Frank Scrimgeour

Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, The Philippines and Thailand

A number of industrialized and developing countries agreed to the terms of the Kyoto protocol to conserve energy and reduce emissions. The close relationship between energy consumption and real GDP growth suggests that energy conservation policies are likely to affect real GDP growth. In this paper, the possible impact of energy conservation policies on the New Zealand economy is examined and compared with Australia and several Asian economies. Causality between energy consumption and GDP in New Zealand is investigated as is the causal relationship between GDP and various disaggregate energy data (coal, natural gas, electricity and oil). Based on the energy data used, it appears that energy conservation policies may not have significant impacts on real GDP growth in industrialized countries such as New Zealand and Australia compared to some Asian economies.

Corporate governance practices of small cap companies and their financial performance: an empirical study in New Zealand

The purpose of this paper is to examine the effect of corporate governance practices of small cap companies have had on their financial performances. Previous studies have mainly examined governance practices of larger corporations. This analysis focuses on the governance variables that have been highlighted by the New Zealand Securities Commission (2004) governance principles and guidelines and also on the governance variables that are supported in the literature as providing an appropriate structure for the firm in the environment in which it operates. The data for 71 small cap companies listed in New Zealand over a five-year period from 2001 to 2005 was analysed. Pooled data, OLS and 2SLS regression techniques were used and Tobins Q, ROA and OPINC were used as the dependent variables. The evidence does support the hypothesis that the existence of board independence and audit committee has enhanced firm financial performance, as measured by Tobins Q.

Simulation of alternative dairy farm pollution abatement policies

New Zealand dairy farmers face a tradeoff between profit maximisation and environmental performance. The integrated simulation model presented here enables assessment of the economic and environmental impact of dairy farming with a focus on nitrogen pollution at the catchment level. Our approach extends the value of the DairyNZ Whole Farm Model (Beukes et al., 2005) as an environmental policy tool by building and integrating nitrogen discharge functions for specific soil types and topography using a metamodelling technique. A hybrid model is created by merging the merits of differential evolution and non-linear optimisation to expedite policy simulations, in which farm profits and nitrogen discharges obtained from the differential evolution optimisation process are assembled to form a profit-pollution frontier. This frontier is then subject to constrained optimisation based on non-linear optimisation in order to predict producer responses to alternative pollution control policies. We apply this framework to derive marginal abatement costs for heterogeneous farm types and find that abatement costs for intensive farms are lower than for moderate and extensive farming systems. We further conclude that abatement can be achieved more cheaply using a compulsory standard or threshold tax than using a standard emissions tax.

Economic modelling for trout management: an introduction and case study

Abstract Trout stocks in Lake Taupo, New Zealand are managed by the Department of Conservation to sustain the fishery and enhance angler opportunity. Efficient management requires funds. These funds are generated from license sales. The revenue from license sales is determined by demand for the licenses and their price. This study reports estimates of the price elasticity of demand for trout licenses. These estimates show how license sales, revenue, royalties and funds for stock enhancement change with price changes and different elasticity estimates.

Long memory volatility in Asian stock markets

Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory. Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts. Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management. Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks. Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

The dynamics of price discovery for cross-listed stocks evidence from US and Chinese markets

Abstract Purpose: This study examines how, and to what extent the trading of the cross-listed China-backed ADRs on the New York Stock Exchange (NYSE) contributes to the information flow and price discovery for the corresponding cross-listed stocks on the Shanghai Stock exchange (SSE). Design/methodology/approach: The study utilizes the information share, Granger causality test, Vector error correction model, Permanent–Temporary Gonzalo–Granger (PT/GG) method and Bivariate DCC-EGARCH model to examine the price discovery dynamics across the cross-listed stocks. Findings: The Granger causality tests show that there is two-way transmission on feedback between the Chinese and US markets. The effects from NYSE to SSE are larger than the other way round. The Bivariate DCC-EGARCH model test results indicate the volatility spill over from NYSE is larger from the SSE. Practical implications: Results suggest that in contrast to previous studies that showed very little contribution to price discovery by Chinese ADRs on the NYSE, the present study indicates that the contribution to price-discovery of Chinese ADRs on NYSE has increased relative to the past, suggesting the importance of changing time frames and economic situations. Originality/value: The study differentiates between long-term and short-term price discovery effects and finds that home country bias persists in the long term and in the short term the information from the Cross-listed China-backed ADRs on the New York Stock Exchange (NYSE) affects price discovery for SSE stocks.

Bank lending to agriculture in a small developing country

This paper explores bank lending to agriculture in Fiji, a small developing country. It examines bank lending by bank type—commercial bank lending and lending by the Fiji Development Bank ( fdb), a non-commercial bank. The findings reveal the importance of both banking types as well as a significant drop in lending since 1993, despite the need to fund the agricultural sector to boost food production activities. Interest rates, government spending and food production strongly determine fdblending to agriculture. The availability of arable land has a negative and significant effect on fdblending to agriculture. The results show commercial banks are performing a slightly different role in the agricultural sector than the Fiji Development Bank; they appear to be unresponsive to economic variables that the fdbis responsive to.

Estimating Long Memory Volatility Using High-Frequency Data of Asian Stock Markets

This article analyzed the presence of long memory in volatility in 5 Asian equity indices namely SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using 5 minutes intraday return series ranging from 05-jan-2015 to 06-Aug-2015. The study employed ARFIMA-FIGARCH model and ARFIMA-APARCH model and compared them with GARCH (1,1) model and APARACH(1,1) in terms of in-sample forecast accuracy. The results confirmed the presence of long memory in both the return and volatility series for all the five markets under study. Among the group, CNIA and STI showed most persistence in both the return and conditional volatility. In terms of forecast measures, the long-memory GARCH models were found to be performing better compared to the short-memory GARCH models.

Using Microsimulation to Maximise Scarce Survey Data: Applications for Catchment Scale Modelling and Policy Analysis

Microsimulation can be used to extend the use of scarce survey resources by creating simulated populations whose characteristics are close to those of the real population. The technique involves merging detailed survey observations with variables from more extensive data sets in order to create a simulated population. We illustrate how microsimulated data enable analysis of the economic and environmental impact of different policies on a catchment for which detailed farm level data was unavailable. Use of microsimulation for agri-environmental policy analysis is applicable to diverse problems from simulation of nitrogen trading to modelling of agent response to policy shocks. Scale flexibility is easily implemented since data can be aggregated or disaggregated to the preferred scale. Simulated catchment data allows better understanding of the effects of policies on different types of farm and should be extremely valuable to organisations that want to minimise the economic impact of environmental policies.