Nour Meddahi

Generalized Disappointment Aversion, Long-run Volatility Risk, and Asset Prices

We assess the aggregate asset pricing implications of generalized disappointment aversion (GDA) in the long-run risks model of Bansal and Yaron (2004). Using analytical formulas for asset valuation ratios and several moment and predictive regression statistics we compare thoroughly several recursive utility models with longrun risks. While persistence of expected consumption growth is fundamental for the moment matching ability of Kreps-Porteus preferences, GDA relies mostly on the persistence of consumption volatility. The long-run growth risk, when coupled with Kreps-Porteus preferences, has the undesirable side-effect of generating the wrong predictability pattern: dividend yields forecast consumption growth but not excess returns. With GDA preferences, the persistent volatility of consumption growth capturing economic uncertainty is enough to generate realistic moments and the observed patterns of predictability. These results are robust to an intertemporal elasticity of substitution lower or greater than one.

Generalized Affine Models

Affine models are very popular in modeling financial time series as they allow for analytical calculation of prices of financial derivatives like treasury bonds and options. The main property of affine models is that the conditional cumulant function, defined as the logarithmic of the conditional characteristic function, is affine in the state variable. Consequently, an affine model is Markovian, like an autoregressive process, which is an empirical limitation. The paper generalizes affine models by adding in the current conditional cumulant function the lagged conditional cumulant function. Hence, generalized affine models are non-Markovian, such as ARMA and GARCH processes, allowing one to disentangle the short term and long-run dynamics of the process. Importantly, the new model keeps the tractability of prices of financial derivatives. This paper studies the statistical properties of the new model, derives its conditional and unconditional moments, as well as the conditional cumulant function of future aggregated values of the state variable, which is critical for pricing financial derivatives. It derives the analytical formulas of the term structure of interest rates and option prices. Different estimating methods are discussed including MLE, QML, GMM, and characteristic function based estimation methods. In a term structure of interest rate out-of-sample forecasting exercise, our results suggest that for a many horizons, a simple multivariate generalized affine model on observed yields predicts the whole term structure of the interest rate better than the VAR and the Nelson-Siegel’s model with AR(1) factor dynamic.

Bootstrapping High-Frequency Jump Tests

ABSTRACT The main contribution of this article is to propose a bootstrap test for jumps based on functions of realized volatility and bipower variation. Bootstrap intraday returns are randomly generated from a mean zero Gaussian distribution with a variance given by a local measure of integrated volatility (which we denote by ). We first discuss a set of high-level conditions on such that any bootstrap test of this form has the correct asymptotic size and is alternative-consistent. We then provide a set of primitive conditions that justify the choice of a thresholding-based estimator for . Our cumulant expansions show that the bootstrap is unable to mimic the higher-order bias of the test statistic. We propose a modification of the original bootstrap test which contains an appropriate bias correction term and for which second-order asymptotic refinements are obtained.