Fixed Income Relative Value Trading

Course Introduction

This 3-day course provides a comprehensive but concise overview over the models and tools required for successful relative value analysis in fixed income markets. Both statistical and financial models are covered and numerous hands-on exercises and case studies prepare participants to translate the theory into profitable trading strategies. The transition to new reference rates and its impact on swaps (spreads) is extensively addressed.

Familiarity with common fixed income instruments and trades is a prerequisite, while advanced mathematical knowledge is not.

Course Details
Course Outline


Day 1: Statistical Relative Value models

Introduction to Fixed Income Relative Value (RV) Analysis
• Concept of RV analysis
• Sources of RV opportunities
• The insights from RV analysis
• Applications of RV analysis: Trading, hedging, asset selection, creating alpha
• RV models: Statistical and financial models and their interaction
• Outline of the course

Principal Component Analysis (PCA): Theory
• What is PCA and how does it help us?
• PCA versus other factor models
• Mathematics of PCA
• Gaining insights into market mechanisms through interpretation of the PCA results
• Decomposing a market into directional (beta) and non-directional (alpha) factors
• Using PCA to screen the market for trading opportunities
• Using PCA for asset selection
• Combining all these elements into a step-by-step guide for PCA-based analysis and trading

Principal Component Analysis: Practice
• Using PCA for yield curve analysis
• Using PCA for swaption analysis
• Using PCA for hedging and asset selection
• Using PCA in other markets: Stocks, FX, commodities

Mean Reversion (MR): Theory
• What is mean reversion and how does it help us?
• Mathematics and model selection
• Calculating conditional expectations and probability densities
• Calculating Sharpe ratios
• Calculating first passage times

Mean Reversion: Practice
• Which performance is likely over which horizon?
• Setting performance targets
• Setting stop loss levels

Practical case study: Applying Statistical RV Models in a Trading Context
• Perform a PCA on the yield curve and find trading opportunities
• Run a mean reversion model to assess the performance potential and speed of these trades

Multi-Variate Ornstein-Uhlenbeck Process (MVOU)
• Combining MR and correlation between time series into a single model
• Intuition and Mathematics of MVOU
• Application 1: Butterfly analysis
• Application 2: Analysis of a portfolio of trades

Day 2: Asset Swaps, Basis Swaps, Default Swaps, and their Combinations
Overview
• Link and mutual influences between all swap markets worldwide
• Consequences for analytic approach and goals
• Roadmap for the day

Reference Rates
• The transition from LIBOR to OIS and SOFR
• Repo market and SOFR calculation
• A model for the spread between unsecured (LIBOR) and secured (repo) rates

Asset Swap Spreads (ASW)
• Model approach: ASW combine a funding and a credit swap
• Model the funding swap via the LIBOR-repo model
• Model the credit swap via adjusting CDS quotes
• Swap spread curves and driving factors of flattening/steepening
• Cyclicality of swap spreads
• Factors not covered in the model (yet): Haircuts, shadow cost of capital and impacts from other markets via the basis swap

Credit Default Swaps (CDS)
• FX component and other pricing issues
• Using adjusted CDS quotes for swap spread analysis and trading
• Other RV trades with CDS

Basis Swaps (BSW)
• Intra-currency basis swaps
• Cross-currency basis swaps
• Swapping bonds into a different currency
• Spreads versus USD SOFR as common yardstick for all global bonds

Combinations and Mutual Influences Between ASW, BSW and CDS
• The “arbitrage inequality” between ASW, BSW and CDS
• Trading this “arbitrage inequality” in practice
• The equilibrium between all global swap markets

Practical Case Study: The Mutual Influences of ASW, BSW and CDS in the JGB Market
Global Bond RV via SOFR Asset Swap Spreads
• Deficiencies of using swap spreads as rich/cheap indicator for bonds
• Mitigating these deficiencies for SOFR asset swap spreads

Other Influencing Factors
• Haircuts
• Regulatory constraints
• Shadow cost of (arbitrage) capital
• The limitations of swap spread models

Day 3: Analytic Tools and Framework for Bonds, Futures and Options
Fitted Curves for Bond Markets
• Goals: Generation of constant maturity time series without structural breaks and of rich/cheap indicators for asset selection
• Functional forms
• External explanatory variables such as benchmark or CTD status
• Model setup
• Interpretation and application of results
• Comparision with SOFR asset swap spreads as rich/cheap indicators

An Analytic Process for Government Bond Markets
• Combining fitted curves, PCA and MR (or MVOU)
• Monitoring the market for trading opportunities
• Defining exposure
• Asset selection
• Execution optimisation
• Portfolio considerations

Bond Futures and their Delivery Option
• The importance of the delivery option
• Usual approach to price the delivery option and its problems
• A better approach to price the delivery option
• Applications: Basis trades and calendar spreads/rolls

Swaption Trading Strategies
• Brief review of option pricing theory
• Classification of option trades
• Different exposures and goals of the different option trades

Swaption Trading Strategy 1: Conditional Curve Trades
• Single underlying: Breakeven analysis, breakeven curves, link to macro models
• Multiple underlyings: Conditional steepeners and butterflies

Swaption Trading Strategy 2: Implied Versus Realized Volatility
• Single underlying: Delta hedging, calculation of realized volatility
• Multiple underlyings: Implied vol curve versus realized vol curve

Swaption Trading Strategy 3: Implied Versus Implied Volatility
• Factor model for the swaption vol surface
• Practical pitfalls

Practical Case Study: Finding, Classifying and Analysing Swaption Trades on the USD Vol Surface