• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.3
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• pp.13-24
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• 2017

This paper aims to implement "structural changes detection procedure" in pairs trading algorithm and to show that the proposed approach outperforms the extant pair trading algorithm. Structural changes in pairs trading are defined in terms of changes in cointegrating factors and broken cointegration relationship. These changes are designed to test extant structural changes and unit root test methodologies. The simulation finds that expanding the changes in structure, increasing the mean reverting process of spread, and extending the consecutive days of broken cointegration will increase the performances of the proposed algorithm. Empirical study results are also consistent those of the simulation studies. The proposed algorithm outperforms the extant algorithm relative to risk and return given that the cumulative profit/loss has a significant upward-slope with minimal variance.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.6
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• pp.1610-1618
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• 2015

This study is aimed to estimate price interactions between wild caught fish and farmed fish on the Korean seafood market, using multivariate cointegration analysis. Major commercially important four wild caught fish (chub mackerel, hairtail, yellow croaker and spanish mackerel) and two farmed fish(oliver flounder and black rockfish) are selected as analytical target fish species. Between 2000 and 2013, monthly production and price data are used in this study. The results of cointegration test showed that there would be a long-term equilibrium relationship among 4 wild caught fish and 2 farmed fish. However, the results of exclusion test indicated that farmed fish might not contribute significantly to the long-run relationship, suggesting that farmed fish might be only a weak substitute for wild caught fish, but no significant interaction could be found.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.4
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• pp.502-510
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• 2014

This study aimed to analyze the relationship between catch amount and market price of sandfish for improvement of fishing revenues and effective fisheries management. By estimating the sandfish price function by fishery, the study tried to investigate changes of prices by catch amount as well as changes of fishing revenues by catch amount and price. Results showed that time series data on catch and price were estimated to be non-stationary from unit root tests, but long-term equilibrium relations between catch amount and price were found from a cointegration analysis. Results of regression analyses indicated that the catch amount would have negative impacts on prices of sandfish in both coastal gillnet and danish seine fisheries.

• East Asian Economic Review
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• v.19 no.1
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• pp.39-70
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• 2015

This paper examines the effects of foreign direct investment (FDI) on South Asian economies' output growth, utilizing recent panel cointegration testing and estimation techniques. Annual panel data on eight SAARC (South Asian Association for Regional Cooperation) member countries' macroeconomic variables over the period 1960- 2013 are employed in empirical analysis. Using various heterogeneous panel cointegration and panel causality tests, a bi-directional relationship between FDI and growth is found. We find evidence for both FDI-led growth and growth-induced FDI hypotheses for the South Asian economies over the sample period. Individual member countries exhibit heterogeneity in terms of the direction or existence of causality subject to their idiosyncratic economic conditions. Among various regressors, FDI, financial development, human capital, and government consumption show the most significant positive effects on output growth. As determinants of FDI, GDP, financial development, human capital, and government consumption are found significant in the region. The bi-directional causality between FDI and growth is found robust to the inclusion of other control variables and using different estimation techniques.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.2
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• pp.5-11
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• 2016

This study investigates the impact of Malaysia's capital market and other key determinants on Economic Growth from the period of 1988 to 2012. The key determinants studied are foreign direct investment and real interest rate. This study also examines the long run and short run relationship between the economic growth and capital market, foreign direct investment, and real interest rate by using bound testing cointegration of Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM) version of ARDL model. The empirical results of the study suggest that there is long- run cointegration among the capital market, foreign direct investment, real Interest rate and economic growth. The result also suggests that capital market and real interest rate have positive impact on economic growth in the short run and long run. Foreign direct investment does not show positive impact on economic growth in the short run but it does in the long run.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.3
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• pp.128-136
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• 2011

In order to investigate the relationship between the prices of wood chips and pulps, regression analysis and cointegration test were conducted. Test results indicated that pulp producers adjusted pulp price in response to the change in wood-chip price and there were a long-run relationship between these prices. This implied that by raising the selling price of pulp, pulp producers avoid profit loss incurred by the increase in the wood-chip price. The existence of cointegration between wood chips and pulp prices implied that pulp producers were competing when they set the selling price of pulp.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.719-726
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• 2008

In this paper we consider a joint test for seasonal cointegrating(CI) ranks that enables us to simultaneously model cointegrated structures across seasonal unit roots in seasonal cointegration. A CI rank test for a single seasonal unit root is constructed and extended to a joint test for multiple seasonal unit roots. Their asymptotic distributions and selected critical values for the joint test are obtained. Through a small Monte Carlo simulation study, we evaluate performances of the tests.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.797-805
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• 2005

Multivariate unit root tests for the VAR(p) model have been commonly used in time series analysis. Several unit root tests were developed and recently Shin(2004) suggested a cointegration test based on weighted symmetric estimator. In this paper, we suggest a multivariate unit root test statistic based on the weighted symmetric estimator. Using a small simulation study, we compare the powers of the new test statistic with the statistics suggested in Shin(2004) and Fuller(1996).

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.501-522
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• 2007

We propose an estimation procedure that can be used for detecting structural changes in the seasonal cointegrated vector autoregressive model. The asymptotic properties of the estimates and the test statistics for the parameter change are provided. A simulation example is presented to illustrate this method and its concept.