Stochastic volatility generated by product autoregressive models

Anvar, P. Muhammed; Balakrishna, N.; Abraham, Bovas

doi:10.1002/sta4.232

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Hsiao, Huang, and Ing (2018) studied about the interval estimation for first‐order AR models with different positive errors and illustrated its use with a blowfly data and a viscosity data. Muhammed Anvar, Balakrishna, and Abraham (2019) proposed the use of a product AR model with generalized gamma distribution to generate stochastic volatility. Han, Braun, and Loeppky (2020) demonstrated the use of a random coefficient exponential tailed mixture minification model and mixture Weibull tailed minification model in modelling the Canadian Fire Weather Index.…”

Section: Introductionmentioning

confidence: 99%

Estimating function method for nonnegative autoregressive models

Prasad

Balakrishna

2023

Statistica Neerlandica

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A stationary sequence of nonnegative random variables generated by autoregressive (AR) models may be used to describe the inter‐arrival times between events in counting processes. Even though, several such models are available in the literature, there is no unified approach to estimate their parameters. In this paper, we propose a class of combined estimating function method to estimate the model parameters of AR models with gamma marginals. The proposed method is compared with other estimation procedures and are illustrated by simulation and data analysis.

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Section: Introductionmentioning

confidence: 99%

Estimating function method for nonnegative autoregressive models

Prasad

Balakrishna

2023

Statistica Neerlandica

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Hierarchical modeling of irregularly spaced financial returns

Anantharaman,

Ravishanker,

Basu

2024

Stat

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Volatility modeling is crucial in finance, especially when dealing with intraday transaction‐level asset returns. The irregular and high‐frequency nature of the data presents unique challenges. While stochastic volatility (SV) models are widely used for understanding patterns in volatility of daily stock returns which constitute regularly spaced time series, new classes of models must be introduced for analyzing volatility in irregularly spaced intraday data. Specifically these models must accommodate the random gaps between successive transactional events. By modeling the gaps using autoregressive conditional duration (ACD) models, we describe a hierarchical irregular SV autoregressive conditional duration (IR‐SV‐ACD) model for estimating and forecasting intertransaction gaps and the volatility of log‐returns. We carry out the analysis in the Bayesian framework via the Hamiltonian Monte Carlo (HMC) algorithm with No‐U‐turn sampler (NUTS) in R using the cmdstanr package. The fits and forecasts are obtained using Monte Carlo averages based on the posterior samples. We illustrate this approach using simulation studies and real data analysis for intraday prices available at microseconds level of health stocks traded on the New York Stock Exchange (NYSE). The log‐returns and gaps are calculated for the stocks and are used for modeling.

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Some Bivariate and Multivariate Models Involving Independent Gamma Distributed Components

Arnold

2021

Methodology and Applications of Statistics

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Stochastic volatility generated by product autoregressive models

Cited by 8 publications

References 29 publications

Estimating function method for nonnegative autoregressive models

Estimating function method for nonnegative autoregressive models

Hierarchical modeling of irregularly spaced financial returns

Some Bivariate and Multivariate Models Involving Independent Gamma Distributed Components

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