tung University, TaiwanYuan-Chung Sheu, National Chao Tung University, TaiwanYuan-Chung Sheu, National Chao Tung University, Taiwan

Chapter 96Chapter 96 Alternative Models for Estimating the Cost of Equity Capital for Property/Casualty Insurers Alternative Models for Estimating the Cost of Equity Capital for Property/Casualty Insurers (Reprint)(Reprint)ByByAlice Lee, San Francisco State University, USAAlice Lee, San Francisco State University, USA J. D. Cumming, Temple University, USAJ. D. Cumming, Temple University, USA

Chapter 97Chapter 97 Implementing a Multi-Factor Term Structure ModelImplementing a Multi-Factor Term Structure ModelByByRen-Raw Chen, Fordham University, USARen-Raw Chen, Fordham University, USALouis O. Scott, Morgan Stanley, USALouis O. Scott, Morgan Stanley, USA

Chapter 98Chapter 98 Taking Positive Interest Rates Seriously (Reprint)Taking Positive Interest Rates Seriously (Reprint)ByByEnlin Pan, Independent consultant, Chicago, USAEnlin Pan, Independent consultant, Chicago, USALiuren Wu, Baruch College, USALiuren Wu, Baruch College, USA

Chapter 99Chapter 99 Positive Interest Rates and Yields: Additional Serious Considerations (Reprint) Positive Interest Rates and Yields: Additional Serious Considerations (Reprint) ByByJonathan E. Ingersoll, Jr., Yale School of Management, USAJonathan E. Ingersoll, Jr., Yale School of Management, USA


Chapter 100 Functional Forms for Performance Evaluation: Evidence from Closed-end Country Funds (RepriChapter 100 Functional Forms for Performance Evaluation: Evidence from Closed-end Country Funds (Reprint)nt)ByByCheng-Few Lee, Rutgers University, USACheng-Few Lee, Rutgers University, USADilip K. Patro, Office of the Comptroller of the Currency, USADilip K. Patro, Office of the Comptroller of the Currency, USABo Liu, Citigroup Global Market Inc., USABo Liu, Citigroup Global Market Inc., USA

Chapter 101 A semimartingale BSDE related to the minimal entropy martingale measure (Reprint)Chapter 101 A semimartingale BSDE related to the minimal entropy martingale measure (Reprint)ByByMichael Mania, A. Razmadze Mathematical Institute, GeorgiaMichael Mania, A. Razmadze Mathematical Institute, GeorgiaMarina Santacroce, Politecnico di Torino, ItalyMarina Santacroce, Politecnico di Torino, ItalyRevaz Tevzadze, Institute of Cybernetics, GeorgiaRevaz Tevzadze, Institute of Cybernetics, Georgia

Chapter 102 The density process of the minimal entropy martingale measure in a stochastic volatility model Chapter 102 The density process of the minimal entropy martingale measure in a stochastic volatility model with jumps (Reprint)with jumps (Reprint)ByBy

Fred Espen Benth, University of Oslo and Agder University College, NorwayFred Espen Benth, University of Oslo and Agder University College, NorwayThilo Meyer-Brandis, University of Oslo, NorwayThilo Meyer-Brandis, University of Oslo, Norway

Chapter 103 Arbitrage Detection from Stock Data: An Empirical StudyChapter 103 Arbitrage Detection from Stock Data: An Empirical StudyByBy

Cheng-Der Fuh, National Central University and Academia Sinica, TaiwanCheng-Der Fuh, National Central University and Academia Sinica, TaiwanSzu-Yu Pai, National Taiwan University, TaiwanSzu-Yu Pai, National Taiwan University, Taiwan


Chapter 104 Detecting Corporate Failure Chapter 104 Detecting Corporate Failure

ByBy

Yanzhi Wang, Yuan Ze University, Taiwan Yanzhi Wang, Yuan Ze University, Taiwan

Lin Lin, National Chi-Nan University, TaiwanLin Lin, National Chi-Nan University, Taiwan

Jenifer Piesse, University of London, UKJenifer Piesse, University of London, UK

Hsien-chang Kuo, National Chi-Nan University, TaiwanHsien-chang Kuo, National Chi-Nan University, Taiwan

Chapter 105 Genetic Programming for Option PricingChapter 105 Genetic Programming for Option Pricing

ByBy

N. K. Chidambaran, Fordham University, USAN. K. Chidambaran, Fordham University, USA

Chapter 106 A Constant Elasticity of Variance (CEV) Family of Stock Price Distributions in Option PriChapter 106 A Constant Elasticity of Variance (CEV) Family of Stock Price Distributions in Option Pricing: Review and Integration (Reprint)cing: Review and Integration (Reprint)

ByBy

Ren-Raw Chen, Rutgers University, USARen-Raw Chen, Rutgers University, USA

Cheng Few Lee, Rutgers University, USACheng Few Lee, Rutgers University, USA

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