Exact and asymptotic tests for possibly non-regular hypotheses on stochastic volatility models

Dufour, Jean–Marie; Valéry, Pascale

doi:10.1016/j.jeconom.2008.12.020

Cited by 18 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The issue with this approach is that the derivatives needed in the delta method are not straightforward to calculate. An alternative approach consists in computing joint confidence sets for the model parameters as done for instance in [21] for stochastic volatility models and in [22] for GARCH models with heavytailed innovations. This method constructs simultaneous confidence sets for the parameters of the model by numerically "inverting" some test statistic such as the likelihood ratio test.…”

Section: The Time-varying Gerber Correlationmentioning

confidence: 99%

A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

Algieri

Leccadito

Toscano³

2021

Forecasting

View full text Add to dashboard Cite

This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window.

show abstract

Section: The Time-varying Gerber Correlationmentioning

confidence: 99%

A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

Algieri

Leccadito

Toscano³

2021

Forecasting

View full text Add to dashboard Cite

show abstract

“…We test the presence of a drift on the Standard and Poor's composite price index (SP), 1928-87. That process is known to involve a large amount of heteroscedasticity and have been used by Gallant et al (1997) and Dufour and Valéry (2008) to fit a stochastic volatility model. Here, we are interested in robust testing without modelling the volatility in the disturbance process.…”

Section: Illustrative Application: Standard and Poor's Driftmentioning

confidence: 99%

Finite-sample distribution-free inference in linear median regressions under heteroscedasticity and non-linear dependence of unknown form

Coudin

Dufour

2009

Econometrics Journal

Self Cite

View full text Add to dashboard Cite

We construct finite-sample distribution-free tests and confidence sets for the parameters of a linear median regression, where no parametric assumption is imposed on the noise distribution. The set-up studied allows for non-normality, heteroscedasticity, non-linear serial dependence of unknown forms as well as for discrete distributions. We consider a mediangale structure--the median-based analogue of a martingale difference--and show that the signs of mediangale sequences follow a nuisance-parameter-free distribution despite the presence of non-linear dependence and heterogeneity of unknown form. We point out that a simultaneous inference approach in conjunction with sign transformations yield statistics with the required pivotality features--in addition to usual robustness properties. Monte Carlo tests and projection techniques are then exploited to produce finite-sample tests and confidence sets. Further, under weaker assumptions, which allow for weakly exogenous regressors and a wide class of linear dependence schemes in the errors, we show that the procedures proposed remain asymptotically valid. The regularity assumptions used are notably less restrictive than those required by procedures based on least absolute deviations (LAD). Simulation results illustrate the performance of the procedures. Finally, the proposed methods are applied to tests of the drift in the Standard and Poor's composite price index series (allowing for conditional heteroscedasticity of unknown form). Copyright (C) The Author(s). Journal compilation (C) Royal Economic Society 2009

show abstract

“…5 Thus, compared to the MMC approach, the AMMC approach will be less time-consuming (Phipps and Byron, 2007). Although the MMC approach is gaining popularity (Beaulieu, Dufour and Khalaf, in press;Dufour and Tarek, 2006;Dufour and Valéry, 2009;Frederic and Olivier, 2006;Thomas et al, 2007), it is criticized for the following reasons: (1) it can be computationally demanding, (2) MMC-based actual rejection frequency may be very much less than the level of the test and may in consequence be severely lacking power and (3) there is a possibility of getting a much larger p value for nuisance parameter values far away from the ones that actually generated the data (MacKinnon, 2009). This paper adopts the classical approach.…”

Section: Introductionmentioning

confidence: 99%

Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach

Sriananthakumar

2013

Economic Modelling

View full text Add to dashboard Cite

Exact and asymptotic tests for possibly non-regular hypotheses on stochastic volatility models

Cited by 18 publications

References 41 publications

A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

Finite-sample distribution-free inference in linear median regressions under heteroscedasticity and non-linear dependence of unknown form

Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach

Contact Info

Product

Resources

About