Version 1
: Received: 25 June 2023 / Approved: 26 June 2023 / Online: 26 June 2023 (04:40:02 CEST)
How to cite:
Samuel, R.T.A.; Chimedza, C.; Sigauke, C. Framework for Simulation Study Involving Volatility Estimation: The GAS Approach. Preprints2023, 2023061735. https://doi.org/10.20944/preprints202306.1735.v1
Samuel, R.T.A.; Chimedza, C.; Sigauke, C. Framework for Simulation Study Involving Volatility Estimation: The GAS Approach. Preprints 2023, 2023061735. https://doi.org/10.20944/preprints202306.1735.v1
Samuel, R.T.A.; Chimedza, C.; Sigauke, C. Framework for Simulation Study Involving Volatility Estimation: The GAS Approach. Preprints2023, 2023061735. https://doi.org/10.20944/preprints202306.1735.v1
APA Style
Samuel, R.T.A., Chimedza, C., & Sigauke, C. (2023). Framework for Simulation Study Involving Volatility Estimation: The GAS Approach. Preprints. https://doi.org/10.20944/preprints202306.1735.v1
Chicago/Turabian Style
Samuel, R.T.A., Charles Chimedza and Caston Sigauke. 2023 "Framework for Simulation Study Involving Volatility Estimation: The GAS Approach" Preprints. https://doi.org/10.20944/preprints202306.1735.v1
Abstract
In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adequately reflects the behaviour of financial time series data, relevant for volatility estimation. This dynamic framework is intended to help interested users on MCS experiments utilising the GAS model for reliable volatility calculations in finance and other areas.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
