Unit 1208: Computational Finance

1. Introduction to Computational Finance

• Definition and scope of computational finance
• Role of computer algorithms and mathematical models in finance
• Applications: financial data analysis, investment decision-making, risk management
• Overview of financial markets and instruments

2. Time Value of Money and Discounted Cash Flows

• Concept of time value of money (TVM)
• Present value (PV) and future value (FV) calculations
• Discounted cash flow (DCF) analysis
• Importance of TVM and DCF in asset valuation and investment appraisal
• Examples of DCF in project and security valuation

3. Portfolio Optimization Techniques

• Introduction to Modern Portfolio Theory (MPT)
• Risk and return trade-offs
• Asset allocation strategies
• Efficient frontier and the Capital Market Line (CML)
• Optimization techniques: mean-variance optimization
• Diversification benefits and portfolio construction

4. Option Pricing Models

• Introduction to derivative securities and options
• Black-Scholes model
  • Assumptions
  • Formula derivation and interpretation
• Binomial option pricing model
  • Multi-period binomial trees
• Applications in option valuation and risk management
• Implementing option trading strategies

5. Risk Management in Finance

• Types of financial risks: market risk, credit risk, operational risk, liquidity risk
• Risk measurement techniques:
  • Value at Risk (VaR)
  • Conditional VaR (CVaR)
  • Stress testing and scenario analysis
• Risk management strategies:
  • Hedging
  • Diversification
  • Use of derivatives for risk mitigation
• Regulatory frameworks and risk compliance

6. Machine Learning in Finance

• Overview of machine learning concepts
• Supervised learning techniques:
  • Regression
  • Classification
• Unsupervised learning techniques:
  • Clustering
• Neural networks and deep learning basics
• Applications in financial data analysis:
  • Market trend prediction
  • Credit scoring
  • Fraud detection
  • Algorithmic trading

7. High-Frequency Trading and Algorithmic Trading

• Definition and characteristics of high-frequency trading (HFT)
• Algorithmic trading strategies and their design
• Market microstructure and order book dynamics
• Role of technology and infrastructure in HFT
• Regulatory and ethical considerations

8. Quantitative Trading Strategies

• Introduction to quantitative trading
• Statistical arbitrage:
  • Concept and implementation
  • Pairs trading
• Trend-following strategies
• Mean-reversion strategies
• Developing and backtesting quantitative models
• Risk and performance evaluation of trading algorithms