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Let's Get Lade : Robust Estimation of Semiparametric Multiplicative Volatility Models
Linton, Oliver B. - 2013
We investigate a model in which we connect slowly time varying unconditional long-run volatility with short …-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on … robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility …
Persistent link: https://www.econbiz.de/10013084890
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Locally robust semiparametric estimation
Chernozhukov, Victor; Escanciano, Juan Carlos; … - 2016
This paper shows how to construct locally robust semiparametric GMM estimators, meaning equivalently moment conditions have zero derivative with respect to the first step and the first step does not affect the asymptotic variance. They are constructed by adding to the moment functions the...
Persistent link: https://www.econbiz.de/10011517194
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Locally robust semiparametric estimation
Chernozhukov, Victor; Escanciano, Juan Carlos; … - 2018
We give a general construction of debiased/locally robust/orthogonal (LR) moment functions for GMM, where the derivative with respect to first step nonparametric estimation is zero and equivalently first step estimation has no effect on the influence function. This construction consists of...
Persistent link: https://www.econbiz.de/10011824067
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Smoothed influence function : another view at robust nonparametric regression
Tamine, Julien - 2001
In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show...
Persistent link: https://www.econbiz.de/10009626684
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Finite-Sample Generalized Confidence Distributions and Sign-Based Robust Estimators In Median Regressions with Heterogeneous Dependent Errors
Coudin, Elise - 2017
We study the problem of estimating the parameters of a linear median regression without any assumption on the shape of the error distribution -- including no condition on the existence of moments -- allowing for heterogeneity (or heteroskedasticity) of unknown form, noncontinuous distributions,...
Persistent link: https://www.econbiz.de/10012962776
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Semiparametric Robust Estimation of Truncated and Censored Regression Models
Cizek, Pavel - 2008
Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semiparametric general trimmed estimator (GTE) of truncated...
Persistent link: https://www.econbiz.de/10014047660
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Nonparametric Estimation of the Leverage Effect : A Trade-off between Robustness and Efficiency
Kalnina, Ilze - 2015
alone. The second approach uses the data on stock prices as well as a certain volatility instrument, such as the CBOE … volatility index (VIX) or the Black-Scholes implied volatility. The theoretical justification for the instrument-based estimator …-only estimator is more robust since it is valid under weaker assumptions. However, in the presence of a valid volatility instrument …
Persistent link: https://www.econbiz.de/10013034657
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Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends
McCloskey, Adam - 2012 - This version: October 2012
parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of …-memory. -- stochastic volatility ; frequency domain estimation ; robust estimation ; spurious persistence ; long-memory ; level shifts …
Persistent link: https://www.econbiz.de/10009660446
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Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends
McCloskey, Adam - 2012
parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of …
Persistent link: https://www.econbiz.de/10013098304
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Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility
Jensen, Mark J. - 2015
Empirical volatility studies have discovered nonstationary, long-memory dynamics in the volatility of the stock market … found with nonparametric estimates of the fractional differencing parameter d, for financial volatility. In this paper, a …, stochastic volatility (SV-FIAR) model. Joint estimates of the autoregressive and fractional differencing parameters of volatility …
Persistent link: https://www.econbiz.de/10011382237