We introduce nonnested hypotheses tests using indirect, simulation-based estimation procedures. We propose different test statistics and carefully examine the corresponding implicit mi hypotheses. A notion of indirect encompassing comes up naturally, which may be compared to the usual encompassing conditions. Then we show how these methods extend standard specification and overidentification tests.

Christian Gourieroux, Alain Monfort; On the Problem of Missing Data in Linear Models, The Review of Economic Studies, Volume 48, Issue 4, 1 October 1981, Pages

[This paper is concerned with linear models. In this context it gives a necessary and sufficient condition for equality of two generalized least squares estimators of any subset of the parameters. The result is applied to many kinds of problems: the Frisch-Waugh problem, equality of OLS and GLS estimators, equality of the single equation GLS and the overall GLS estimators in seemingly unrelated regressions, equality of partial and overall 3SLS, lineartransformation of a linear model, superfluous observations, and mixed estimation.]

We introduce new semi-parametric models for the analysis of rates and proportions, such as proportions of default, (expected) loss-given-default and credit conversion factor encountered in credit risk analysis. These models are especially convenient for the stress test exercises demanded in the current prudential regulation. We show that the Least Impulse Response Estimator, which minimizes the estimated effect of a stress, leads to consistent parameter estimates. The new models with their associated estimation method are compared with the other approaches currently proposed in the literature such as the beta and logistic regressions. The approach is illustrated by both simulation experiments and the case study of a retail P2P lending portfolio.

We define a battery of Sharpe performance measures, which differ by the information taken into account in their computation, but also by the potential use of the fund by the investor. Four advantages of Sharpe performance based rating are especially important for the investor. First, the performance measures correspond to the standard measures used for mutual funds and known by retail investors. Second, we can compare the numerical results, even if they are obtained with different assumptions. Third, the rankings are based on regression analysis and easy to compute. Fourth, we can easily use these performance measures in the design of an optimal basket of hedge funds. Finally, we can use the performance measures to partition the set of funds into homogenous segments.

In this paper, we introduce the extended method of moments (XMM) estimator. This estimator accommodates a more general set of moment restrictions than the standard generalized method of moments (GMM) estimator. More specifically, the XMM differs from the GMM in that it can handle not only uniform conditional moment restrictions (i.e., valid for any value of the conditioning variable), but also local conditional moment restrictions valid for a given fixed value of the conditioning variable. The local conditional moment restrictions are of special relevance in derivative pricing to reconstruct the pricing operator on a given day by using the information in a few cross sections of observed traded derivative prices and a time series of underlying asset returns. The estimated derivative prices are consistent for a large time series dimension, but a fixed number of cross sectionally observed derivative prices. The asymptotic properties of the XMM estimator are nonstandard, since the combination of uniform and local conditional moment restrictions induces different rates of convergence (parametric and nonparametric) for the parameters.

We define a disastrous default as the default of a systemic entity. Such an event is expected to have a negative effect on the economy and to be contagious. Bringing macroeconomic structure to a no-arbitrage asset-pricing framework, we exploit prices of disaster-exposed assets (credit and equity derivatives) to extract information on (i) the expected influence of a disastrous default on consumption and (ii) the probability of a financial meltdown. Using European data, we find that the returns of disaster-exposed assets are consistent with a systemic default being followed by a 2% decrease in consumption. The recessionary influence of disastrous defaults implies that financial instruments whose payoffs are exposed to such credit events carry substantial risk premiums. We also produce systemic risk indicators based on the probability of observing a certain number of systemic defaults or a sharp drop of consumption.

The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called “structural” shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is “static” and is due to the fact that there is an infinite number of linear transformations of a given random vector making its components uncorrelated. The second identification problem is “dynamic” and is a consequence of the fact that, even if a SVARMA admits a non-invertible moving average (MA) matrix polynomial, it may feature the same second-order dynamic properties as a VARMA process in which the MA matrix polynomials are invertible (the fundamental representation). The aim of this article is to explain that these difficulties are mainly due to the Gaussian assumption, and that both identification challenges are solved in a non-Gaussian framework if the structural shocks are assumed to be instantaneously and serially independent. We develop new parametric and semi-parametric estimation methods that accommodate non-fundamentalness in the MA dynamics. The functioning and performances of these methods are illustrated by applications conducted on both simulated and real data.