Autoregressive Gamma Processes

Gouriéroux, Christian; Jasiak, Joann

doi:10.2139/ssrn.757358

Cited by 68 publications

(87 citation statements)

References 18 publications

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“…Here we describe and solve an analogous model based on ARG(1) processes, discrete-time analogs of continuous-time square-root processes. See, for example, Gourieroux and Jasiak (2006) and Le, Singleton, and Dai (2010). The analysis parallels Appendix I.E.…”

Section: Appendix H: Models Based On Arg Processesmentioning

confidence: 93%

Sources of Entropy in Representative Agent Models

Backus¹,

Chernov²,

Zin

2014

The Journal of Finance

135

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We propose two metrics for asset pricing models and apply them to representative agent models with recursive preferences, habits, and jumps. The metrics describe the pricing kernel's dispersion (the entropy of the title) and dynamics (time dependence, a measure of how entropy varies over different time horizons). We show how each model generates entropy and time dependence and compare their magnitudes to estimates derived from asset returns. This exercise -and transparent loglinear approximations -clarifies the mechanisms underlying these models. It also reveals, in some cases, tension between entropy, which should be large enough to account for observed excess returns, and time dependence, which should be small enough to account for mean yield spreads. JEL Classification Codes: E44, G12.

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Section: Appendix H: Models Based On Arg Processesmentioning

confidence: 93%

Sources of Entropy in Representative Agent Models

Backus¹,

Chernov²,

Zin

2014

The Journal of Finance

135

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show abstract

“…The vector z t follows a multivariate ARG ν (ϕ, μ) process. This process, introduced by Gouriéroux and Jasiak (2006), is the time-discretized Cox, Ingersoll, and Ross (1985) process (see also Monfort et al 2017).…”

Section: A4 Autoregressive Gamma Processesmentioning

confidence: 99%

Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

Grishchenko

Mouabbi

Renne

2019

J of Money Credit Banking

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We use several U.S. and euro‐area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time‐varying uncertainty. We obtain survey‐consistent distributions of future inflation at any horizon, both in the U.S. and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the United States, while mild de‐anchoring occurred in the euro area. As of our sample end, both areas appear to be almost equally anchored.

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“…-Plots ofφ (upper panels),φ * (middle panels), and the estimated change of measureφ(x)φ * (x) between the stationary distribution Q and the distribution Q corresponding to E (lower panels) under recursive preferences using the estimated preference parameters in the left panel of Table III. where {v t } is a first-order autoregressive gamma process (a discrete-time version of the Feller square-root process; see Gourieroux and Jasiak (2006)) so the state vector is X t = (g t v t ). We refer to the second specification as SV-AR(1).…”

Section: Empirical Applicationmentioning

confidence: 99%

Nonparametric Stochastic Discount Factor Decomposition

Christensen¹

2017

Econometrica

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We assume that the state process X = {X t : t ∈ T } is either beta-mixing or rho-mixing. The beta-mixing coefficient between two σ-algebras A and B iswith the supremum taken over all A-measurable finite partitions {A i } i∈I and Bmeasurable finite partitions {B j } j∈J . The beta-mixing coefficients of X are defined asWe say that X is exponentially beta-mixing if β q ≤ Ce −cq for some C c > 0. The rho-mixing coefficients of X are defined asWe say that X is exponentially rho-mixing if ρ q ≤ e −cq for some c > 0. We use the sequence ξ k = sup x G −1/2 b k (x) to bound convergence rates. When X has bounded rectangular support and Q has a density that is bounded away from 0 and ∞, ξ k is known to be O( √ k) for (tensor-product) spline, cosine, and certain wavelet bases and O(k) for (tensor-product) polynomial series (see, e.g., Newey (1997), Chen andChristensen (2015)). It is also possible to derive alternative sufficient conditions in terms of higher moments of) by extending arguments in Hansen (2015) to accommodate weakly dependent data and asymmetric matrices. Timothy M. Christensen:

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Autoregressive Gamma Processes

Cited by 68 publications

References 18 publications

Sources of Entropy in Representative Agent Models

Sources of Entropy in Representative Agent Models

Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

Nonparametric Stochastic Discount Factor Decomposition

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