Generalized Laplacian Distributions and Autoregressive Processes

Jose, Kim; Thomas, Manu Mariam

doi:10.1080/03610926.2010.508863

Cited by 7 publications

(1 citation statement)

References 22 publications

(13 reference statements)

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“…-Discrete case: Autoregressive (AR) processes with binomial distribution [11], ARMA with marginals negative binomial and geometric marginals and, in general, processes for integer values under subdispersion [12]. -Continuous case: Gamma processes [13], AR models with marginal generalized Laplace distribution [14] and AR processes with epsilon-skew-Gaussian innovations [15].…”

Section: Introductionmentioning

confidence: 99%

Scaled Muth–ARMA Process Applied to Finance Market

et al. 2023

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The analysis of financial market time series is an important source for understanding the economic reality of a country. We introduce a new autoregressive moving average (ARMA) process, the sMuth–ARMA model, which has the sMuth law as the marginal distribution and has one of its parameters as a proportion that can control amodal and unimodal behavior. We propose a procedure for obtaining the maximum likelihood estimators for its parameters and evaluate its performance for various link functions through Monte Carlo simulations. This research also addresses the issue of fluctuations in cryptocurrencies, which has played an increasingly important role in the global economy. An application to the range-based volatility of Tether (USDT) stablecoin prices shows the usefulness of the application of the proposed model over the Gaussian and other models reviewed.

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Section: Introductionmentioning

confidence: 99%

Scaled Muth–ARMA Process Applied to Finance Market

et al. 2023

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A notable Gamma‐Lindley first‐order autoregressive process: An application to hydrological data

2022

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A new Gamma-Lindley (GaL) first-order autoregressive process (AR) is introduced, called GaL-AR(1). The distribution for the GaL-AR(1) innovation process and some of its structural properties are derived, such as the Laplace transform for its bivariate distribution, the conditional variance and expected value, and the spectral and autocorrelation functions. We propose two different estimation procedures whose asymptotic properties are studied by Monte Carlo experiments. An application to actual data from hydrology is performed. Results point out that our process outperforms other six non-Gaussian AR(1) models that are well defined in the time series literature.

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AR Models with Stationary Non-Gaussian Real-Valued Marginals

Balakrishna

2021

Non-Gaussian Autoregressive-Type Time Series

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Generalized Laplacian Distributions and Autoregressive Processes

Cited by 7 publications

References 22 publications

Scaled Muth–ARMA Process Applied to Finance Market

Scaled Muth–ARMA Process Applied to Finance Market

A notable Gamma‐Lindley first‐order autoregressive process: An application to hydrological data

AR Models with Stationary Non-Gaussian Real-Valued Marginals

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