Rarity of Joint Probability Between Interest and Inflation Rates in the 1998 Economic Crisis in Indonesia and Their Comparison Over Three Time Periods
DOI:
https://doi.org/10.15642/mantik.2022.8.1.10-17Keywords:
Copula, Economic crisis, Inflation rate, Interest rate, Joint return period, UncertaintyAbstract
After more than twenty years, there has been no economic crisis as severe as 1998 based on inflation and interest rates. It is interesting to compare the conditions before and after the 1998 crisis and the economic conditions in the last decade in Indonesia. Therefore, this study aims to analyze the relationship between inflation and interest rates using a copula-based joint distribution. The joint return period of the 1998 economic crisis is estimated from this joint distribution. The results showed that the Gumbel copula is the most suitable bivariate copula to construct a joint distribution between inflation and interest rates in 1990-2019, with an upper tail dependency of 0.6224. Moreover, the joint return period between inflation and interest rates more severe than 1998 is 389 years with a 95% confidence interval of [47, ∞] years. This result is uncertain because many factors affect inflation and interest rates. The inflation rate decreased after the 1998 crisis. Meanwhile, in the last decade, the inflation and interest rates were much lower than in the two previous periods.
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References
T. T. H. Tambunan, “The Indonesian Experience with Two Big Economic Crises,” Mod. Econ., vol. 01, no. 03, pp. 156–167, 2010, doi: 10.4236/me.2010.13018.
P. R. Fallon and R. E. B. Lucas, “The impact of financial crises on labor markets, household incomes, and poverty: A review of evidence,” World Bank Res. Obs., vol. 17, no. 1, pp. 21–45, 2002, doi: 10.1093/wbro/17.1.21.
H. Waters, F. Saadah, and M. Pradhan, “The impact of the 1997-98 East Asian economic crisis on health and health care in Indonesia,” Health Policy Plan., vol. 18, no. 2, pp. 172–181, 2003, doi: 10.1093/heapol/czg022.
T. Susmonowati, “Economic Value Added (EVA) Sebagai Pengukuran Kinerja Keuangan Pada Industri Telekomunikasi Suatu Analisis Empirik,” Transparansi J. Ilm. Ilmu Adm., vol. 1, no. 1, pp. 101–119, 2018, doi: 10.31334/trans.v1i1.142.
Y. R. Liu, Y. P. Li, Y. Ma, Q. M. Jia, and Y. Y. Su, “Development of a Bayesian-copula-based frequency analysis method for hydrological risk assessment –
The Naryn River in Central Asia,” J. Hydrol., vol. 580, 2020, doi: 10.1016/j.jhydrol.2019.124349.
M. K. Najib, S. Nurdiati, and A. Sopaheluwakan, “Copula based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone
areas,” Model. Earth Syst. Environ., vol. 8, no. 2, pp. 2817–2826, 2022, doi: 10.1007/s40808-021-01267-5.
M. K. Najib, S. Nurdiati, and A. Sopaheluwakan, “Multivariate Fire Risk Models Using Copula Regression in Kalimantan, Indonesia,” Nat. Hazards, 2022, doi: 10.1007/s11069-022-05346-3.
M. H. Afshar, A. U. Sorman, and M. T. Yilmaz, “Conditional copula-based spatial-temporal drought characteristics analysis-A case study over Turkey,” Water (Switzerland), vol. 8, no. 10, 2016, doi: 10.3390/w8100426.
J. Zscheischler and E. M. Fischer, “The record-breaking compound hot and dry 2018 growing season in Germany,” Weather Clim. Extrem., vol. 29, 2020, doi: 10.1016/j.wace.2020.100270.
M. Sklar, “Fonctions de Répartition àn Dimensions et Leurs Marges,” Publ. L’Institut Stat. L’Université Paris, vol. 8, pp. 229–231, 1959.
C. Schölzel and P. Friederichs, “Multivariate non-normally distributed random variables in climate research - Introduction to the copula approach,” Nonlinear Process. Geophys., vol. 15, no. 5, pp. 761–772, 2008, doi: 10.5194/npg-15-761-2008.
H. Joe, Multivariate models and multivariate dependence concepts. London: CRC Press, 1997.
S. Ly, K.-H. Pho, S. Ly, and W.-K. Wong, “Determining Distribution for the Product of Random Variables by Using Copulas,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3430862.
K. Jin, K. Son, and G. Heo, “Copula-based common cause failure models with Bayesian inferences,” Nucl. Eng. Technol., vol. 53, no. 2, pp. 357–367, 2021, doi: 10.1016/j.net.2020.08.014.
Z. Hao and V. P. Singh, “Review of dependence modeling in hydrology and
water resources,” Prog. Phys. Geogr., vol. 40, no. 4, pp. 549–578, 2016, doi: 10.1177/0309133316632460.
M. K. Najib, S. Nurdiati, and A. Sopaheluwakan, “Quantifying the joint distribution of drought indicators in Borneo fire-prone area,” IOP Conf. Ser. Earth Environ. Sci., vol. 880, no. 1, p. 012002, 2021, doi: 10.1088/1755-1315/880/1/012002.
A. Buike, “Copula Modeling for World’s Biggest Competitors,” Ph.D. Dissertation, Universiteit van Amsterdam, 2018.
H. Joe, “Asymptotic efficiency of the two-stage estimation method for copula-based models,” J. Multivar. Anal., vol. 94, no. 2, pp. 401–419, 2005, doi: 10.1016/j.jmva.2004.06.003.
X. Wei, H. Zhang, V. P. Singh, C. Dang, S. Shao, and Y. Wu, “Coincidence probability of streamflow in water resources area, water receiving area and impacted area: Implications for water supply risk and potential impact of
water transfer,” Hydrol. Res., vol. 51, no. 5, pp. 1120–1135, 2020, doi: 10.2166/nh.2020.106.
M. N. Tahroudi, Y. Ramezani, C. De Michele, and R. Mirabbasi, “Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions,” Hydrol. Res., vol. 51, no. 6, pp. 1332–1348, 2020, doi: 10.2166/nh.2020.036.
I. Pobočíková, Z. Sedliačková, and M. Michalková, “Application of Four Probability Distributions for Wind Speed Modeling,” Procedia Eng., vol. 192, pp. 713–718, 2017, doi: 10.1016/j.proeng.2017.06.123.
M. Farooq, M. Shafique, and M. S. Khattak, “Flood frequency analysis of river swat using Log Pearson type 3, Generalized Extreme Value, Normal, and Gumbel Max distribution methods,” Arab. J. Geosci., vol. 11, no. 9, 2018, doi: 10.1007/s12517-018-3553-z.
S. Baran, P. Szokol, and M. Szabó, “Truncated generalized extreme value distribution-based ensemble model output statistics model for calibration
of wind speed ensemble forecasts,” Environmetrics, vol. 32, no. 6, 2021, doi: 10.1002/env.2678.
T. W. Anderson, “Anderson–Darling Tests of Goodness-of-Fit,” in International Encyclopedia of Statistical Science, Berlin, Heidelberg: Springer, 2011, pp. 52–54.
E. Bouyé, V. Durrleman, A. Nikeghbali, G. Riboulet, and T. Roncalli,
Copulas for Finance - A Reading Guide and Some Applications. https://dx.doi.org/10.2139/ssrn.1032533, 2000.
M. H. Afshar, A. Ü. Şorman, F. Tosunoğlu, B. Bulut, M. T. Yilmaz, and A. Danandeh Mehr, “Climate change impact assessment on mild and extreme drought events using copulas over Ankara, Turkey,” Theor. Appl. Climatol., vol. 141, no. 3–4, pp. 1045–1055, 2020, doi: 10.1007/s00704-020-03257-6.
R. Link, T. B. Wild, A. C. Snyder, M. I. Hejazi, and C. R. Vernon, “100 Years of Data Is Not Enough To Establish Reliable Drought Thresholds,” J. Hydrol. X, vol. 7, 2020, doi: 10.1016/j.hydroa.2020.100052.
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