Karachi inter-bank offered rate (kibor) forecasting: Box-jenkins (arima) testing approach

DSpace Repository

Show simple item record

dc.contributor.author Ahmed, Rizwan Raheem
dc.contributor.author Vveinhardt, Jolita
dc.contributor.author Ahmad, Nawaz
dc.contributor.author Štreimikienė, Dalia
dc.contributor.other Ekonomická fakulta cs
dc.date.accessioned 2017-06-26
dc.date.available 2017-06-26
dc.date.issued 2017-06-15
dc.identifier.issn 1212-3609
dc.identifier.uri https://dspace.tul.cz/handle/15240/20851
dc.description.abstract The aim of this paper is to find out the forecasting model that is the one, which gives the best output of forecasting. So that policy makers can be benefited from this research. Thus, this research will also evaluate the performance of ARMA, and Box-Jenkins (ARIMA) forecasting models for KIBOR in case of Pakistan. Karachi Inter Bank Offer Rates (KIBOR) is the average interest rate at which banks want to lend money to other banks. KIBOR as a benchmark, to encourage transparency, to promote consistency in market based pricing and to improve management of the market risk undertaken by banks. Researchers have used 6-month rates of KIBOR; data is of 4 years from 2012 to 2015. Therefore, keeping in view of the importance of KIBOR, the objective of this research is to forecast, Karachi Inter Bank Offer Rates (KIBOR) using time series autoregressive moving average (ARMA), Box-Jenkins (ARIMA) model. The study is significant at 1%, the forecasting of rates shows that the rates are very close to the actual one and it further concluded that the applied model Box-Jenkins (ARIMA) is perfect for the forecasting. The results of AIC revealed that there is no evidence of autocorrelation and there is no sample error and the model is useful and robust. It is finally concluded that the forecasting of KIBOR rates by ARIMA (Box-Jenkins) model is very helpful for policy makers. The results extracted from this model are reliable for making any forecasting and also beneficial for government functionaries, financial experts and policy makers of financial institutions in order to device their future strategies. en
dc.format text
dc.format.extent 188-198 stran cs
dc.language.iso en
dc.publisher Technical university of Liberec, Czech Republic en
dc.publisher Technická Univerzita v Liberci cs
dc.relation.ispartof Ekonomie a Management cs
dc.relation.ispartof Economics and Management en
dc.relation.isbasedon Abledu, G. K., & Agbodah, K. (2012). Stochastic Forecasting and Modeling of Volatility of Oil Prices in Ghana using ARIMA Time series model. European Journal of Business and Management, 4(16), 122-131.
dc.relation.isbasedon Adhikari, R., & Agrawal, R. K. (2014). A combination of artificial neural network and random walk models for financial time series forecasting. Neural Computing and Applications, 24(6), 1441-1449. doi:10.1007/s00521-013-1386-y.
dc.relation.isbasedon Ahoniemi, K. (2006). Modeling and forecasting implied volatility - an econometric analysis of the VIX. Helsinki Center of Economic Research [Discussion Paper 129].
dc.relation.isbasedon Akaike, H. (1974). A New Look at Statistical Model Identification. IEEE Transactions on Automatic Control, AC-19, 716-723.
dc.relation.isbasedon Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208. doi:10.1080/09603100701604225.
dc.relation.isbasedon Alnaa, S. E., & Ahiakpor, F. (2011). ARIMA (Autoregressive Integrated Moving Average) Approach to Predicting Inflation in Ghana. Journal of Economic and International Finance, 3(5), 328-336.
dc.relation.isbasedon Amos, C. (2010). Time Series Modeling with Applications to South African Inflation Data (Unpublished master thesis). University of Kwazulu Natal.
dc.relation.isbasedon Awogbemi, C. A., & Oluwaseyi, A. (2011). Modeling Volatility in Financial Time Series: Evidence from Nigerian inflation rates. Ozean Journal of Applied Sciences, 4(3), 337-350.
dc.relation.isbasedon Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
dc.relation.isbasedon Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79.
dc.relation.isbasedon Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control (3rd ed.). Prentice-Hall.
dc.relation.isbasedon Bryan, M., & Cecchetti, S. (1993). Measuring Core Inflation [NBER Working Paper Series No. 4303, March].
dc.relation.isbasedon Cecchetti, S. (1995). Inflation Indicators and Inflation Policy. In B. Bernanke & J. Rotemberg (Eds.), NBER Macroeconomic Annual. London: MIT Press.
dc.relation.isbasedon Chatfield, C. (1979). Inverse Autocorrelations. Journal of the Royal Statistical Society, Series A (General), 142(3), 363-377. doi:10.2307/2982488.
dc.relation.isbasedon Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. Power Systems, IEEE Transactions on Power Systems, 18(3), 1014-1020. doi:10.1109/TPWRS.2002.804943.
dc.relation.isbasedon Dua, P., Raje, N., & Sahoo, S. (2004). Interest Rate Modeling and Forecasting in India [Occasional paper no.3]. Centre for Development Economics, Delhi School of Economics.
dc.relation.isbasedon Georgoff, D., & Murdick, R. (1986). Manager's Guide to Forecasting. Harvard Business Review, Vol. 1 (January-February), p. 110.
dc.relation.isbasedon Gómez, V., & Maravall, A. (1998). Automatic Modeling Methods for Univariate Series [Banco de España Working Paper No. 9808].
dc.relation.isbasedon Hannan, E. (1980). The Estimation of the Order of ARMA Process. Annals of Statistics, 8(5), 1071-1081.
dc.relation.isbasedon Igogo, T. (2010). Real Exchange Rate Volatility and International Trade flows in Tanzania. (Unpublished master thesis). University of Dar es Salaam.
dc.relation.isbasedon Irfan, M., Maria, M., & Awais, M. (2010). Modeling Conditional Heteroscedasticity and Forecasting in Short Term Interest Rate of KIBOR. International Journal of Economic Perspectives, 4(4), 635-654.
dc.relation.isbasedon Karanasos, M., & Kim, J. (2003). Moments of the ARMA-EGARCH model. Econometrics Journal, 6(1), 146-166.
dc.relation.isbasedon Makridakis, M., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16(4), 451-476. doi:10.1016/S0169-2070(00)00057-1.
dc.relation.isbasedon McLeod, A. I., & Li, W. K. (1983). Diagnostic checking of ARMA Time series Models using Squared - Residual Autocorrelations. Journal of Time Series Analysis, 4(4), 269-273. doi:10.1111/j.1467-9892.1983.tb00373.x.
dc.relation.isbasedon Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting irish inflation using ARIMA models. Central Bank and Financial Services Authority of Ireland Technical Paper Series, 3, 46.
dc.relation.isbasedon Owusu, F. K. (2010). Time series ARIMA modeling of Inflation in Ghana: (1990-2009). (Unpublished master thesis). Kwame Nkrumah University of Science and Technology, Institute of Distance Education, Kumasi, Ghana.
dc.relation.isbasedon Pankratz, A. (1991). Forecasting with Dynamic Regression Models. New York: John Wiley & Sons.
dc.relation.isbasedon Radha, S., & Thenmozhi, M. (2002). Forecasting short-term interest rates using ARMA, ARMA-GARCH and ARMA-EGARCH models. In 9th Capital Markets Conference Paper. Indian Institute of Capital Markets.
dc.relation.isbasedon Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6(2), 461-464.
dc.relation.isbasedon Sehgal, S., Bijoy, K., & Deisying, F. (2011). Modeling and forecasting debt market yields: evidence from India. Banks and Bank Systems, 6(4), 49-63.
dc.relation.isbasedon Stockton, D., & Glassman, J. (1987). An Evaluation of the Forecast Performance of Alternative Models of Inflation. Review of Economics and Statistics, 69(1), 108-117. doi:10.2307/1937907.
dc.relation.isbasedon Toor, S., & Ali, M. (2013). Forecasting of Deposit Rates and Time Series Analysis [Technical Report, BS Actuarial Sciences and Risk Management]. University of Karachi. Retrieved from https://www.academia.edu/6054561/FORECASTING_OF_DEPOSIT_RATES_AND_TIME_SERIES_ANALYSIS.
dc.relation.isbasedon Weiss, A. A. (1984). ARMA models with ARCH Errors. Journal of Time Series Analysis, 5(2), 129-143. doi:10.1111/j.1467-9892.1984.tb00382.x.
dc.rights CC BY-NC
dc.subject KIBOR en
dc.subject ARMA en
dc.subject Box-Jenkins en
dc.subject ARIMA en
dc.subject forecasting en
dc.subject.classification B23
dc.subject.classification E4
dc.subject.classification E5
dc.subject.classification E47
dc.title Karachi inter-bank offered rate (kibor) forecasting: Box-jenkins (arima) testing approach en
dc.type Article en
dc.publisher.abbreviation TUL
dc.relation.isrefereed true
dc.identifier.doi 10.15240/tul/001/2017-2-014
dc.identifier.eissn 2336-5604
local.relation.volume 20
local.relation.issue 2
local.relation.abbreviation E+M cs
local.relation.abbreviation E&M en
local.faculty Faculty of Economics
local.citation.spage 188
local.citation.epage 198
local.access open
local.fulltext yes


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search

Browse

My Account