Mathematics and Statistics for Financial Risk Management 1st Edition by Michael Miller ISBN 1118170628 9781118170625

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Product details:
ISBN 10: 1118170628
ISBN 13: 9781118170625
Author: Michael Miller
The recent financial crisis and its impact on the broader economy underscore the importance of financial risk management in today’s world. At the same time, financial products and investment strategies are becoming increasingly complex. Today, it is more important than ever that risk managers possess a sound understanding of mathematics and statistics.

In a concise and easy-to-read style, each chapter of this book introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problems and application sections demonstrate how these techniques can be applied to actual risk management problems. Exercises at the end of each chapter and the accompanying solutions at the end of the book allow readers to practice the techniques they are learning and monitor their progress. A companion website includes interactive Excel spreadsheet examples and templates.

• Covers basic statistical concepts from volatility and Bayes’ Law to regression analysis and hypothesis testing
• Introduces risk models, including Value-at-Risk, factor analysis, Monte Carlo simulations, and stress testing
• Explains time series analysis, including interest rate, GARCH, and jump-diffusion models
• Explores bond pricing, portfolio credit risk, optimal hedging, and many other financial risk topics

If you’re looking for a book that will help you understand the mathematics and statistics of financial risk management, look no further.

Mathematics and Statistics for Financial Risk Management 1st Edition Table of contents:

Chapter 1: Introduction to Financial Risk Management

• The Role of Risk Management in Finance
• Types of Financial Risks: Market, Credit, Operational, and Liquidity Risk
• Overview of Risk Management Tools and Techniques
• The Regulatory Environment in Financial Risk Management

Chapter 2: Probability Theory and Financial Risk

• Basic Concepts of Probability
• Conditional Probability and Bayes’ Theorem
• Random Variables and Probability Distributions
• Common Probability Distributions in Finance: Normal, Lognormal, Exponential
• Applications of Probability in Financial Risk

Chapter 3: Statistics for Financial Risk

• Descriptive Statistics: Mean, Median, Mode, Variance, and Standard Deviation
• Sampling and Estimation Techniques
• Confidence Intervals and Hypothesis Testing
• Regression Analysis and its Financial Applications
• Time Series Analysis in Finance

Chapter 4: Financial Instruments and their Risk

• Overview of Financial Instruments: Stocks, Bonds, Derivatives
• Pricing Financial Instruments
• Measuring and Managing the Risk of Financial Instruments
• Sensitivity Analysis and Greeks (Delta, Gamma, Vega, Theta)

Chapter 5: Portfolio Theory and Risk Management

• Modern Portfolio Theory: Diversification and Efficient Frontier
• Capital Asset Pricing Model (CAPM)
• The Risk-Return Trade-Off in Portfolio Construction
• The Role of Correlation in Portfolio Management
• Risk Measures: Value at Risk (VaR) and Conditional VaR

Chapter 6: Quantitative Methods for Risk Management

• Monte Carlo Simulation in Risk Management
• Stress Testing and Scenario Analysis
• Copulas and Multivariate Distributions
• Risk Aggregation and Diversification
• The Role of Quantitative Models in Risk Assessment

Chapter 7: Value at Risk (VaR) and its Limitations

• Definition and Calculation of VaR
• Historical Simulation, Parametric VaR, and Monte Carlo VaR
• The Pros and Cons of VaR as a Risk Measure
• Backtesting VaR Models
• Regulatory Requirements and the Use of VaR

Chapter 8: Credit Risk and Credit Derivatives

• Introduction to Credit Risk
• Credit Rating and Credit Scoring Models
• Credit Derivatives: Credit Default Swaps (CDS) and Collateralized Debt Obligations (CDOs)
• Measuring Credit Exposure and Credit VaR
• Credit Risk Models: Merton Model and CreditMetrics

Chapter 9: Operational Risk and Liquidity Risk

• Defining Operational Risk and its Sources
• Models for Operational Risk: Loss Distribution Approach
• Measuring and Managing Liquidity Risk
• The Role of Liquidity in Financial Crises
• Regulatory Approaches to Operational and Liquidity Risk

Chapter 10: Time Series and Forecasting in Financial Risk Management

• Time Series Models: ARIMA, GARCH, and Exponential Smoothing
• Forecasting Volatility and Market Movements
• Risk and Return Forecasting
• The Use of Time Series in Financial Planning and Risk Assessment
• Applications of Forecasting in Stress Testing

Chapter 11: Statistical Methods in Financial Risk Management

• Hypothesis Testing in Risk Management
• Bootstrapping and Resampling Methods
• Bayesian Methods in Financial Risk
• Non-parametric Methods and Their Application
• The Role of Statistical Models in Regulatory Compliance

Chapter 12: Risk Management in Practice

• Case Studies in Financial Risk Management
• The Role of Risk Managers and Financial Analysts
• Implementing Risk Management Strategies in Financial Institutions
• The Impact of Technology and Algorithms on Risk Management
• Ethical Issues and Challenges in Financial Risk Management

Chapter 13: Advanced Topics in Financial Risk Management

• Advanced Monte Carlo Methods
• Stochastic Processes and Brownian Motion in Financial Models
• Advanced Credit Risk Modelling: Structural and Reduced-Form Models
• Risk Management in Derivatives Markets
• Emerging Topics: Fintech, AI, and Machine Learning in Risk Management

Chapter 14: Regulatory Framework and Financial Risk

• The Role of Financial Regulators
• Key Regulations: Basel III, Dodd-Frank, and Solvency II
• Stress Testing and the Role of Regulators
• Compliance and Risk Management Standards
• International Perspectives on Risk Regulation

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