ORPP logo
Image from Google Jackets

Handbook of Recent Advances in Commodity and Financial Modeling : Quantitative Methods in Banking, Finance, Insurance, Energy and Commodity Markets.

By: Contributor(s): Material type: TextTextSeries: International Series in Operations Research and Management Science SeriesPublisher: Cham : Springer International Publishing AG, 2017Copyright date: ©2018Edition: 1st edDescription: 1 online resource (323 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319613208
Subject(s): Genre/Form: Additional physical formats: Print version:: Handbook of Recent Advances in Commodity and Financial ModelingLOC classification:
  • T57.6-.97
Online resources:
Contents:
Intro -- Preface -- Contents -- Part I Risk Modeling -- 1 Directional Returns for Gold and Silver: A Cluster Analysis Approach -- 1.1 Introduction and Literature Review -- 1.2 Data Collection and Preparation -- 1.3 Methodology: Two-Step Cluster Analysis -- 1.4 Gold with Clusters -- 1.4.1 Training Set Variable Importance -- 1.4.2 Validation Set Results for the Gold Models -- 1.5 Silver with Clusters -- 1.5.1 Training Set Variable Importance -- 1.5.2 Validation Set Results for the Silver Models -- 1.6 Summary and Conclusions -- References -- 2 Impact of Credit Risk and Business Cycles on Momentum Returns -- 2.1 Introduction -- 2.2 Literature Review -- 2.2.1 The Persistence of Momentum Returns in Different Dimensions -- 2.2.2 Momentum Returns and Credit Ratings -- 2.2.3 Momentum Returns and Risk Factors -- 2.3 Data -- 2.3.1 Methods -- 2.4 Empirical Findings -- 2.4.1 Can the Fama-French Three Factors Explain Momentum Returns in Credit-Rated Stocks? -- 2.4.2 Can Market States Explain the Momentum Returns in Credit-Rated Stocks? -- 2.4.3 Can Macroeconomic Factors Explain the Momentum Returns in Credit-Rated Stocks? -- 2.5 Conclusions -- Appendix: S&amp -- P Credit Rating -- References -- 3 Drivers of LBO Operating Performance: An Empirical Investigation in Asia -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Tax Benefit -- 3.2.2 Free Cash-Flow -- 3.2.3 Ownership Structure -- 3.2.4 Macroeconomic Factors -- 3.3 Institutional Background of Emerging Economies: The Case of Asia -- 3.3.1 Academic Background -- 3.3.2 Institutional Background -- 3.4 Data Sources and Descriptive Statistics -- 3.4.1 Sample Description -- 3.4.2 Benchmark Comparison -- 3.4.3 Descriptive Statistics -- 3.4.4 Analysis and Discussion -- 3.5 Results -- 3.5.1 OLS Model -- 3.5.2 Introduction of LBO Dummy Variable -- 3.5.3 Introduction of Geographical Area Dummy Variables.
3.5.4 Geographical Areas and Governance (Table 3.8) -- 3.5.5 Efficiency and Profitability Impacts (Table 3.9) -- 3.6 Conclusion -- References -- 4 Time Varying Correlation: A Key Indicator in Finance -- 4.1 Introduction -- 4.2 Recent Literature -- 4.3 The Correlation Measure -- 4.3.1 Data Simulation -- 4.3.2 Comparing the Correlation Estimators -- 4.4 Measuring Correlation -- 4.4.1 Stationarity of the Series -- 4.4.2 Structural Breaks -- 4.4.3 Correlations Between Commodities and Financial Markets -- 4.4.4 Correlations Between Energy Commodities -- 4.5 Concluding Remarks -- References -- 5 Measuring Model Risk in the European Energy Exchange -- 5.1 Introduction and Background -- 5.2 The Relative Measure of Model Risk -- 5.3 Data and Preliminary Analysis -- 5.4 Model Setting and Estimation -- 5.4.1 The GARCH Methodology -- 5.4.2 Dynamic Model Risk Quantification -- 5.5 Empirical Results -- 5.6 Conclusions and Future Research -- References -- Part II Pricing and Valuation -- 6 Wine Futures: Pricing and Allocation as Levers Against Quality Uncertainty -- 6.1 Introduction -- 6.1.1 Winemaking Process and the Tasting Reviews -- 6.2 Literature Review -- 6.2.1 Pricing and Quantity Decisions Under Uncertainty -- 6.2.2 Advance Selling -- 6.2.3 Wine Tasting -- 6.2.4 Contribution over Noparumpa et al. (2015a) -- 6.3 The Model -- 6.3.1 The Model -- 6.3.2 Demand for Wine Futures -- 6.4 Analysis -- 6.5 Empirical Analysis with Bordeaux Winery Data -- 6.5.1 Wine Futures as a Quantity Lever -- 6.5.2 Wine Futures as a Price Lever -- 6.5.3 Financial Benefit from the Proposed Stochastic Optimization Model -- 6.5.4 Financial Impact from a Wine Futures Market -- 6.6 Conclusions -- Appendix -- References -- 7 VIX Computation Based on Affine Stochastic Volatility Modelsin Discrete Time -- 7.1 Introduction -- 7.2 General Setup -- 7.3 VIX Index -- 7.4 Special Cases.
7.4.1 Dynamic Variance Gamma -- 7.4.2 Dynamic Normal Inverse Gaussian -- 7.4.3 Dynamic Normal Tempered Stable -- 7.5 Empirical Analysis -- 7.6 Conclusions -- A Appendix -- A.1 Conditional Moment Generating Function -- A.2 Martingale Condition -- A.3 VIX Index: Derivation Formula -- A.4 VIX Index: Autoregressive Model -- References -- 8 Optimal Adaptive Sequential Calibration of Option Models -- 8.1 Introduction -- 8.2 Methods -- 8.2.1 Review of Calibration Methods -- 8.2.2 Extended Parameter Dynamics -- 8.2.2.1 Random Walk Dynamics -- 8.2.2.2 Random Coefficient Dynamics -- 8.2.2.3 Mean Reversion Dynamics -- 8.2.3 Optimal Tuning -- 8.2.4 Model Selection -- 8.3 Simulations -- 8.4 Empirical Study -- 8.4.1 The Black-Scholes Model -- 8.4.2 The Heston Model -- 8.4.2.1 Numerical Results -- 8.5 Conclusion -- References -- 9 Accurate Pricing of Swaptions via Lower Bound -- 9.1 Introduction -- 9.2 A Lower Bound on Swaption Prices -- 9.2.1 Affine Models -- 9.3 The Geometric Average Approximate Exercise Region -- 9.4 Models and Numerical Results -- 9.4.1 Affine Gaussian Models -- 9.4.2 Multi-factor Cox-Ingersoll-Ross (CIR) Model -- 9.4.3 Gaussian Model with Double Exponential Jumps -- 9.4.4 Balduzzi, Das, Foresi and Sundaram Model -- 9.4.5 Numerical Results -- 9.4.5.1 Vasicek Model, Three-Factors Gaussian Model and Cox-Ingersoll and Ross Model -- 9.4.5.2 Two-Factor Gaussian Model with Double Exponential Jumps -- 9.4.5.3 Balduzzi, Das, Foresi and Sundaram Model -- 9.5 Conclusions -- A Appendix -- A.1 Proof Proposition 1 -- A.2 Proof of the Analytical Lower Bound for Gaussian Affine Models -- References -- Part III Optimization Techniques -- 10 Portfolio Optimization Using Modified Herfindahl Constraint -- 10.1 Introduction -- 10.2 Review of the Constraints About Diversification -- 10.2.1 Upper-Bound Constraint -- 10.2.2 Lower-Bound Constraint.
10.2.3 Lp-Norm Constraint -- 10.2.4 Entropy Constraint -- 10.3 Portfolio Allocation Models Under Consideration -- 10.3.1 Risk-Based Strategies -- 10.3.1.1 Equally Weighted Portfolio (EW) -- 10.3.1.2 The Shortsale-Constrained Global Minimum-Variance Portfolio (GMV) -- 10.3.1.3 The Shortsale-Constrained Equal Risk Contribution Portfolio (ERC) -- 10.3.1.4 The Shortsale-Constrained Maximum Diversified Portfolio (MDP) -- 10.3.2 Taylor Approximation of the Expected Utility (EU) with Constraints on Portfolio Diversification -- 10.3.2.1 Diversifying Portfolios Through Weight Constraint -- 10.4 Empirical Analysis -- 10.4.1 Description of the Data Base -- 10.4.2 Empirical Protocol -- 10.4.2.1 In-Sample -- 10.4.2.2 Out-of-Sample -- 10.4.3 Results with MHI-Constraint -- 10.5 Conclusions -- References -- 11 Dynamic Asset Allocation with Default and Systemic Risks -- 11.1 Introduction -- 11.2 No-Arbitrage Dynamics of the Risky Asset Values -- 11.3 The Optimal Investment Rule -- 11.4 Numerical Analysis -- 11.5 Conclusions -- References -- 12 Optimal Execution Strategy in Liquidity FrameworkUnder Exponential Temporary Market Impact -- 12.1 Introduction -- 12.2 The Model Framework -- 12.3 Exponential Market Impact Function -- 12.3.1 Evaluation of W-1 -- 12.4 Conclusion -- A Lambert W Function -- A.1 Taylor Series for -1e&lt -- z&lt -- 0 -- A.2 Series Expansions About the Branch Point z=-1e -- A.3 Asymptotic Series for z&lt -- 0 -- References -- 13 Optimal Multistage Defined-Benefit Pension Fund Management -- 13.1 Introduction -- 13.2 DB Pension Fund Management -- 13.3 Liabilities, Liquidity and A-L Duration Matching -- 13.4 Risk Capital and Risk-Adjusted Performance -- 13.5 Funding Conditions and ALM Optimization -- 13.6 Case Study: A 20 Year Pension Fund ALM Problem -- 13.6.1 Evolution of Funding Conditions -- 13.6.2 Worst Case Scenario Analysis -- 13.7 Conclusion.
Appendix -- References -- 14 Currency Hedging for a Multi-national Firm -- 14.1 Introduction -- 14.2 Exchange Rate Models -- 14.2.1 Review of Exchange Rate Studies -- 14.2.2 An Equilibrium Correction Model -- 14.2.3 Taylor Rule Based Models -- 14.2.4 Random Walk Model -- 14.3 A Dynamic Hedging Model -- 14.3.1 Exchange Rate Scenarios -- 14.3.2 Revenues and Expenditures -- 14.3.3 Forwards and Options -- 14.3.4 Model Formulation -- 14.4 Comparison of Hedging Strategies Over a Single Year -- 14.5 Out-of-Sample Tests -- 14.6 Conclusions -- Appendix: Hedging Results with Exchange Rate Model EqC and TrE -- References.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
No physical items for this record

Intro -- Preface -- Contents -- Part I Risk Modeling -- 1 Directional Returns for Gold and Silver: A Cluster Analysis Approach -- 1.1 Introduction and Literature Review -- 1.2 Data Collection and Preparation -- 1.3 Methodology: Two-Step Cluster Analysis -- 1.4 Gold with Clusters -- 1.4.1 Training Set Variable Importance -- 1.4.2 Validation Set Results for the Gold Models -- 1.5 Silver with Clusters -- 1.5.1 Training Set Variable Importance -- 1.5.2 Validation Set Results for the Silver Models -- 1.6 Summary and Conclusions -- References -- 2 Impact of Credit Risk and Business Cycles on Momentum Returns -- 2.1 Introduction -- 2.2 Literature Review -- 2.2.1 The Persistence of Momentum Returns in Different Dimensions -- 2.2.2 Momentum Returns and Credit Ratings -- 2.2.3 Momentum Returns and Risk Factors -- 2.3 Data -- 2.3.1 Methods -- 2.4 Empirical Findings -- 2.4.1 Can the Fama-French Three Factors Explain Momentum Returns in Credit-Rated Stocks? -- 2.4.2 Can Market States Explain the Momentum Returns in Credit-Rated Stocks? -- 2.4.3 Can Macroeconomic Factors Explain the Momentum Returns in Credit-Rated Stocks? -- 2.5 Conclusions -- Appendix: S&amp -- P Credit Rating -- References -- 3 Drivers of LBO Operating Performance: An Empirical Investigation in Asia -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Tax Benefit -- 3.2.2 Free Cash-Flow -- 3.2.3 Ownership Structure -- 3.2.4 Macroeconomic Factors -- 3.3 Institutional Background of Emerging Economies: The Case of Asia -- 3.3.1 Academic Background -- 3.3.2 Institutional Background -- 3.4 Data Sources and Descriptive Statistics -- 3.4.1 Sample Description -- 3.4.2 Benchmark Comparison -- 3.4.3 Descriptive Statistics -- 3.4.4 Analysis and Discussion -- 3.5 Results -- 3.5.1 OLS Model -- 3.5.2 Introduction of LBO Dummy Variable -- 3.5.3 Introduction of Geographical Area Dummy Variables.

3.5.4 Geographical Areas and Governance (Table 3.8) -- 3.5.5 Efficiency and Profitability Impacts (Table 3.9) -- 3.6 Conclusion -- References -- 4 Time Varying Correlation: A Key Indicator in Finance -- 4.1 Introduction -- 4.2 Recent Literature -- 4.3 The Correlation Measure -- 4.3.1 Data Simulation -- 4.3.2 Comparing the Correlation Estimators -- 4.4 Measuring Correlation -- 4.4.1 Stationarity of the Series -- 4.4.2 Structural Breaks -- 4.4.3 Correlations Between Commodities and Financial Markets -- 4.4.4 Correlations Between Energy Commodities -- 4.5 Concluding Remarks -- References -- 5 Measuring Model Risk in the European Energy Exchange -- 5.1 Introduction and Background -- 5.2 The Relative Measure of Model Risk -- 5.3 Data and Preliminary Analysis -- 5.4 Model Setting and Estimation -- 5.4.1 The GARCH Methodology -- 5.4.2 Dynamic Model Risk Quantification -- 5.5 Empirical Results -- 5.6 Conclusions and Future Research -- References -- Part II Pricing and Valuation -- 6 Wine Futures: Pricing and Allocation as Levers Against Quality Uncertainty -- 6.1 Introduction -- 6.1.1 Winemaking Process and the Tasting Reviews -- 6.2 Literature Review -- 6.2.1 Pricing and Quantity Decisions Under Uncertainty -- 6.2.2 Advance Selling -- 6.2.3 Wine Tasting -- 6.2.4 Contribution over Noparumpa et al. (2015a) -- 6.3 The Model -- 6.3.1 The Model -- 6.3.2 Demand for Wine Futures -- 6.4 Analysis -- 6.5 Empirical Analysis with Bordeaux Winery Data -- 6.5.1 Wine Futures as a Quantity Lever -- 6.5.2 Wine Futures as a Price Lever -- 6.5.3 Financial Benefit from the Proposed Stochastic Optimization Model -- 6.5.4 Financial Impact from a Wine Futures Market -- 6.6 Conclusions -- Appendix -- References -- 7 VIX Computation Based on Affine Stochastic Volatility Modelsin Discrete Time -- 7.1 Introduction -- 7.2 General Setup -- 7.3 VIX Index -- 7.4 Special Cases.

7.4.1 Dynamic Variance Gamma -- 7.4.2 Dynamic Normal Inverse Gaussian -- 7.4.3 Dynamic Normal Tempered Stable -- 7.5 Empirical Analysis -- 7.6 Conclusions -- A Appendix -- A.1 Conditional Moment Generating Function -- A.2 Martingale Condition -- A.3 VIX Index: Derivation Formula -- A.4 VIX Index: Autoregressive Model -- References -- 8 Optimal Adaptive Sequential Calibration of Option Models -- 8.1 Introduction -- 8.2 Methods -- 8.2.1 Review of Calibration Methods -- 8.2.2 Extended Parameter Dynamics -- 8.2.2.1 Random Walk Dynamics -- 8.2.2.2 Random Coefficient Dynamics -- 8.2.2.3 Mean Reversion Dynamics -- 8.2.3 Optimal Tuning -- 8.2.4 Model Selection -- 8.3 Simulations -- 8.4 Empirical Study -- 8.4.1 The Black-Scholes Model -- 8.4.2 The Heston Model -- 8.4.2.1 Numerical Results -- 8.5 Conclusion -- References -- 9 Accurate Pricing of Swaptions via Lower Bound -- 9.1 Introduction -- 9.2 A Lower Bound on Swaption Prices -- 9.2.1 Affine Models -- 9.3 The Geometric Average Approximate Exercise Region -- 9.4 Models and Numerical Results -- 9.4.1 Affine Gaussian Models -- 9.4.2 Multi-factor Cox-Ingersoll-Ross (CIR) Model -- 9.4.3 Gaussian Model with Double Exponential Jumps -- 9.4.4 Balduzzi, Das, Foresi and Sundaram Model -- 9.4.5 Numerical Results -- 9.4.5.1 Vasicek Model, Three-Factors Gaussian Model and Cox-Ingersoll and Ross Model -- 9.4.5.2 Two-Factor Gaussian Model with Double Exponential Jumps -- 9.4.5.3 Balduzzi, Das, Foresi and Sundaram Model -- 9.5 Conclusions -- A Appendix -- A.1 Proof Proposition 1 -- A.2 Proof of the Analytical Lower Bound for Gaussian Affine Models -- References -- Part III Optimization Techniques -- 10 Portfolio Optimization Using Modified Herfindahl Constraint -- 10.1 Introduction -- 10.2 Review of the Constraints About Diversification -- 10.2.1 Upper-Bound Constraint -- 10.2.2 Lower-Bound Constraint.

10.2.3 Lp-Norm Constraint -- 10.2.4 Entropy Constraint -- 10.3 Portfolio Allocation Models Under Consideration -- 10.3.1 Risk-Based Strategies -- 10.3.1.1 Equally Weighted Portfolio (EW) -- 10.3.1.2 The Shortsale-Constrained Global Minimum-Variance Portfolio (GMV) -- 10.3.1.3 The Shortsale-Constrained Equal Risk Contribution Portfolio (ERC) -- 10.3.1.4 The Shortsale-Constrained Maximum Diversified Portfolio (MDP) -- 10.3.2 Taylor Approximation of the Expected Utility (EU) with Constraints on Portfolio Diversification -- 10.3.2.1 Diversifying Portfolios Through Weight Constraint -- 10.4 Empirical Analysis -- 10.4.1 Description of the Data Base -- 10.4.2 Empirical Protocol -- 10.4.2.1 In-Sample -- 10.4.2.2 Out-of-Sample -- 10.4.3 Results with MHI-Constraint -- 10.5 Conclusions -- References -- 11 Dynamic Asset Allocation with Default and Systemic Risks -- 11.1 Introduction -- 11.2 No-Arbitrage Dynamics of the Risky Asset Values -- 11.3 The Optimal Investment Rule -- 11.4 Numerical Analysis -- 11.5 Conclusions -- References -- 12 Optimal Execution Strategy in Liquidity FrameworkUnder Exponential Temporary Market Impact -- 12.1 Introduction -- 12.2 The Model Framework -- 12.3 Exponential Market Impact Function -- 12.3.1 Evaluation of W-1 -- 12.4 Conclusion -- A Lambert W Function -- A.1 Taylor Series for -1e&lt -- z&lt -- 0 -- A.2 Series Expansions About the Branch Point z=-1e -- A.3 Asymptotic Series for z&lt -- 0 -- References -- 13 Optimal Multistage Defined-Benefit Pension Fund Management -- 13.1 Introduction -- 13.2 DB Pension Fund Management -- 13.3 Liabilities, Liquidity and A-L Duration Matching -- 13.4 Risk Capital and Risk-Adjusted Performance -- 13.5 Funding Conditions and ALM Optimization -- 13.6 Case Study: A 20 Year Pension Fund ALM Problem -- 13.6.1 Evolution of Funding Conditions -- 13.6.2 Worst Case Scenario Analysis -- 13.7 Conclusion.

Appendix -- References -- 14 Currency Hedging for a Multi-national Firm -- 14.1 Introduction -- 14.2 Exchange Rate Models -- 14.2.1 Review of Exchange Rate Studies -- 14.2.2 An Equilibrium Correction Model -- 14.2.3 Taylor Rule Based Models -- 14.2.4 Random Walk Model -- 14.3 A Dynamic Hedging Model -- 14.3.1 Exchange Rate Scenarios -- 14.3.2 Revenues and Expenditures -- 14.3.3 Forwards and Options -- 14.3.4 Model Formulation -- 14.4 Comparison of Hedging Strategies Over a Single Year -- 14.5 Out-of-Sample Tests -- 14.6 Conclusions -- Appendix: Hedging Results with Exchange Rate Model EqC and TrE -- References.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

There are no comments on this title.

to post a comment.

© 2024 Resource Centre. All rights reserved.