This work discusses trading strategies with focus on the application in the government bond market. An arbitrage-free yield curve prediction model and a parametric estimation method are presented to form the basis of finding trading strategies. The arbitrage-free model is based on the Heath-Jarrow-Morton model. The parametric approach is the Dynamic Nelson-Siegel method. For the US Treasury yield curve the performance of both methods is tested and compared to each other.
Moreover, portfolio optimization with respect to the conditional value at risk is illustrated. A smoothing technique and the Nesterov procedure are exhibited as efficient implementations of the linked portfolio selection problem. At last, it is shown in an example for US Treasuries how the estimated yield curve can be incorporated into portfolio optimization to derive trading strategies.
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In der vorliegende Arbeit wird gezeigt, wie Strategien für das Handeln von staatlichen Obligationen entwickelt werden können. Die Basis hierzu bilden ein arbitrage-freier Ansatz und ein parametrischer Ansatz, um die Zinskurve vorherzusagen. Der arbitrage-freie Ansatz basiert auf dem Heath-Jarrow-Morton Modell, der parametrische Ansatz ist die dynamische Nelson-Siegel Methode. Der praktische Nutzen beider Verfahren wird für US Staatsanleihen untersucht und einander gegenüber gestellt.
Im Weiteren wird die Theorie der Portfolio Optimierung bezüglich des Conditional Value at Risks vorgestellt und zwei Verfahren zu dessen effizienten Implementierung erklärt. Schlussendlich wird an einem Beispiel für US Staatsanleihen gezeigt, wie die Methoden zur Zinsvorhersage in das Porfoliooptimierungsproblem mit einbezogen werden können, um Handelsstrategien zu entwickeln.
Contents
1 Preface
2 An Overview of Bonds
2.1 Bond Prices and The Yield Curve
2.2 Forces Driving Bond Prices
3 Arbitrage-free Yield Modelling
3.1 General Concepts
3.2 The Heath-Jarrow-Morton Model
3.3 The Time Discrete Heath-Jarrow-Morton Model
3.4 Empirical Quality of the Time Discrete HJM Model
4 Parametric Yield Curve Estimation
4.1 The Dynamic Nelson-Siegel Method
4.2 Shortcomings of the Dynamic Nelson-Siegel Method
4.3 Empirical Quality of the DNS Method
5 Portfolio Optimization
5.1 Optimal Portfolio with Regards to Conditional Value at Risk
5.2 Efficient Implementation
5.3 Example of Optimal Portfolios
6 Conclusion and Outlook
Research Objectives & Topics
This work aims to develop and evaluate trading strategies for government bonds by integrating yield curve forecasting models with portfolio optimization techniques that utilize Conditional Value at Risk (CVaR) as a risk measure.
- Theoretical and empirical comparison of the Heath-Jarrow-Morton (HJM) and Dynamic Nelson-Siegel (DNS) yield curve models.
- Application of the Conditional Value at Risk (CVaR) metric for portfolio selection.
- Implementation of efficient numerical procedures for portfolio optimization, including smoothing and Nesterov algorithms.
- Empirical assessment using US Treasury yield data from 2009 to 2013.
- Derivation of practical trading strategies based on yield predictions.
Extract from the Book
2.2 Forces Driving Bond Prices
In the preceding section four basic shapes of the yield curve are found. Those shapes are the normal, inverted, flat and humped shape. In the introductory section of this chapter four major risks of bonds are pinpointed which are default or credit risk, interest rate risk, inflation risk and liquidity risk. Also, the importance of the time value of money is pointed out. Subject of this chapter is to link the risks surrounding a bond investment to the different yield curve shapes. Within the scope of this the dynamics of bond prices are examined closer which become apparent through analysis of the bond’s risk factors. The bond’s yield and hence its price is influenced by a range of economical and political factors.
Whatever influences a bond’s yield influences the price of this bond due to the negative relationship between yields and prices. It is noted in the preceding section that bond holders have to be compensated for their loss in liquidity. Bonds with a long lifetime have to be cheap in comparison to bonds with a short lifetime to make them attractive to investors because the holder’s liquidity is limited for a longer time in case of the long-term bond. So as a bond approaches maturity its price tends to the amount of the face value. This translates to an upward-sloping normal shape of the yield curve. Different yield curve shapes show distortions of this effect caused by other phenomena like a changing interest rate.
Summary of Chapters
Preface: Introduces the importance of bonds, the motivation for yield curve modeling, and provides an overview of the HJM and DNS methodologies used in the study.
An Overview of Bonds: Explains basic bond market concepts, defines yield curve shapes, and identifies major risk factors and economic forces that influence bond prices.
Arbitrage-free Yield Modelling: Establishes the mathematical framework for HJM modeling, including the time-discrete version and empirical testing on US Treasury yields.
Parametric Yield Curve Estimation: Introduces the Dynamic Nelson-Siegel (DNS) method, discusses its theoretical shortcomings regarding arbitrage, and performs empirical analysis.
Portfolio Optimization: Develops the theory for optimizing portfolios using Conditional Value at Risk (CVaR) and compares different implementation strategies.
Conclusion and Outlook: Summarizes the key findings, noting the performance differences between the HJM and DNS models in the context of bond trading strategies.
Keywords
Bond Markets, Yield Curve, Heath-Jarrow-Morton Model, Dynamic Nelson-Siegel Method, Portfolio Optimization, Conditional Value at Risk, CVaR, Interest Rate Risk, Arbitrage-free, US Treasuries, Financial Modeling, Trading Strategies, Quantitative Easing, Inflation Risk, Liquidity Risk.
Frequently Asked Questions
What is the primary focus of this master's thesis?
The thesis focuses on developing and analyzing trading strategies for government bonds by using yield curve prediction models and portfolio optimization based on Conditional Value at Risk (CVaR).
What are the two main yield curve models compared in the work?
The author compares the arbitrage-free Heath-Jarrow-Morton (HJM) model and the parametric Dynamic Nelson-Siegel (DNS) method.
What is the central research question regarding trading strategies?
The research explores how expectations derived from different yield curve models can be successfully incorporated into portfolio optimization to derive effective trading strategies.
Which risk measure does the author prioritize for portfolio optimization?
The author uses Conditional Value at Risk (CVaR) as the primary measure of risk, citing its mathematical advantages over traditional variance-based methods.
What does the empirical part of the work consist of?
The empirical analysis utilizes US Treasury yield curve data spanning from January 2009 to November 2013 to test model performance and strategy outcomes.
What is the main finding regarding the performance of the models?
The HJM-based expectations generally perform better than the DNS-based expectations for the considered sample, particularly because the DNS method frequently struggles with non-standard, multi-humped yield curve shapes.
How does the author handle the bias in the HJM model?
The thesis defines an estimator for the yield changes and derives a formulation for the bias term, suggesting bias correction to achieve more accurate predictions, especially at the short end of the yield curve.
Why are smoothing techniques and the Nesterov procedure discussed?
These techniques are introduced to solve the portfolio optimization problem more efficiently, as the standard linear optimization approach can become computationally expensive and ill-posed.
- Quote paper
- Niklas Lachenicht (Author), 2015, Trading Strategies In Bond Markets, Munich, GRIN Verlag, https://www.grin.com/document/359274
