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9054 results in Finance and accountancy

Page 130 of 453

  • 3 - Links among Models, Monetary Policy and the Macroeconomy

  • from Part I - The Foundations
  • Riccardo Rebonato
  • Book:
    Bond Pricing and Yield Curve Modeling
    Published online:
    25 May 2018
    Print publication:
    07 June 2018, pp 49-62

  • Summary

    THE PURPOSE OF THIS CHAPTER

    In our treatment we do not provide a macroeconomic or monetary-economics foundation of yield curve modelling. In this sense, our approach is therefore ‘reduced-form’: we acknowledge that deeper drivers (say, expected inflation, or the output gap) than the variables we use (say, the short rate, or the slope of the yield curve) affect the yield curve; but, for a variety of reasons, in our modelling we choose to make use of the latter, less fundamental, variables instead of the fundamental ones.

    Why would we want to do so? Perhaps because they are more easily observable (estimating the slope of the yield curve requires a Bloomberg terminal; assessing the output gap is a tad more difficult). Perhaps because they synthetically embed a lot of information about the more fundamental variables (much as the temperature of a gas ‘contains’ information about the velocities of its molecules). Perhaps because they give rise to a simpler analytical treatment (we will show, for instance, that bond prices are given by expectations of a function of the path of the short rate). More generally, whenever one uses a reduced-form approach, one has a rough idea of the link between the fundamental variables and the phenomenon at hand, but one despairs of one's ability to connect all the modelling dots from the former to the latter. One therefore takes a number of judicious shortcuts through the toughest hairpin turns, hoping that one will still land in the right direction.

    Even if we embrace a high-level, reduced-form approach it is still very useful to have at least an approximate picture of the links between the reduced-form variables that we use and the more fundamental variables that drive the yield curve. Providing this link is the task undertaken in this chapter. As we do so, we also present the first extension of the simple Vasicek model that we will encounter in Chapter 10.

  • 1 - What This Book Is About

  • from Part I - The Foundations
  • Riccardo Rebonato
  • Book:
    Bond Pricing and Yield Curve Modeling
    Published online:
    25 May 2018
    Print publication:
    07 June 2018, pp 3-23

  • Summary

    It is not my intention to detain the reader by expatiating on the variety, or the importance of the subject, which I have undertaken to treat; since the merit of the choice would serve to render the weakness of the execution still more apparent, and still less excusable. But […] it will perhaps be expected that I should explain, in a few words, the nature and limits of my general plan.

    Edward Gibbon, The Decline and Fall of the Roman Empire

    MY GOAL IN WRITING THIS BOOK

    In this book I intend to look at yield-curve modelling from a ‘structural’ perspective. I use the adjective structural in a very specific sense, to refer to those models which are created with the goal of explaining (as opposed to describing) the yield curve. What does ‘explaining’ mean? In the context of this book, I mean accounting for the observed yields by combining the expectations investors form about future rates (and, more generally, the economy) and the compensation they require to bear the risk inherent with holding default-free bonds. (As we shall see later, there is a third ‘building block’, ie, convexity.)

    This provides one level of explanation, but one could go deeper. So, for instance, the degree of compensation investors require in order to bear ‘interestrate risk’ could be derived (‘explained’) in more fundamental terms from the strategy undertaken by a rational, risk-averse investor who is faced with a set of investment opportunities and wants to maximize her utility from consumption in amultiperiod economy. I will sketch with a broad brush the main lines of this fundamental derivation, but will not pursue this line of argument in great detail. The compensation exacted by investors for bearing market risk (the ‘market price of risk’) will instead be empirically related (say, via regressions) either to combinations of past and present bond prices and yields, or to past history and present values of macroeconomic variables.

    Another way to look at what I try to do in this book is to say that I describe the market price of risk in order to explain the yield curve. If one took a more ‘fundamental’ approach, one could try to explain the market price of risk as well, but would still have to describe something more basic, say, the utility function.

  • 34 - Generalizations: The Adrian–Crump–Moench Model

  • from Part VII - What the Models Tell Us
  • Riccardo Rebonato
  • Book:
    Bond Pricing and Yield Curve Modeling
    Published online:
    25 May 2018
    Print publication:
    07 June 2018, pp 663-687

  • Summary

    THE PURPOSE OF THIS CHAPTER

    In this chapter we generalize the treatment in Chapters 32 and 33. As the reader will recall, in those chapters we played match makers, trying to unite the statistical and the no-arbitrage research strands when the state variables were chosen to be (proxies of) the yield principal components.

    The generalization presented in this chapter follows the treatment in Adrian, Crump and Moench (2013), who

  • choose some linear combination of yields as state variables;

  • impose that bonds should be priced as expectations of the state-pricedeflator (doing so ensures absence of arbitrage);

  • enforce the affine assumption for the yields and the market price of risk;

  • express the excess returns in terms of the affinely constrained stateprice deflator;

  • recast the resulting equations in such a form that they lend themselves to an econometric estimation of the parameters of the process for these state variables; and

  • establish a connection between the parameters of the statistical models and the ‘yield coefficients’ of the underlying exponentially affine model.

    • The heart of this procedure is point 5 (the econometric estimation). By carrying out this estimation, the authors are able to determine the state-dependent market price of risk, which constitutes the key ‘structural’ bridge between the real-world and risk-neutral measures in their explanatory view of yield curve dynamics.

    We report and comment on the estimates of the term premia in the closing sections of the chapter, and we revisit the issue of under what conditions expected excess returns are the same as term premia. Finally, we discuss what happens when they are not.

    We stress that, besides being of intrinsic interest, the model by Adrian et al. (2013) can also be seen as a representative instance of similar modelling approaches, such as the one in Ang and Piazzesi (2003).

    THE STRATEGY BEHIND THE ADRIAN–CRUMP–MOENCH MODEL

    One of the most important features of the approach is by Adrian, Crump and Moench (2013) is that a ‘traditional’ affine model has demurely receded in the background.

  • 35 - An Affine, Stochastic-Market-Price-of-Risk Model

  • from Part VII - What the Models Tell Us
  • Riccardo Rebonato
  • Book:
    Bond Pricing and Yield Curve Modeling
    Published online:
    25 May 2018
    Print publication:
    07 June 2018, pp 688-713

  • Summary

    A well-known scientist (some say it was Bertrand Russell) once gave a public lecture on astronomy. He described how the earth orbits around the sun and how the sun, in turn, orbits around the center of a vast collection of stars called our galaxy. At the end of the lecture, a little old lady at the back of the room got up and said: “What you have told us is rubbish. The world is really a flat plate supported on the back of a giant tortoise.” The scientist gave a superior smile before replying, “What is the tortoise standing on?” “You're very clever, young man, very clever,” said the old lady. “But it's turtles all the way down!”

    Stephen Hawking, A Brief History of Time, 1988

    THE PURPOSE OF THIS CHAPTER

    If one uses a structural affine model to describe the yield curve, one has to establish an affine relationship between the state variables and the market price of risk. In non-trivial cases (ie, if the affine relationship is not just a constant), the resulting behaviour for the market price of risk is stochastic – because the state variables are stochastic. However, if the values of the state variables are known (in statistical parlance, if one conditions on a particular set of values for the state variables), there can be one and only one possible value for the market price of risk.

    So, for instance, if one of the state variables is the slope of the yield curve, and, on the basis of our regression studies, we impose a deterministic (affine) relationship between market price of risk and the yield curve slope, then the instantaneous expected excess return will always be exactly the same whenever the slope has a certain value.

    In this chapter we break the deterministic link between the market price of risk and the return-predicting factor. We do so by introducing a model that enforces the regularities uncovered by the empirical investigation on average, but not at each point in time. We argue that this will result in a more realistic decomposition of the observed market yields into an expectation and a riskpremiumcomponent.

The Growth of Shadow Banking
  • A Comparative Institutional Analysis
  • Matthias Thiemann
  • Published online:
    26 July 2018
    Print publication:
    31 May 2018
  • The 'shadow banking system' refers to a system of credit-provision occurring outside of the official regulatory perimeter of commercial banks. Facilitated by securitization vehicles, mutual funds, hedge funds, investment banks and mortgage companies, the function and regulation of these shadow banking institutions has come under increasing scrutiny after the subprime crisis of 2007–8. Matthias Thiemann examines how regulators came to tolerate the emergence of links between the banking and shadow banking systems. Through a comparative analysis of the US, France, the Netherlands and Germany, he argues that fractured domestic and global governance systems determining the regulatory approach to these links ultimately aggravated the recent financial crisis. Since 2008, shadow banking has even expanded and the incentives for banks to bend the rules have only increased with increasing regulation. Thiemann's empirical work suggests how state-finance relations could be restructured to keep the banking system under state control and avoid future financial collapses.

Page 130 of 453