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Type :article
Subject :HF5601 Accounting
Main Author :Phoong, Seuk Wai
Additional Authors :Phoong, Seuk Yen
Phoong, Kok Hau
Title :Analysis of structural changes in financial datasets using the breakpoint test and the markov switching model
Place of Production :Tanjong Malim
Publisher :Fakulti Sains dan Matematik
Year of Publication :2019
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Full Text :Login required to access this item.

Abstract : Universiti Pendidikan Sultan Idris
The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non?stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short?to?medium time horizons. Some analysts have indicated that the difficulty in forecasting the crude oil price is due to the fact that economic models cannot consistently show evidence of a strong connection between commodities and economic fundamentals, and, as a result, regarded the idea that economic fundamentals help predict price values as random luck. This study aimed to overcome the limitations of the economic models through the detection of structural changes as well as breaks in the data, using a breakpoint test. The Markov switching model is used to address the price patterns that led to a different market state. The results show that there are several changes as well as breaks in the estimated model. Moreover, there is an asymmetric correlation between the crude oil price and the GDP.

References

1. Fan, J.; Yao, Q. Nonlinear Time Series: Nonparametric and Parametric Methods; Springer: Berlin, Germany, 2005.

2. Phoong, S.W.; Phoong, S.Y.; Moghavvemi, S.; Phoong, K.H. Multiple Breakpoint Test on Crude Oil Price. Found. Manag. 2019, 11, 187–196.

3. Hamilton, J.D. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica 1989, 57, 357–384.

4. Krolzig, H.M. Markov?Switching Vector Autoregression; Springer: Berlin, Germany, 1997.

5. Shaari, M.S.; Hussain, N.E.; Abdullah, H. The effects of oil price shocks and exchange rate volatility on inflation: Evidence from Malaysia. Int. Bus. Res. 2012, 5, 106–112.

6. Siddiqui, M.A.; Butt, S.I.; Gilani, O.; Jamil, M.; Maqsood, A.; Zhang, F. Optimizing Availability of a Framework in Series Configuration Utilizing Markov Model and Monte Carlo Simulation Techniques. Symmetry 2017, 9, 96.

7. Basnet, H.C.; Upadhyaya, K.P. Impact of oil price shocks on output, inflation and the real exchange rate: Evidence from selected ASEAN countries. Appl. Econ. 2015, 47, 3078–3091.

8. David, S.A.; Inácio, C.M.C., Jr.; Tenreiro Machado, J.A. Ethanol Prices and Agricultural Commodities: An Investigation of Their Relationship. Mathematics 2019, 7, 774.

9. Alghalith, M. The interaction between food prices and oil prices. Energy Econ. 2010, 32, 1520–1522.

10. Nazlioglu, S.; Soytas, U. World oil prices and agricultural commodity prices: Evidence from an emerging market. Energy Econ. 2011, 33, 488–496.

11. Nazlioglu, S. World oil and agricultural commodity prices: Evidence from nonlinear causality. Energy Policy 2011, 39, 2935–2943.

12. Rafiq, S.; Salim, R.; Bloch, H. Impact of crude oil price volatility on economic activities: An empirical investigation in the Thai economy. Resour. Policy 2009, 34, 121–132.

13. Zhou, Z.; Jin, Q.; Peng, J.; Xiao, H.; Wu, S. Further Study of the DEA?Based Framework for Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models. Mathematics 2019, 7, 827.

14. Quandt, R.E. Estimation of the parameters of a linear regression system obeying two separate regime. J. Am. Stat. Assoc. 1958, 53, 873–880.

15. Quandt, R.E. A new approach to estimating switching regression. J. Am. Stat. Assoc. 1972, 67, 306–317.

16. Goldfeld, S.M.; Quandt, R.E.A. Markov model for switching regressions. J. Econom. 1973, 1, 3–16.

17. Cosslett, S.R.; Lee, L.F. Serial correlation in the latent discrete variable models. J. Econom. 1985, 27, 79–97.

18. Kim, C.J. Dynamic linear models with Markov?switching. J. Econom. 1994, 60, 1–22.

19. Phoong, S.W.; Ismail, M.T.; Sek, S.K. Linear Vector Error Correction Model versus Markov Switching Vector Error Correction Model to Investigate Stock Market Behaviour. Asian Acad. Manag. J. Account. Financ. 2014, 10, 133–149.

20. Ardia, D.; Bluteau, K.; Boudt, K.; Leopoldo, C. Forecasting risk with Markov?switching GARCH models:A large?scale performance study. Int. J. Forecast. 2018, 34, 733–747.

21. Caporale, G.; Zekokh, T. Modelling volatility of cryptocurrencies using Markov?Switching GARCH models. Res. Int. Bus. Financ. 2019, 48, 143–155.

22. Berument, M.; Ceylan, N.; Dogan, N. The Impact of Oil Price Shocks on the Economic Growth of Selected MENA Countries. Energy J. 2010, 31, 149–176.

23. Arezki, R.; Jakab, Z.; Laxton, D.; Matsumoto, A.; Nurbekyan, A.; Wang, H.; Yao, J. Oil Prices and the Global Economy. Int. Monet. Fund 2017, 17, 1?30.  

24. Shahbaz, M.; Lean, H.H. Does financial development increase energy consumption? The role of industrialization and urbanization in Tunisia. Energy Policy 2012, 40, 473–479.

25. Gómez, M.; Ciarreta, A.; Zarraga, A. Linear and nonlinear causality between energy consumption and economic growth: The case of Mexico 1965–2014. Energies 2018, 11, 784.

26. Gómez, M.; Rodríguez, J.C. Energy Consumption and Financial Development in NAFTACountries, 1971– 2015. Appl. Sci. 2019, 9, 302.

27. De Martino, I. Decaying Dark Energy in Light of the Latest Cpsmological Dataset. Symmetry 2018, 10, 372.

28. Farah, P.D. Five Years of China WTO Membership: EU and US Perceptives about China’s Compliance with Transparency Commitments and the Transitional Review Mechanism. Leg. Issuses Econ. Integr. 2006, 33, 263–304.

29. Neftci, S.N. Are economic time series asymmetric over the business cycle? J. Political Econ. 1984, 92, 306– 328.

30. Brunner, A.D. Conditional symmetries in real GNP: A semi nonparametric approach. J. Bus. Econ. Stat. 1992, 10, 65–72.

31. Laredic, S.; Mignon, V. The impact of oil prices on GDP in European countries: An empirical investigation based on asymmetric cointegration. Energy Policy 2006, 34, 3910–3915.

32. Gadea, M.D.; Gómez?Loscos, A.; Montañés, A. Oil Price and Economic Growth: A Long Story? Econometrics 2016, 4, 41.

33. Benhmad, F. Dynamic cyclical comovements betweem oil prices and US GDP: A wavelet perspective. Energy Policy 2013, 57, 141–151.

34. Rafiq, S.; Bloch, H. Explaining commodity prices through asymmetric oil shocks: Evidence from nonlinear models. Resour. Policy 2016, 50, 34–48.

35. Miao, D.W.C.; Wu, C.C.; Su, Y.K. Regime?switching in volatility and correlation structure using range? based models with Markov?switching. Econ. Model. 2013, 31, 87–93.

36. Balcilar, M.; Eyden, R.; Uwilingiye, J.; Gupta, R. The Impact of Oil Price on South African GDP Growth: A Bayesian Markov Switching VAR Analysis. Afr. Dev. Rev. 2017, 29, 319–336.

37. Ayodeji, I.O. A Three?State Markov?Modulated Switching Model for Exchange Rates. J. Appl. Math. 2016, , 5061749.

 


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