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The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investig...
The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the per...
We present a new theory of homogeneous volatility (and variance) estimators for arbitrary stochastic processes. The main tool of our theory is the parsimonious encoding of all the information containe...
We present a novel procedure for scaling relatively high frequency tail probability and quantile estimates for the conditional distria.
EGARCH models for conditionally heteroscedastic time series have attracted a steadily increasing degree of attention in financial econometrics and related fields. These models are able to represent so...
This paper examines the precision of estimators of Quantile-Based Risk Measures (Value at Risk, Expected Shortfall, Spectral Risk Measures). It first addresses the question of how to estimate the prec...
We derive closed-form expansions for the asymptotic distribution of Hansen and Scheinkman [1995. Back to the future:generating moment implications for continuous-time Markov processes. Econometrica 63...
This paper studies the estimation of the volatility parameter in a model where the driving process is a Brownian motion or a more general symmetric stable process that is perturbed by another Lévy pro...
We provide a general method to analyze the asymptotic properties of a variety of estimators of continuous time diffusion processes when the data are not only discretely sampled in time but the time se...

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