LINEAR UNIVARIATE GARCH(P,Q) AND PINK NOISE

Graduate #56
Discipline: Mathematics and Statistics
Subcategory: Mathematics and Statistics
Session: 4
Room: Park Tower 8219

Fatemeh Norouzi - Morgan State University

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In this research, we aim to use the Univariate Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model which have been used in forecasting the conditional volatility of time series. We investigate the ability of the linear Univariate GARCH(p,q) to reproduce power law statistics and detect whether it has the pink noise out of power spectral density function. For this investigation, we are exploring derivations of stochastic delay differential equations from linear Univariate GARCH(p,q) process. By finding the similar and general class of stochastic delay differential equations, we will verify the probability density function (PDF) by using the Fokker Planck equation. This PDF will absolutely help us in defining the noise which is shown in power spectral density function.

Funder Acknowledgement(s): 2020 ERN Conference in STEM Morgan State University

Faculty Advisor: Gaston N'Guerekata, Gaston.N'Guerekata@morgan.edu

Role: In this research, we aim to use the Univariate Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model which have been used in forecasting the conditional volatility of time series. We investigate the ability of the linear Univariate GARCH(p,q) to reproduce power law statistics and detect whether it has the pink noise out of power spectral density function. For this investigation, we are exploring derivations of stochastic delay differential equations from linear Univariate GARCH(p,q) process. By finding the similar and general class of stochastic delay differential equations, we will verify the probability density function (PDF) by using the Fokker Planck equation. This PDF will absolutely help us in defining the noise which is shown in power spectral density function.