Modeling Trend in Foreign Currency Exchange Rate and Future Forecasting in Terms of Pakistani Rupees

Author(s)

Muneeb Ahmad , Yousaf Ali Khan , Prof: Chonghui Jiang , Ghazala Akhtar , Muhammad Masood ,

Download Full PDF Pages: 144-155 | Views: 791 | Downloads: 243 | DOI: 10.5281/zenodo.3702079

Volume 9 - January 2020 (01)

Abstract

The demonstrating monetary pattern in outside cash swapping scale is an exceptionally famous strategy in money and future anticipating of remote monetary standards is a useful method for speculators, merchants and exporters in everywhere throughout the world. Pakistan is a truly reasonable nation for the venture open doors for the financial specialists who need to gain long haul benefits from their speculations. There are numerous open doors for interest in horticultural, industry, and cultivating, lodging and the travel industry areas in Pakistan. Be that as it may, for the fascination of remote financial specialists the future anticipating of Pak-Rupee esteem is fundamental and it is likewise essential to discover answers for the swapping scale strength in the future. Our investigation discovers the rupee's conversion scale esteems in the past and furthermore foresee around thirty days future cash trade rates by utilizing the (Auto-backward Integrated Moving Average) ARIMA model. ARIMA model not just give past qualities first contrast stationary bends and furthermore give future determining conversion scale estimations of remote monetary forms. The three most significant monetary standards Dollar, Pound and Euro are used for future anticipating trade paces of Pak-Rupee.

Keywords

Modeling trends, ARIMA Model, Future Forecasting Trends, Pakistani Rupee, Monetary Finance 

References

i.        Ahmed Saeed, R. U. (2012). AN ECONOMETRIC ANALYSIS OF DETERMINANTS OF EXCHANGE RATE IN PAKISTAN. International Journal of Business and Social Science, 194.

ii.      Ahmed, M. (2014). Pakistan's Exchange Rate Policy: An Econometric Investigation. The Pakistan Development Review, 70.

iii.    Athanasopoulos, R. J. (2018). Chapter 8 ARIMA models. Melbourne: (c) OTexts.

iv.     Bracke, T. (2011). EXCHANGE RATE ANCHORING – IS THERE STILL A DE FACTO US DOLLAR STANDARD? Social Science Research Network electronic library http://www.ecb.europa.eu/pub/scientific/wps/date/html/index.en.html, 30.

v.       Brownlee, J. (2019, 09 18). How to Create an ARIMA Model for Time Series Forecasting in Python. Retrieved 01 27, 2020, from machinelearningmastery: https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/

vi.     Chen, J. (2019, 04 13). TECHNICAL ANALYSIS ADVANCED TECHNICAL ANALYSIS CONCEPTS. Retrieved 01 19, 2020, from https://www.investopedia.com/: https://www.investopedia.com/terms/a/autoregressive-integrated-moving-average-arima.asp

vii.   Chkili Walid, A. C. (2011). Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach. Emerging Markets Review, 272–292.

viii. Haider, M. A. (2005). Exchange Rate Behaviour after Recent Float: The Experience of Pakistan . Pakistan Society for Development Economists (PSDE), 28.

ix.     JAMSHED Y. UPPAL, S. R. (2017). Modelling Foreign Exchange Risk in a Managed Float Regime: Challenges for Pakistan. Papers and Proceedings, 179-180.

x.       Janjua, M. A. (2007). Pakistan’s External Trade: Does Exchange Rate Misalignment Matter for Pakistan? The Lahore Journal of Economics, 146.

xi.     Kamruzzaman, J. (2004). ANN-Based Forecasting of ForeignCurrency Exchange Rates . Neural Information Processing - Letters and Reviews, 57.

xii.   Muhammad Yasir, M. Y. (2019). An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System. Applied Sciences, 12.

xiii. Nau, R. (2019, 06 02). Notes on nonseasonal ARIMA models. Retrieved 01 20, 2019, from http://people.duke.edu/: https://people.duke.edu/~rnau/411arim.htm

xiv. Prabhakaran, S. (2019, 02 18). ARIMA Model – Complete Guide to Time Series Forecasting in Python. Retrieved 01 27, 2020, from Machine Learning Plus: https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/

xv.   QAYYUM, H. H. (2014). Estimation of Keynesian Exchange Rate Model of Pakistan by Considering Critical Events and Multiple Co-integrating Vectors. Munich Personal Repec Archive, 32.

xvi. QAYYUM, M. A. (2007). EXCHANGE RATE DETERMINATION IN PAKISTAN: EVIDENCE BASED ON PURCHASING POWER PARITY THEORY. Munich Personal RePEc Archive, 190.

xvii.         Raza, S. A. (2017). Determinants of Exchange Rate in Pakistan: Revisited with Structural Break Testing. Global Business Review, 21.

xviii.       Rehman, S. K. (2014). Analysis of Exchange Rate Fluctuations: A Study of PKR VS USD. Journal of Managerial Sciences, 59.

xix. Yasir Kamal, H.-U.-H. (2012). Modeling the exchange rate volatility, using generalized autoregressive conditionally heteroscedastic (GARCH) type models: Evidence from Pakistan. African Journal of Business Management, 2838.

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