MONFORT Alain

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This thesis is dedicated to a particular class of nonlinear dynamic factor models, the Markovian regime-switching dynamic factor models (MS-DFM). By combining the features of the dynamic factor model and the Markov regime-switching model (i.e. the ability to aggregate massive amounts of information and to track fluctuating processes), this framework has proven to be very useful and suitable for several applications, the most important of which is the analysis of business cycles.The knowledge of the current state of business cycles is crucial in order to monitor economic health and to evaluate the results of economic policies. Nevertheless, this is not an easy task to achieve because, on the one hand, there is no commonly accepted data set and methods to identify turning points, and on the other hand, official institutions announce a new turning point, in countries where such a practice exists, with a structural delay of several months.The MS-DFM is able to solve these problems by providing estimates of the current state of the economy in a fast, transparent and reproducible way based on the common component of macroeconomic indicators characterizing the real sector.This thesis contributes to the vast literature on the identification of turning points of the business cycle in three directions. In Chapter 3, the two MS-DFM estimation techniques, the one-step and the two-step methods, are compared and applied to French data to obtain the timing of business cycle turning points. In Chapter 4, on the basis of Monte Carlo simulations, we study the convergence of the estimators of the chosen technique - the two-step estimation method - and we analyze their behavior in finite sample. In Chapter 5, we propose an extension of MS-DFM - the dynamically influenced MS-DFM (DI-MS-DFM) - that allows us to evaluate the contribution of the financial sector to the dynamics of the business cycle and vice versa, while taking into account the fact that the interaction between them may be dynamic.

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A bank that lends on the unsecured market requires compensations for facing the default risk of the borrowing bank (credit risk) and the risk associated to its own future funding needs (liquidity risk). In this paper, we propose a quadratic term-structure model of the spreads between unsecured and risk free interbank rates. Our no-arbitrage econometric framework allows us to decompose the term structure of spreads into credit and liquidity components and to identify risk premia associated with each of these two risks. Our results suggest that, over the period 2012-2013, most of the reduction in interbank spreads comes from a decrease in liquidity-related risk components.

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This thesis presents several extensions to positive affine interest rate models. A first chapter introduces the concepts related to the models used in the following chapters. It details the definition of so-called affine processes, and the construction of asset price models obtained by non-arbitrage. Chapter 2 proposes a new estimation and filtering method for linear-quadratic state-space models. The next chapter applies this estimation method to the modeling of interbank spreads in the Eurozone, in order to decompose the fluctuations related to default and liquidity risk. Chapter 4 develops a new technique to create multivariate affine processes from their univariate counterparts, without imposing conditional independence between their components. The last chapter applies this method and derives a multivariate affine process in which some components can remain at zero for extended periods. Incorporated into an interest rate model, this process can efficiently account for zero-bottom rates.

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This article presents an intensity-based model of euro-area sovereign spreads. To identify liquidity-pricing effects, we exploit the information contained in the spreads between bonds issued by a German agency (KfW) and their sovereign counterparts. KfW’s liabilities being guaranteed by the German government, these spreads are essentially liquidity-driven. Liquidity effects are found to account for a sizeable share of spreads’ fluctuations. After having filtered risk premiums out of the spreads, we estimate the physical default probabilities of eleven countries. Physical probabilities of default are lower than risk-neutral ones, consistently with the existence of a nondiversifiable euro-area sovereign credit risk.

This thesis develops different regime-switching models of the term structure of interest rates. A general framework for modeling the rates associated with different issuers is presented (chapter 2). This framework is exploited to analyze the government rates of ten euro zone countries between 1999 and 2012 (chapter 3). A crisis regime is used to explain the increase in rate volatility during the financial crisis. This study also shows that the liquidity of securities is a determining factor for their valuation. The modeling framework is completed in order to study the causal link between two types of tensions: those linked to liquidity motives and those linked to credit motives (chapter 4). Finally, the influence of monetary policy on the yield curve is examined through a model in which an innovative use of regime shifts allows for the production of realistic paths for central bank policy rates (chapter 5).

This article proposes an overview of the usefulness of the regime switching approach for building various kinds of bond pricing models and of the roles played by the regimes in these models. Both default-free and defaultable bonds are considered. The regimes can be used to capture stochastic drifts and/or volatilities, to represent discrete target rates, to incorporate business cycles or crises, to introduce contagion, to reproduce zero lower bound spells, or to evaluate the impact of standard or nonstandard monetary policies. From a technical point of view, we stress the key role of Markov chains, Compound Autoregressive (Car) processes, Regime Switching Car processes and multihorizon Laplace transforms.

The general objective of this thesis is to propose a discrete-time approach to dynamic price modeling of various financial or physical assets: options on stocks, zero-coupons, bonds, interest rate derivatives (swaps, caps, floors, options on zero-coupons), forwards or futures contracts on financial or physical assets, options on forwards or futures. These models can be used for derivatives valuation, price and return forecasting or hedging. All the proposed models have important points in common: the definition of factors, the specification of the historical dynamics of these factors, the introduction of a stochastic discount factor, the consideration of no-arbitrage constraints, the derivation of risk-neutral dynamics, the computation of financial asset prices, and the statistical inference on the model parameters.

Non-linear state-space models (or dynamic models with latent variables) form a very broad class that includes in particular many models used in economics and finance. However, the development of these models is hampered by the difficulties of calculating the likelihood, which requires the calculation of integrals whose dimension is a multiple of the number of observations. This thesis deals with the use of simulation-based methods, which provide powerful tools to solve this type of problem. After presenting the existing methods in the literature (chapter 1), extensions and new methods are proposed in the four other chapters. Chapter 2 proposes the indirect functional inference approach, which is a very general estimation method based on the principle of indirect inference. This method considers as link functions conditional moments estimated by non-parametric techniques. Chapters 3 and 4 deal with models with regime shifts for which the filter introduced by Hamilton does not allow to compute the likelihood. In chapter 3, a class of simulators, based on the importance function technique, is proposed to approximate the likelihood function in the framework of state-space models with regime changes. Chapter 4 suggests a Bayesian resolution, using partially non-informative laws and hybrid Gibbs sampling, for the estimation of arma models with regime changes. In the last chapter, MCMC type algorithms are used and the simulated likelihood ratio method is proposed to approximate the likelihood function and thus the maximum likelihood estimator. This is a very general approach that has many advantages. In each chapter, the theoretical properties of the estimation method, filtering and smoothing are studied and Monte-Carlo experiments illustrate the good performances of the proposed methods.