The Advanced Fixed Income and Derivatives Management Guide. Saied Simozar
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method of portfolio construction in fixed income. The two methods are complementary to each other; however, top-down is usually analyzed on a monthly or quarterly basis.
There is a step-by-step outline of building a spreadsheet based tool for designing new products or maintaining an existing portfolio. This tool provides the tracking error, marginal contribution to risk, and can be used for what-if analysis or to see how the portfolio would have performed during prior financial crises or how additions of new asset classes or sectors alter the risk profile of the portfolio. There is also a method to identify the structure of the competitive universe and design a product that could compete in that space.
We have provided detailed steps and formulation for the implementation of the framework that is outlined in the book. Many of the components can be built in spreadsheets; however, reliable and efficient analytics require the development of the necessary tools as separate programs. The benefits of such a framework and the potential performance improvements significantly outweigh its development costs.
Acknowledgement
You might think that following some of the seven hundred or so formulas in the book is not a trivial task, let alone deriving them. Kris Kowal, Managing Director and Chief Investment Officer of DuPont Capital Management, Fixed Income Division, offered to review the manuscript and re-derive nearly all the formulas in the book. Kris provided numerous helpful suggestions and comments that were instrumental in reshaping the book into its present form. In many cases, following Kris's recommendations additional steps were added to the derivations to make it easier for the reader to follow. Thanks Kris.
Foreword
In 1998, shortly after arriving at Putnam Investments, Saied Simozar began work on a model for the term structure of interest rates that was to become a cornerstone of an entire complex of portfolio management tools and infrastructure. It was fortuitous timing because that rate model had the dual benefits of being derived through current market pricing structure (rather than historical regressions) and the flexibility to quickly incorporate new security types.
The late 1990s marked something of a sea change in the fixed income markets. The years leading up to that period had been defined by big global themes and trends like receding global inflation rates and the development of out of benchmark sectors like high yield corporate bonds and emerging market debt, as well as global interest rate convergence under the nascent stages of European Monetary Union. Under these broad trends, return opportunities, portfolio positioning, and risk could easily be characterized in terms of duration and sector allocation percentages.
Much of that changed in 1998 when the combination of increasingly complex security types, rapid globalization of financial markets, and large mobile pools of capital set the stage for a series of rolling financial crises that rocked global financial markets and eventually led to the collapse of one of the most sophisticated hedge funds of that era – Long Term Capital Management. In the aftermath, it became clear that traditional methods of monitoring portfolio positioning and risk were insufficient to manage all the moving parts in modern fixed income portfolios.
Fortuitously, that term model (and the portfolio management tools built around it) allowed Putnam to effectively navigate through that financial storm. Perhaps more importantly, it provided the basis for an infrastructure that could easily adapt and change with the ever evolving fixed income landscape. Today, while many of the original components of that infrastructure have been augmented and updated, the basic tenants of the philosophical approach remains in place.
In his book, Saied lays out a blueprint for a set of integrated tools that can be used in all aspects of fixed income portfolio management from term structure positioning, analysis of spread product, security valuation, risk measurement, and performance attribution. While the work is firmly grounded in mathematical theory, it is conceptually intuitive and imminently practical to implement. Whether you are currently involved in the management of fixed income portfolios or are looking to get a better understanding of all the inherent complexities, you won't find a more comprehensive and flexible approach.
About the Author
Saied Simozar, PhD has spent almost 30 years in fixed income portfolio management, fixed income analytics, scientific software development and consulting. He is a principal at Fipmar, Inc., an investment management consulting firm in Beverly Hills, CA. Prior to that, Saied was a Managing Director at Nuveen Investments, with responsibilities for all global fixed income investments. He has also been a Managing Director at Bank of America Capital Management responsible for all global and emerging markets portfolios of the fixed income division. Prior to that, he was a senior portfolio manager at Putnam Investments and DuPont Pension Fund Investments.
Introduction
One of the keys to managing investment portfolios is identification and measurement of sources of risk and return. In fixed income, the most important source is the movement of interest rates. Even though changes in interest rates at different maturities are not perfectly correlated, diversifying a portfolio across the maturity spectrum will not lead to interest rate risk reduction. In general, a portfolio of one security that matches the duration of a benchmark tends to have a lower tracking error with the benchmark than a well-diversified portfolio that ignores duration.
Historically, portfolio managers have used Macaulay or modified duration to measure the sensitivity of a portfolio to changes in interest rates. With the increased efficiency of the markets and clients' demands for better risk measurement and management, several approaches for modeling the movements of the term structure of interest rates (TSIR) have been introduced.
A few TSIR models are based on theoretical considerations and have focused on the time evolution or stochastic nature of interest rates. These models have traditionally been used for building interest rate trees and for pricing contingent claims. For a review of these models, see Boero and Torricelli [1].
Another class of TSIR models is based on parametric variables, which may or may not have a theoretical basis, and their primary emphasis is to explain the shape of the TSIR. An analytical solution of the theoretical models would also lead to a parametric solution of the TSIR; see Ferguson and Raymar for a review [2]. Parametric models can be easily used for risk management and they almost always lead to an improvement over the traditional duration measurement. Willner [3] has applied the term structure model proposed by Nelson and Siegel [4] to measure level, slope and curvature durations of securities.
Key rate duration (KRD) proposed by Ho [5] is another attempt to account for non-parallel movements of the TSIR. A major shortcoming of KRD is that the optimum number and maturity of key rates are not known, and often on-the-run treasuries are used for this purpose. Additionally, key rates tend to have very high correlations with one another, especially at long maturities, and it is difficult to attach much significance to individual KRDs. The most important feature of KRD is that the duration contribution of a key rate represents the correct hedge for that part of the curve.
Another approach that has recently received some attention for risk management is the principal components analysis (PCA) developed by Litterman and Scheinkman [6]. In PCA, the most significant components of the yield curve movements are calculated through the statistical analysis of historical yields at various maturities. A very attractive feature of principal components, as far as risk management is concerned, is that they are orthogonal to each other (on the basis of historical data). The first three components of PCA usually account for more than 98 % of the movements of the yield curve.
Another class of yield curve models is based on splines. Cubic splines are widely used for fitting the yield curve and are useful for valuation purposes, to the extent that the yield curve is smooth. Cubic splines can be unstable, especially if the number of bonds is relatively low. For a review of different yield curve models, see Advanced Fixed Income Analysis by Moorad Choudhry [7].
All of the above models are useful either for risk management or pricing, but not for both. For portfolio management applications, it is quite difficult to translate either KRDs or PCA durations into positions in a portfolio. Likewise, it is not straightforward