Asymptotic properties of Maximum Likelihood Estimator of the Mixture Autoregressive model with Applications to Financial Risk

M.I Akinyemi; Georgi N. Boshnakov

Asymptotic properties of Maximum Likelihood Estimator of the Mixture Autoregressive model with Applications to Financial Risk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Mixture autoregressive models provide a flexible framework for modelling time series. These
models capture conditional heterogeneity, multi-modality, skewness, kurtosis and heavy tails using
only standard distributions as building blocks. We show that the maximum likelihood estimator
(MLE) of this class of models is consistent and asymptotically normal. We also give applications to
estimation of financial risk.

Original language	English
Title of host publication	In JSM proceedings, Section on Risk Analysis. Alexandria, VA: American Statistical Association
Pages	64-78
Number of pages	15
Publication status	Published - 2015
Event	JSM 2015 - Seattle, United States Duration: 8 Aug 2015 → 13 Aug 2015

Conference

Conference	JSM 2015
Country/Territory	United States
City	Seattle
Period	8/08/15 → 13/08/15

Cite this

@inproceedings{b183ecc867744c34b0dfb94f03982de0,

title = "Asymptotic properties of Maximum Likelihood Estimator of the Mixture Autoregressive model with Applications to Financial Risk",

abstract = "Mixture autoregressive models provide a flexible framework for modelling time series. Thesemodels capture conditional heterogeneity, multi-modality, skewness, kurtosis and heavy tails usingonly standard distributions as building blocks. We show that the maximum likelihood estimator(MLE) of this class of models is consistent and asymptotically normal. We also give applications toestimation of financial risk.",

author = "M.I Akinyemi and Boshnakov, {Georgi N.}",

year = "2015",

language = "English",

pages = "64--78",

booktitle = "In JSM proceedings, Section on Risk Analysis. Alexandria, VA: American Statistical Association",

note = "JSM 2015 ; Conference date: 08-08-2015 Through 13-08-2015",

}

Asymptotic properties of Maximum Likelihood Estimator of the Mixture Autoregressive model with Applications to Financial Risk. / Akinyemi, M.I; Boshnakov, Georgi N.
In JSM proceedings, Section on Risk Analysis. Alexandria, VA: American Statistical Association. 2015. p. 64-78.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Asymptotic properties of Maximum Likelihood Estimator of the Mixture Autoregressive model with Applications to Financial Risk

AU - Akinyemi, M.I

AU - Boshnakov, Georgi N.

PY - 2015

Y1 - 2015

N2 - Mixture autoregressive models provide a flexible framework for modelling time series. Thesemodels capture conditional heterogeneity, multi-modality, skewness, kurtosis and heavy tails usingonly standard distributions as building blocks. We show that the maximum likelihood estimator(MLE) of this class of models is consistent and asymptotically normal. We also give applications toestimation of financial risk.

AB - Mixture autoregressive models provide a flexible framework for modelling time series. Thesemodels capture conditional heterogeneity, multi-modality, skewness, kurtosis and heavy tails usingonly standard distributions as building blocks. We show that the maximum likelihood estimator(MLE) of this class of models is consistent and asymptotically normal. We also give applications toestimation of financial risk.

M3 - Conference contribution

SP - 64

EP - 78

BT - In JSM proceedings, Section on Risk Analysis. Alexandria, VA: American Statistical Association

T2 - JSM 2015

Y2 - 8 August 2015 through 13 August 2015

ER -

Asymptotic properties of Maximum Likelihood Estimator of the Mixture Autoregressive model with Applications to Financial Risk

Abstract

Conference

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Cite this