Search results

1 – 3 of 3
To view the access options for this content please click here
Article
Publication date: 16 August 2011

Akihiro Fukushima

The purpose of this paper is to propose two hybrid forecasting models which integrate available ones. A hybrid contaminated normal distribution (CND) model accurately…

Abstract

Purpose

The purpose of this paper is to propose two hybrid forecasting models which integrate available ones. A hybrid contaminated normal distribution (CND) model accurately reflects the non‐normal features of monthly S&P 500 index returns, and a hybrid GARCH model captures a serial correlation with respect to volatility. The hybrid GARCH model potentially enables financial institutions to evaluate long‐term investment risks in the S&P 500 index more accurately than current models.

Design/methodology/approach

The probability distribution of an expected investment outcome is generated with a Monte Carlo simulation. A taller peak and fatter tails (kurtosis), which the probability distribution of monthly S&P 500 index returns contains, is produced by integrating a CND model and a bootstrapping model. The serial correlation of volatilities is simulated by applying a GARCH model.

Findings

The hybrid CND model can simulate the non‐normality of monthly S&P 500 index returns, while avoiding the influence of discrete observations. The hybrid GARCH model, by contrast, can simulate the serial correlation of S&P 500 index volatilities, while generating fatter tails. Long‐term investment risks in the S&P 500 index are affected by the serial correlation of volatilities, not the non‐normality of returns.

Research limitations/implications

The hybrid models are applied only to the S&P 500 index. Cross‐sectional correlations among different asset groups are not examined.

Originality/value

The proposed hybrid models are unique because they combine available ones with a decision tree algorithm. In addition, the paper clearly explains the strengths and weaknesses of existing forecasting models.

Details

The Journal of Risk Finance, vol. 12 no. 4
Type: Research Article
ISSN: 1526-5943

Keywords

To view the access options for this content please click here
Article
Publication date: 31 May 2011

Akihiro Fukushima and J. Jeffrey Peirce

This paper seeks to propose a hybrid performance measurement framework integrating available frameworks and mathematical models. The hybrid framework potentially allows

Downloads
1148

Abstract

Purpose

This paper seeks to propose a hybrid performance measurement framework integrating available frameworks and mathematical models. The hybrid framework potentially allows decision makers to move from intuitive decisions to analysis‐based decisions by using a complete hierarchy of objectives, mathematical equations and a simulation of increased capabilities. To illustrate the utility of the proposed framework, this paper aims to apply the framework to a hypothetical decision‐making scenario in a computer manufacturing company.

Design/methodology/approach

In the proposed framework, a developed hierarchy is verified with correlation and regression analyses. Mathematical equations relating performance indicators are defined with a multiple linear regression model. An expected final outcome and uncertainty are evaluated with a Monte Carlo simulation.

Findings

An organization can find consistent performance indicators based on correlation and regression analyses. In addition, based on a forecast final outcome, the organization can make proactive decisions about up‐front investments in its capabilities.

Research limitations/implications

A hierarchy of objectives developed in this paper is not comprehensive. A scenario used for simulating a future outcome is hypothetical.

Originality/value

Although some studies illustrate mathematical equations relating objectives, the studies are limited to parts of a hierarchy and there are few practical directions. This paper proposes mathematical equations that represent vertical relationships among objectives in a hierarchy, while evaluating the importance of a performance measurement system in a big picture. Moreover, this paper explains a decision‐making procedure based on a forecast outcome.

Details

Measuring Business Excellence, vol. 15 no. 2
Type: Research Article
ISSN: 1368-3047

Keywords

To view the access options for this content please click here
Article
Publication date: 7 November 2016

Akihiro Otsuka and Shoji Haruna

This paper aims to estimate electricity demand functions in Japan’s residential sector.

Abstract

Purpose

This paper aims to estimate electricity demand functions in Japan’s residential sector.

Design/methodology/approach

The authors use a partial adjustment model and empirically analyze regional residential electricity demand by using data on 47 Japanese prefectures.

Findings

The results reveal that the price elasticity of residential electricity demand during the analytical period (1990-2010) is remarkably different among prefectures, depending on the magnitude of floor space per household. In addition, this study finds that price elasticity is high compared with income elasticity, implying that residential electricity demand changes with rates. Furthermore, an analysis of factors influencing electricity demand in the residential sector shows that increasing electricity demand growth in each region can be attributable mainly to declining electricity rates and increasing number of households.

Research limitations/implications

These results suggest that monitoring the electricity rates and the number of households is important for forecasting future residential electricity demand at region.

Originality/value

The study considers the impact of the number of households on overall electricity demand and identifies other factors contributing to growth in residential electricity demand. The findings can be used to derive projections for future electricity demand.

Details

International Journal of Energy Sector Management, vol. 10 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

1 – 3 of 3