# Statistical estimation for CAPM with long-memory dependence

Tomoyuki Amano, Tsuyoshi Kato, Masanobu Taniguchi

Research output: Contribution to journalArticle

1 Citation (Scopus)

### Abstract

We investigate the Capital Asser Pricing Model (CAPM) with time dimension. By using time series analysis, we discuss the estimation of CAPM when market portfolio and the error process are long-memory process and correlated with each other. We give a sufficient condition for the return of assets in the CAPM to be short memory. In this setting, we propose a two-stage least squares estimator for the regression coefficient and derive the asymptotic distribution. Some numerical studies are given. They show an interesting feature of this model.

Original language English 571034 Advances in Decision Sciences 2012 https://doi.org/10.1155/2012/571034 Published - 2012

### Fingerprint

Statistical Estimation
Long Memory
Pricing
Data storage equipment
Two-stage Least Squares
Long Memory Process
Costs
Market Model
Least Squares Estimator
Time Series Analysis
Regression Coefficient
Asymptotic distribution
Time series analysis
Numerical Study
Model
Sufficient Conditions
Statistical estimation
Long memory

### ASJC Scopus subject areas

• Decision Sciences(all)
• Applied Mathematics
• Computational Mathematics
• Statistics and Probability

### Cite this

Statistical estimation for CAPM with long-memory dependence. / Amano, Tomoyuki; Kato, Tsuyoshi; Taniguchi, Masanobu.

In: Advances in Decision Sciences, Vol. 2012, 571034, 2012.

Research output: Contribution to journalArticle

@article{9c82d16c5740464f8f7f7a81f9f20aec,
title = "Statistical estimation for CAPM with long-memory dependence",
abstract = "We investigate the Capital Asser Pricing Model (CAPM) with time dimension. By using time series analysis, we discuss the estimation of CAPM when market portfolio and the error process are long-memory process and correlated with each other. We give a sufficient condition for the return of assets in the CAPM to be short memory. In this setting, we propose a two-stage least squares estimator for the regression coefficient and derive the asymptotic distribution. Some numerical studies are given. They show an interesting feature of this model.",
author = "Tomoyuki Amano and Tsuyoshi Kato and Masanobu Taniguchi",
year = "2012",
doi = "10.1155/2012/571034",
language = "English",
volume = "2012",
journal = "Advances in Decision Sciences",
issn = "2090-3359",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - Statistical estimation for CAPM with long-memory dependence

AU - Amano, Tomoyuki

AU - Kato, Tsuyoshi

AU - Taniguchi, Masanobu

PY - 2012

Y1 - 2012

N2 - We investigate the Capital Asser Pricing Model (CAPM) with time dimension. By using time series analysis, we discuss the estimation of CAPM when market portfolio and the error process are long-memory process and correlated with each other. We give a sufficient condition for the return of assets in the CAPM to be short memory. In this setting, we propose a two-stage least squares estimator for the regression coefficient and derive the asymptotic distribution. Some numerical studies are given. They show an interesting feature of this model.

AB - We investigate the Capital Asser Pricing Model (CAPM) with time dimension. By using time series analysis, we discuss the estimation of CAPM when market portfolio and the error process are long-memory process and correlated with each other. We give a sufficient condition for the return of assets in the CAPM to be short memory. In this setting, we propose a two-stage least squares estimator for the regression coefficient and derive the asymptotic distribution. Some numerical studies are given. They show an interesting feature of this model.

UR - http://www.scopus.com/inward/record.url?scp=84855612041&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84855612041&partnerID=8YFLogxK

U2 - 10.1155/2012/571034

DO - 10.1155/2012/571034

M3 - Article

AN - SCOPUS:84855612041

VL - 2012

JO - Advances in Decision Sciences

JF - Advances in Decision Sciences

SN - 2090-3359

M1 - 571034

ER -