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…
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
Keywords
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 decision…
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
Keywords
Akihiro Uto and Elizabeth Maly
After the Great East Japan Earthquake (GEJE), the need for disaster case management (DCM) was highlighted through the efforts of the Sendai Bar Association, which investigated the…
Abstract
Purpose
After the Great East Japan Earthquake (GEJE), the need for disaster case management (DCM) was highlighted through the efforts of the Sendai Bar Association, which investigated the situation of survivors. This paper provides an overview of DCM in Japan since the GEJE, including key findings from investigations and legal consultations conducted by the Sendai Bar Association and the first author, who took part in the surveys with survivors in Ishinomaki City, clarifying the large number of homebound survivors and their needs.
Design/methodology/approach
In recent years there has been growing attention to the importance of DCM for supporting life and housing recovery of disaster survivors. Along with the expansion of DCM activities over several decades in Japan, the need for DCM was increasingly recognized after the 2011 GEJE and tsunami, especially for home-based survivors left out of government-provided disaster recovery support programs. As one-on-one advice to support individual recovery needs, the focus of DCM in Japan is legal advice to help survivors effectively navigate support policies.
Findings
Since the GEJE, there has been growing support for DCM in Japan, including from practitioners, scholars, and regional and national governments. However, although DCM can be an effective way to support housing recovery, even 12 years after the GEJE, there are still survivors in need of additional support.
Originality/value
Drawing on a detailed case study and action research of the first author, this paper contributes to the still limited international literature on DCM in Japan.
Details
Keywords
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.