1Introduction to Financial Engineering and Data Analysis
1174.6 Summary
21.1 Overview of Financial Engineering
1184.7 Exercise
3Origins and Evolution
119Financial Risk Management
4Core Concepts
1205.1 Value at Risk (VaR) and Expected Shortfall
5Techniques and Tools
121Introduction to VaR and ES:
6Applications
122VaR Calculation Methods:
7Challenges and Future Directions
123Expected Shortfall Calculation:
81.2 Role of Statistics and Data Analysis
124Significance in Financial Risk Management:
91.3 Data Sources and Data Collection Methods
125Limitations and Criticisms:
10Data Sources in Financial Engineering:: Data Collection Methods in Financial Engineering:
1265.2 Portfolio Risk Measurement and Optimization
111.4 Descriptive Statistics in Financial Engineering
127Importance of Portfolio Risk Measurement and Optimization
12Key Principles of Descriptive Statistics
128Portfolio Risk Measurement
13Key Measures in Descriptive Statistics
129Diversification
14Practical Applications of Descriptive Statistics in Financial Engineering: Challenges and Limitations
130Asset Allocation Strategies
151.5 Summary
131Portfolio Optimization Techniques
161.6 Exercise
132Challenges and Limitations
17Probability Theory for Financial Applications
1335.3 Credit Risk Modelling and Default Probabilities
182.1 Probability Distributions in Finance
1345.4 Stress Testing and Scenario Analysis
19Importance of Probability Distributions in Finance
135Objectives of Stress Testing and Scenario Analysis:
20Types of Probability Distributions in Finance
136Importance of Stress Testing and Scenario Analysis:
21Applications of Probability Distributions in Finance: Implications for Decision-Making
137Types of Stress Testing and Scenario Analysis:
222.2 Random Variables and Expectation
138Methodologies and Best Practices:
23Foundations of Random Variables:
139Challenges and Limitations:
24Probability Distributions in Financial Engineering:
1405.5 Market Risk, Credit Risk, and Liquidity Risk Integration
25Expectation in Financial Engineering:
1415.6 Summary
26Properties of Expectation:
1425.7 Exercise
27Applications of Random Variables and Expectation in Financial Engineering:
143Financial Derivatives Pricing and Hedging
282.3 Joint Distributions and Copulas
1446.1 Option Pricing Models
292.3.1 Copula Functions and Dependence Structures
145The Black-Scholes Model
302.3.2 Copula-Based Risk Measures
146Assumptions and Limitations of the Black-Scholes Model
312.3.3 Copula Estimation Techniques
147Extensions and Variations of the Black-Scholes Model
322.4 Conditional Probability and Bayes’ Theorem
148Numerical Methods for Option Pricing
332.5 Monte Carlo Simulation in Finance
149Applications of Option Pricing Models
34Principles of Monte Carlo Simulation:
1506.2 Greeks and Sensitivity Analysis
35Applications of Monte Carlo Simulation in Finance:
1516.3 Volatility Surface Modelling: Understanding Volatility:
36Implementation of Monte Carlo Simulation in Finance:: Limitations of Monte Carlo Simulation:
1526.4 Exotic Options and Structured Products
372.6 Summary
153Exotic Options:: Structured Products:
382.7 Exercise
1546.5 Risk-neutral Valuation and Hedging Strategies
39Statistical Inference in Finance
155Risk-Neutral Valuation
40Significance of Statistical Inference in Finance:
156Binomial Model for Option Pricing
41Key Concepts in Statistical Inference:
157Black-Scholes Model
42Methods of Statistical Inference:
158Hedging Strategies
43Applications of Statistical Inference in Finance:: Challenges and Limitations:
159Practical Considerations
443.1 Estimation Theory
1606.6 Summary
453.2 Hypothesis Testing in Financial Decision Making
1616.7 Exercise
46Theoretical Foundations of Hypothesis Testing in Finance:
162Machine Learning Techniques for Finance
47Practical Applications of Hypothesis Testing in Finance:
163Types of Machine Learning Techniques in Finance:
48Challenges in Hypothesis Testing in Finance:: Recent Developments in Hypothesis Testing in Finance:
164Applications of Machine Learning in Finance:
493.3 Parametric and Nonparametric Tests
165Challenges and Considerations:: Future Directions:
50Parametric Tests:
1667.1 Supervised Learning
51Common Parametric Tests in Finance:
167Applications of Supervised Learning in Finance
52Assumptions of Parametric Tests:
168Regression Techniques in Finance
53Nonparametric Tests:
169Polynomial Regression
54Common Nonparametric Tests in Finance:
170Support Vector Regression (SVR)
55Advantages and Disadvantages of Nonparametric Tests:
171Classification Techniques in Finance
56When to Use Parametric vs. Nonparametric Tests in Finance:
172Decision Trees
573.4 Bootstrap Methods in Finance
173Random Forests
583.4.1 Resampling Techniques
1747.2 Unsupervised Learning
59Types of Resampling Techniques: Applications of Resampling Techniques in Finance
175Unsupervised Learning
603.4.2 Bootstrapped Confidence Intervals and Hypothesis Testing: Conclusion:
176Clustering
613.4.3 Bootstrap Aggregating (Bagging)
177Dimensionality Reduction
623.5 Bayesian Inference and Markov Chain Monte Carlo Methods
178Machine Learning Techniques for Finance
63Bayesian Inference:
179Portfolio Management
64Bayes’ Theorem:
180Risk Management
65Markov Chain Monte Carlo (MCMC) Methods:
181Algorithmic Trading
66Basics of MCMC:
182Fraud Detection
67Applications of Bayesian Inference and MCMC in Finance:
1837.3 Ensemble Methods
68Challenges and Considerations:
1847.4 Deep Learning Models in Finance
693.6 Summary
185Introduction to Deep Learning in Finance:
703.7 Exercise
186Applications of Deep Learning Models in Finance:: Example Applications of Deep Learning Models in Finance:
71Time Series Analysis and Forecasting
1877.5 Reinforcement Learning for Algorithmic Trading
72Significance of Time Series Analysis in Financial Engineering:
188Understanding Reinforcement Learning
73Methodologies in Time Series Analysis:
189Components of Reinforcement Learning
74Applications of :
190Exploration vs. Exploitation
75Challenges and Considerations:: Advancements and Future Directions:
191Reinforcement Learning Algorithms
764.1 Stationarity and Autocorrelation
192Application in Algorithmic Trading
77Stationarity in Time Series Analysis
1937.6 Summary
78Types of Stationarity
1947.7 Exercise
79Autocorrelation in Time Series Analysis: Time Series Analysis and Forecasting in Financial Engineering
195High-Frequency Data Analysis
804.2 ARIMA Models and Seasonal Decomposition
1968.1 Tick Data Processing and Cleaning
81Understanding Time Series Data
197Significance of Tick Data Processing and Cleaning:
82ARIMA Models
198Algorithms for Tick Data Processing and Cleaning:: Real-World Implementation:
83Seasonal Decomposition
1998.2 Market Microstructure Models
84Integration in Financial Engineering
200Market Microstructure: An Overview
85Challenges and Considerations
201Importance of Market Microstructure in Finance
864.3 ARCH and GARCH Models for Volatility Forecasting
202Market Microstructure Models
87Understanding Volatility
203Example: The Kyle Model
88Autoregressive Conditional Heteroskedasticity (ARCH) Model
204Application of Market Microstructure Models
89Model Specification
205Algorithmic Approaches in Market Microstructure Modeling
90Estimation and Inference
206Algorithm: Market Impact Models
91Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model
207Challenges and Future Directions
92Model Specification
2088.4 Order Book Dynamics and Market Impact
93Estimation and Inference
209Importance of Order Book Dynamics and Market Impact
94Applications in Financial Engineering
210Applications of Order Book Dynamics and Market Impact Analysis
95Challenges and Extensions
211Algorithms and Methodologies: Case Study: Order Book Dynamics and Market Impact in Bitcoin Trading
964.4 Multivariate Time Series Analysis
2128.5 Limit Order Books and Order Flow Analysis
974.5 Forecast Evaluation and Model Selection Criteria
213Limit Order Books (LOBs)
984.5.1 Forecast Accuracy Measures
214Applications
99Importance of Forecast Accuracy Measures
215Order Flow Analysis
100Common Forecast Accuracy Measures
216Components
101Model Selection Criteria in Financial Engineering
217Algorithms
102Application of Forecast Accuracy Measures in Model Selection
218Applications
103Challenges and Limitations
2198.6 Summary
1044.5.2 Information Criteria
2208.7 Exercise
105Understanding Information Criteria
221Case Studies and Applications in Financial Engineering
106Types of Information Criteria
2229.1 Risk Management Case Studies
107Relevance in Financial Engineering
2239.2 Algorithmic Trading Case Studies
108Application in Forecast Evaluation
2249.3 Portfolio Management Case Studies
109Application in Model Selection Criteria
2259.4 Financial Innovation and Product Development
110Practical Considerations and Limitations
2269.5 Emerging Trends and Future Directions
1114.5.3 Out-of-Sample vs. In-Sample Forecast Evaluation
2279.6 Summary
112In-Sample Forecast Evaluation
2289.7 Exercise
113Out-of-Sample Forecast Evaluation
229References
114Comparing In-Sample and Out-of-Sample Evaluation
230Glossary
115Importance in Financial Engineering
231Index
116Challenges and Considerations
