Risk Management in Finance; 1 Financial Investment, Financial Risk and Risk Management; 1.1 Financial Markets and Financial Investment; 1.2 Main Risks in Financial Markets; 1.3 Risk Countermeasures: Hedging and Diversifying; 1.4 Risk Management by Diversification; 1.5 Outline of Part I; 2 Market Risk Measures in Financial Investments; 2.1 Market Risk and Its Measurement; 2.2 Variance: Fluctuation Is Taken as Risk; 2.2.1 Definition of Variance; 2.2.2 Estimation of Variance; 2.3 Value at Risk: A Likely Loss Is Taken as Risk; 2.3.1 Definition of Value at Risk Estimation of VaR: Three Methods2.3.2.1 Variance-Covariance Method; 2.3.2.2 Historical Simulation Method; 2.3.2.3 Monte Carlo Simulation Method; 2.4 Conditional VaR: Expected Loss Behind VaR Is Taken as Risk; 2.4.1 Definition of Conditional VaR; 2.4.2 Estimation of CVaR; 2.5 Other Risk Measures: Failure Is Taken as Risk; 2.6 Summary; 3 Market Risk Control in Investment Decisions; 3.1 Portfolio Selection and Its Models; 3.2 MV Model and Its Variations; 3.2.1 The Base MV Model and Its Two Variations; 3.2.2 Solving Methods for MV Based Models Two MV Based Models with Computational Advantages3.3 M-VaR Model and Its Solving Method; 3.3.1 Methods for Solving M-VaR Models; 3.3.1.1 Minimize VaR Approximately; 3.3.1.2 Minimize VaR Indirectly; 3.3.1.3 Minimize VaR Using Heuristics; 3.3.2 Solving M-VaR Model Using the Soft Optimization Approach; 3.3.2.1 The Ideas of the Soft Optimization Approach; 3.3.2.2 The Algorithm for Solving Model (3.20); 3.4 M-CVaR Model and Its Solving Method; 3.5 Other M-Risk Models and Solving Methods; 3.6 Summary; 4 Market Risk Measures for Flexible Investments; 4.1 Flexible Investments Risk Measures for Investments with Uncertain Exit Time4.2.1 Period Value at Risk; 4.2.2 Risk Measures Based on Average Loss in Time Axis; 4.3 Estimation of PVaR with Scenario Simulation; 4.3.1 Monte Carlo Simulation Method for Estimating PVaR; 4.3.2 Historical Simulation Method for Estimating PVaR; 4.4 Estimation of Risk Measures Based on Average Loss; 4.4.1 Estimation of Risk Measures Under Complete Information About the Probabilities of Exit Time; 4.4.2 Estimation of Risk Measures Under Partial Information About the Probabilities of Exit Time; 4.5 Summary Market Risk Control in Flexible Investment Decisions5.1 Evaluation of Investments with Flexible Investment Term; 5.2 Two Kinds of Model for Flexible Investment Decisions; 5.3 M-PVaR Model and Solving Methods; 5.3.1 Solving PVaR Minimization Model by Solving a Mixed Integer Linear Programming; 5.3.2 Solving PVaR Minimization Model Using the Soft Optimization Approach; 5.4 M-Risk Models and Solving Methods; 5.4.1 M-Risk Models with Complete Probability Information Regarding Exit Time; 5.4.2 M-Risk Models with Partial Probability Information Regarding Exit Time M-risk(t) models and their solving methods Basic results on stochastic programming Stochastic programming with recourse Multi stage stochastic programming model Stochastic Integer Programming Chance-Constrained Programming Two-Stage Model Tbking Variance into Account Inventory Distribution Problem Lateral Transshipments Numerical Experiments Reorganization of Logistics Network Background Formulation Sets Parameters Variables. Solution Algorithm CVaR Optimization Model Numerical Experiments Node Costs Customer Demand