ESTIMATING THE ARCH PARAMETERS BY SOLVING LINEAR EQUATIONS

By Arup Bose, Kanchan Mukherjee. 2003

63 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8a8d8767f3d02b1d06fb2cabddb85dd926d2571a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Piotr Fryzlewicz
2. Theofanis Sapatinas
3. S. Rao
4. Fukang Zhu
5. Dimitris Nicolas Politis
6. Lingxue Pan
7. Esther Ruiz
8. D. Pena

In Global and Planetary Change

1. Lingxue Pan
2. Esther Ruiz
3. D. Pena
4. Dimitris Nicolas Politis
5. Theofanis Sapatinas
6. Piotr Fryzlewicz
7. Fukang Zhu
8. S. Rao

In Statistics and Probability

1. Lingxue Pan
2. S. Rao
3. Esther Ruiz
4. Fukang Zhu
5. Dimitris Nicolas Politis
6. D. Pena
7. Theofanis Sapatinas
8. Piotr Fryzlewicz

Citations in the corpus

• Effects of outliers on the identification and estimation of GARCH models (2007, methodology): “Bose and Mukherjee (2003) propose to estimate the parameters of ARCH(p) models using the GLS estimator which is computationally simple while having asymptotic efficiency equivalent to the ML estimator.”
• GARCH Models (2010, unknown): “The FGLS method was developed, in the ARCH case, by Bose and Mukherjee (2003) (see also Gouriéroux, 1997). The convexity results used for the study of the constrained estimator can be found, for instance, in Moulin and Fogelman-Soulié (1979).”
• Estimation and testing for a Poisson autoregressive model (2011, methodology): “The structure of model (1) looks like that of autoregressive conditional heteroscedastic (ARCH) model proposed by Engle (1982), but there are essential differences between them, so tools developed for ARCH models cannot be used directly to model (1).ThereisvastliteratureonstatisticalinferenceforARCH”
• Normalized least-squares estimation in time-varying ARCH models (2008, methodology): “In the stationary ARCH framework, Giraitis and Robinson (2001), Bose and Mukherjee (2003), Horváth and Liese (2004) and Ling (2007) have considered the weighted least-squares estimator for different weight functions.”
• Bootstrap prediction intervals for linear, nonlinear, and nonparametric autoregressions (2016, methodology): “Instead, Bose and Mukherjee(2003) proposed a linear estimator of the ARCH parameter by solving two sets of linear equations.”