State-space dynamic functional regression for multicurve fixed income spread analysis and stress testing

Forecasting the yield curve with the arbitrage-free dynamic Nelson–Siegel model: Brazilian evidence

PurposeThe purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA).Design/methodology/approachThe authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors.FindingsMonthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting.Practical implicationsThe authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue.Originality/valueTo the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-Standard Monetary Policy in the Euro Area

The macroeconomic determinants of the US term structure during the Great Moderation

Dynamic Credit Default Swaps Curves in a Network Topology

On Brazil’s Term Structure: Stylized Facts and Analysis of Macroeconomic Interactions

Yield Curve Factors, Factor Volatilities, and the Predictability of Bond Excess Returns

In recent years, affine term structure models have provided alternatives to the expectations hypothesis and have become very popular in the finance literature. In particular, the widely accepted dynamic Nelson–Siegel model employs ingenious measures of the level, slope, and curvature of the yield curve that captured the attention of Francis and Hua. They supplement the dynamic Nelson-Siegel model with the Federal Reserve’s Survey of Professional Forecasters data. Because these data utilize information from dozens of professional forecasters who study numerous macroeconomic variables, the authors wanted to see if this information-rich supplementary data could be used to improve the interest rate forecasting models for out-of-sample forecasts for Treasury bond maturities ranging from three months to 10 years that extend from three months to one year into the future. <b>TOPICS:</b>VAR and use of alternative risk measures of trading risk, factor-based models, statistical methods

The term structure of interest rates, i.e., yield curve, has long been of interest to monetary policymakers and their advisers. The transmission of monetary is conventionally viewed as running from shortterm interest rates managed by central banks to longer-term rates that influence aggregate demand. A central bank's leverage over longer-term rates comes from fact that market determines these as average expected level of short rates over relevant horizon (abstracting from a term premium and default risk). Working in other direction, long bond rate contains a premium for expected and, thus, serves as an indicator of credibility of a central bank's commitment to low inflation.1 Different theoretical perspectives support two above-mentioned uses of term structure for monetary policy: John Hicks's (1939) expectations theory of term structure supports first, and Irving Fisher's (1896) decomposition of nominal bond rates into expected and an expected real return supports second.2 The two views are compatible in principle, although reconciling them creates difficulties of interpretation in practice. For example, does a steepening yield curve indicate a loss of confidence in central bank's commitment to low inflation, or does it indicate that markets expect tighter in form of a higher path of short rates pursued by central bank? The yield curve contains information of use to monetary policymakers, but it needs to be interpreted in light of judicious subsidiary identifying conditions, together with other data on economy. Some circumstances lend themselves to clearer interpretations than others, and there are many pitfalls. Whether or not one regards longer-term interest rates as economic indicators or as part of transmission mechanism for policy, or both, term structure plays a potentially important role in policymaking process. In spite of its complexity, term structure cannot be ignored. This article addresses some issues involved in using term structure to conduct monetary policy. I begin by discussing long bond rate as an indicator of expectations. Second, I comment on role that bond rates have played in recent U.S. monetary history. Third, I explain how information in yield curve can be used to overcome what I call policy in pipeline problem. Fourth, I review recent empirical evidence supporting two theoretical views underlying our understanding of term structure. I explain how peso problems associated with inflation scares in bond market may help to account for a serious empirical failure of expectations theory of term structure. I also discuss evidence supporting view that significant movements in long-term interest rates are largely driven by expected inflation. Finally, I point out some pitfalls of using term structure to make tactical decisions. 1. PURSUING LOW INFLATION The Chairman of Federal Reserve Board, Alan Greenspan, supports a longrun goal for price stability such that the expected rate of change of general level of prices ceases to be a factor in individual and business decisionmaking.3 The long bond rate is particularly well suited to help a central bank assess degree to which it has achieved low defined in that way. One could compare behavior of yield on a long-term nominal bond to its behavior in a past period in which was very low and public was reasonably confident that it would stay low. For instance, in United States 30-year nominal bond rate ranged from around 3 percent to a little over 4 percent from mid-1950s until mid-1960s, a period in which averaged around 1 to 2 percent, and presumably, long-term expectations were no more than that.4 One would think that if Federal Reserve (the Fed) were to achieve full credibility for low inflation, long bond rate would once again move down to 3 to 4 percent range. …

Zero coupon rates are not observable in the market for a range of maturities. Therefore, an estimation methodology is required to derive the zero coupon yield curves from observable data. If we deal with approximations of empirical data to create yield curves it is necessary to choose suitable mathematical functions. We use parametric model of Nelson and Siegel. The current mathematical apparatus employed for this kind of approximation is outlined. This theoretical background is applied to an estimation of the zero-coupon yield curve derived from the Czech coupon bond market. There are many methodologies and each can provide surprisingly different results. Nevertheless, each seeks to provide an estimation that fit the data well while maintaining an easily interpretable form. On an initial test data sample we have not faced any problems, reported elsewhere, of not having found the global optimum or having found multiple local minima.

A hybrid spline-based parametric model for the yield curve

This chapter highlights aspects of the vibrant ongoing research program associated with the ideas developed in earlier chapters. It begins with a collage-style sketch of work involving Bayesian analysis, functional form for factor loadings, term structures of credit spreads, and nonlinearities. It then discusses in greater detail the incorporation of more than three yield factors. Next, it treats stochastic volatility in both dynamic Nelson–Siegel model (DNS) and arbitrage-free DNS (AFNS) environments, with some attention to the issue of unspanned stochastic volatility. Finally, it discusses the incorporation of macroeconomic fundamentals in their relation to bond yields. It also introduces aspects of modeling real versus nominal yields in DNS/AFNS environments, a theme treated in detail in Chapter 5.

A macro-finance term structure model with multivariate stochastic volatility

In this paper, we develop evidence related to the efficiency with which movements in the yield curve for United States government securities are predictable by reference to a representative set of exogenous macroeconomic determinants of forward market rates of interest. This evidence relates importantly to the efficiency with which such macroeconomic information is incorporated into the current position of the yield curve. If the position of the yield curve quickly and fully adjusts to all relevant economic information, then any particular subset of this information (such as that associated with an econometric forward rate structure) will not importantly associate with future movements in the yield curve. Instead, such future movements will primarily be the result of future changes in the state of relevant economic information. However, in the event that current yield curves do not fully reflect relevant economic information, then such information may have predictive content for future yield curves. Thus, this examination of the efficiency with which the yield curve can be predicted has important implications regarding the efficiency with which information is incorporated into the current yield curve. The model employed to achieve this analysis follows Fisherian term structure theory in assuming that forward rates are the most direct representation of variables about which investors hold interest rate expectations. Accordingly, a vital part of our approach to measuring yield-curve expectations is a model explaining expected movements in forward rates. In this regard, we rely upon recent work by ourselves [3] in which a model of forward markets expectations has been developed and found to be well supported by the data in a series of in-sample tests. The substantial explanatory power of our previous model in explaining current forward rates by reference to current macroeconomic variables suggests it is an adequate representation of the process by which changes in information are contemporaneously incorporated into the yield curve. If this same model also leads to efficient predictions, the suggestion is that all the information in the exogenous variables is not incorporated into the current yield curve position; conversely if the