Nonparametric estimation of kernel functions of Brownian semi-stationary processes with an application to electricity markets

Shin Kanaya, Asger Lunde, Sauri Arregui Orimar

Research output: Working paperResearch

Abstract

This paper considers nonparametric estimation of Brownian
semi-stationary (BSS) processes. We establish fully nonparametric identification of the so-called kernel function through the autocovariance function of our the process of interest. This identification result allows us to propose a new nonparametric series-based estimator of the kernel function by matching t
he sample autocovariance function with the model-implied. We investigate finite-sample properties of the proposed estimator through a simulation study, and apply it to the study of spot price data from electricity markets.
Original languageEnglish
Number of pages41
Publication statusPublished - 2015

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Autocovariance Function
Electricity Market
Nonparametric Estimation
Stationary Process
Kernel Function
Nonparametric Identification
Estimator
Simulation Study
Series
Model

Cite this

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abstract = "This paper considers nonparametric estimation of Browniansemi-stationary (BSS) processes. We establish fully nonparametric identification of the so-called kernel function through the autocovariance function of our the process of interest. This identification result allows us to propose a new nonparametric series-based estimator of the kernel function by matching the sample autocovariance function with the model-implied. We investigate finite-sample properties of the proposed estimator through a simulation study, and apply it to the study of spot price data from electricity markets.",
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