Interest Rate Models Asset Allocation and Quantitative Techniques for Central Banks and Sovereign Wealth Funds 1st Edition by Arjan B Berkelaar, Joachim Coche, Ken Nyholm ISBN 023027353X 9780230273535 by Arjan Bastiaan Berkelaar, Joachim Coche, Ken Nyholm 0230240127 instant download after payment.

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ISBN 10: 023027353X 
ISBN 13: 9780230273535
Author:  Arjan B Berkelaar, Joachim Coche, Ken Nyholm

Interest Rate Models Asset Allocation and Quantitative Techniques for Central Banks and Sovereign Wealth Funds 1st Table of contents:

Part I: Foundations of Interest Rate Modeling

Chapter 1: Introduction to Interest Rate Markets and Yield Curves

• Overview of fixed income instruments (bonds, swaps, futures).
• Understanding the yield curve: types, drivers, and interpretation.
• Market conventions and pricing basics.

Chapter 2: No-Arbitrage Pricing Theory and Stochastic Processes

• Fundamental theorem of asset pricing.
• Stochastic calculus basics (Brownian motion, Itô's Lemma).
• Risk-neutral measure and equivalent martingale measure.

Chapter 3: Short Rate Models

• Vasicek model: theory, calibration, and limitations.
• Cox-Ingersoll-Ross (CIR) model: theory, calibration, and limitations.
• Extensions and variants of short rate models.

Chapter 4: HJM and BGM (Market Models)

• Heath-Jarrow-Morton (HJM) framework: principles, drift condition, implementation.
• Brace-Gatarek-Musiela (BGM) or LIBOR Market Model:
  • Log-normal forward LIBOR rates.
  • Calibration to cap and swaption volatilities.
  • Advantages and disadvantages compared to short rate models.

Chapter 5: Advanced Interest Rate Models

• Stochastic volatility models for interest rates.
• Jump diffusion models.
• Multi-factor models.
• Models for negative interest rates.

Chapter 6: Calibration Techniques for Interest Rate Models

• Calibration to market data (bonds, swaps, caps, swaptions).
• Least squares, maximum likelihood, and numerical optimization techniques.
• Practical challenges and robust calibration.

Part II: Asset Allocation for Central Banks and Sovereign Wealth Funds

Chapter 7: Unique Objectives and Constraints of Central Banks and SWFs

• Long-term investment horizons.
• Liquidity requirements, safety, and capital preservation.
• Intergenerational equity and sustainability.
• Political and social mandates.
• Liability-driven investment (LDI) for pension liabilities (where applicable).

Chapter 8: Strategic Asset Allocation (SAA) Frameworks

• Mean-variance optimization (MVO) and its limitations for long-term investors.
• Risk budgeting approaches.
• Factor-based allocation.
• Robust optimization and uncertainty in parameters.

Chapter 9: Dynamic Asset Allocation (DAA) and Tactical Asset Allocation (TAA)

• Discretionary vs. systematic DAA.
• Momentum, value, and carry strategies.
• Risk parity and volatility targeting.

Chapter 10: Managing Fixed Income Portfolios for Large Institutions

• Benchmarking and passive management.
• Active fixed income strategies (duration management, yield curve positioning, credit analysis).
• Inflation-linked bonds and hedging strategies.

Chapter 11: Alternative Investments for SWFs

• Private equity, real estate, infrastructure.
• Hedge funds and diversifying strategies.
• Illiquidity premium and due diligence.

Chapter 12: ESG Integration and Sustainable Investing

• Environmental, Social, and Governance factors.
• Impact investing and responsible investment principles.
• Measuring and reporting ESG performance.

Part III: Quantitative Techniques and Risk Management

Chapter 13: Portfolio Construction and Optimization beyond MVO

• Black-Litterman model.
• Resampling and robust portfolio optimization.
• Hierarchical risk parity.
• Machine learning in portfolio construction.

Chapter 14: Risk Measurement and Management for Large Portfolios

• Value-at-Risk (VaR) and Expected Shortfall (ES).
• Stress testing and scenario analysis.
• Counterparty credit risk.
• Liquidity risk management.

Chapter 15: Performance Measurement and Attribution

• Risk-adjusted performance metrics (Sharpe ratio, Sortino ratio).
• Performance attribution for multi-asset portfolios.
• Benchmarking selection and custom benchmarks.

Chapter 16: Operational Risk and Governance

• Internal controls and oversight.
• Cybersecurity and data integrity.
• Governance structures for central banks and SWFs.

Chapter 17: Machine Learning and Big Data in Quantitative Finance

• Applications in forecasting, alpha generation, and risk modeling.
• Natural Language Processing (NLP) for market sentiment.
• Challenges of data quality and interpretability.

Conclusion

• Summary of key insights.
• Future trends in interest rate modeling, asset allocation, and quantitative finance for central banks and SWFs.
• Ongoing challenges and opportunities.

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Tags: Arjan B Berkelaar, Joachim Coche, Ken Nyholm, Interest, Rate