Indirect Evidence of the Effect of Informal Economy in the Behavior of the Foreign Currency’s Prices: A Case Study
DOI:
https://doi.org/10.59573/emsj.8(4).2024.18Ключевые слова:
exchange rate, informal economy, log-periodic, multifractal, econophysics, q-GaussianАннотация
The effect of informal economy on the behavior of exchange rate is complex and difficult to evaluate in common economic conditions. But if informal sector is sizeable and a certain foreign currency is used for transactions therein without being converted, the supply and the demand for this currency, the velocity of the money circulation, and several other variables would be impacted, implicating a measurable response in the corresponding FX rates’ behavior, while the other currencies’ prices will be less affected. Consequently, distinguishable dissimilarities regarding dynamics and statistical features are expected for the exchange rates of the currencies traded in the country. By evidencing and analyzing them, we can proceed with a backward analysis to identify the hidden relationships between informal economy and exchange rates. As a case study we analyzed the FX rates of main currencies traded in Albania, considering that specifically the Euro is used at a non-neglected volume as national currency substitute in the informal sector. Recognizing the complexity of the relationship under investigation, we have employed an interdisciplinary approach by incorporating econometrics and econophysics approaches. After examining the multifractal features of each FX series, the non-stationarity measure for distributions of the corresponding rate of returns, and the significance of self-organizing regimes on FX time series, we noticed that the price of the EU currency behaves distinguishably different. We described the specific features observed for the Euro-ALL exchange rates as an indication of their response to the informal economy and partial usage of the Euro in the role of national currency in this sector. Those findings are used also for opinionizing regarding formalization issues and scenarios of replacement of national currency by the Euro. Acknowledging that informality is undoubtedly harmful for the economy, this work is limited to providing an alternative explanation of specific FX rates comportment observed in the country, and to offer a descriptive analysis for similar systems.
Библиографические ссылки
Alberola, E. & Urrutia, C. (2019). Does informality facilitate inflation stability? Monetary and Economic Department April 2019. Bank for International Settlements 2019. https://www.bis.org/publ/work778.pdf
Andrade, J. S., Duarte, A. P., & Duarte, A. (2013). Testing for Nonlinear Adjustment in the Portuguese Target Zone: Is there a Honeymoon Effect? (No. 5305). EcoMod.
Antoniadesa, I.P., Karakatsanis, L.P., & Pavlos, E.G. (2021). Dynamical Characteristics of Global Stock Markets Based on Time Dependent Tsallis Non-Extensive Statistics and Generalized Hurst Exponents. arXiv:2012.06856 [q-fin.ST]
Bank of Albania. (n.d.). https://www.bankofalbania.org/
Betts, C. & Devereux, M. (2000). Exchange rate dynamics in a model of pricing-to-market. Journal of International Economics, 50, 215 –244.
Bila, L., Grech, D., & Podhajska, E. (2016). Methods of Non-Extensive Statistical Physics in Analysis of Price Returns on Polish Stock Market. Acta Physica Polonica, 129, 5.
Borland, L. (2002). Option pricing formulas based on a non-Gaussian stock price model. Phys. Rev. Lett., 89, 098701.
Bothmer, H. C. G. V. (2003). Significance of log-periodic signatures in cumulativenoise. Quantitative Finance, 3(5), 370-375. DOI: 10.1088/1469-7688/3/5/303
Dell’Anno, R. (2023). Measuring the unobservable: estimating informal economy by a structural equation modeling approach. Int Tax Public Finance, 30, 247–277.
Dell’Anno, R., & Schneider, F. (2009). A complex approach to estimate shadow economy: The structural equation modelling. In Faggini M., & Lux T. (Eds.), Coping with the complexity of economics (pp. 111–130). Springer-Verlag New Economic Windows series.
Denaj, A., Prenga, D., & Tahiri, V. (2023, September). General features of the time data series of Covid-19 in Albania. In AIP Conference Proceedings (Vol. 2872, No. 1). AIP Publishing.
Di Matteo, T. (2007). Multi-scaling in finance. Quantitative finance, 7(1), 21-36., doi:10.1080/14697680600969727.
Elgin, C., Kose, M., Ohnsorge, F., & Yu, S. (Eds.). (2021). DP16497 Understanding Informality. CEPR Press Discussion Paper No. 16497. https://cepr.org/publications/dp16497
EuroStat. (n.d.). https://ec.Europa.eu/Eurostat/statistics-explained
Feder, J. (1988). Fractals. New York: Plenum Press. ISBN 978-0-306-42851-7.
Geraskin, P., & Fantazzini, D. (2013). Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask. The European Journal of Finance, 19(5), 366-391.
Gluzman, S., & Sornette, D. (2002). Log-periodic route to fractal functions. Physical Review E, 65(3), 036142. doi:10.1103/PhysRevE.65.036142
Gray, M., & Turnovsky, S. (1979). The Stability of Exchange Rate Dynamics under Perfect Myopic Foresight. International Economic Review, 20(3), 643–660. DOI: 10.2307/2526263
Hamilton, A. (2018). Understanding Exchange Rates and Why They Are Important. Reserve Bank of Australia Bulletin – December 2018.
Hurst, H.E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770. DOI: 10.1061/TACEAT.0006518
Ihlen, E.A.F. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Front. Physiol., 3, 141. https://doi.org/10.3389/fphys.2012.00141
Jiang, Z. Q., Xie, W. J., Zhou, W. X., & Sornette, D. (2019). Multifractal analysis of financial markets: a review. Reports on Progress in Physics, 82(12), 125901.
Johansen, A. & Sornette, D. (1998). Evidence of Discrete Scale Invariance in DLA and Time-to-Failure by Canonical Averaging. International Journal of Modern Physics C, 9(3), 433-447.
Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, 316(1-4), 87-114.
Khan, M. S., & Montiel, P. J. (1987). Real exchange rate dynamics in a small, primary-exporting country. IMF Econ Rev, 34(4), 681-710.
Krugman, P. R. (1991). Target zones and exchange rate dynamics. The Quarterly Journal of Economics, 106(3), 669-682. https://doi.org/10.2307/2937922
Kushta, E., Vuka, E., Prenga, D., & Dika, I. (2024). Investigating Statistical Features of the FX Bid Ask Series in a Small Economy with a Sizeable Informal Economy. Journal of Human, Earth, and Future, 5(1), 19-33.
Kuzmin, A. (2022). Mathematical Exchange Rates Modelling: Equilibrium and Nonequilibrium Dynamics. Mathematics, 10, 4672. https://doi.org/10.3390/math10244672
Lera, S.C., & Sornette, D. (2015). Currency target-zone modeling: An interplay between physics and economics. Physical Review: E, Statistical, nonlinear, and Soft Matter Physics, 92(6), 062828.
Mantegna, R. & Stanley, H. (2007). An introduction to econophysics: correlations and complexity in finance. Cambridge University Press New York, NY, USA.
Mantegna, R., & S Stanley, E. (2000). An Introduction to Econophysics. Cambridge University Press, UK.
McCauley, J. L. (2004). Dynamics of markets: econophysics and finance. Cambridge University Press.
Miller, M., & Weller, P. (2013). Currency Bubbles Which Affect Fundamentals: A Qualitative Treatment. The Economic Journal, 100(400), 170-179.
Minister of Finance. (n.d.). https://financa.gov.al
Morales, R., Di Matteo, T., Gramatica, R., & Aste, T. (2012). Dynamical generalized Hurst expo-nent as a tool to monitor unstable periods in financial time series. Physica A: Statistical Mechanics and Its Applications, 391(11), 3180-3189. DOI: 10.[8]6/j.physa.2012.01.004.50
Obstfeld, M., & Stockman, A. C. (1985). Chapter 18 Exchange-rate dynamics. Handbook of International Economics, 917–977. DOI: 10.1016/s1573-4404(85)02009-3
Pavlos, G. P., Karakatsanis, L. P., Xenakis, M. N., Pavlos, E. G., Iliopoulos, A. C., & Sarafopoulos, D. V. (2014). Universality of non-extensive Tsallis statistics and timeseries analysis: Theory and applications. Physica A: Statistical Mechanics and Its Applications, 395, 58–95. http://doi.org/10.[8] 6/j.physa.2013.08.026.
Pavlos, G.P. (2012). Complexity in Theory and Practice: Toward the Unification of Non-equilibrium Physical Processes. Chaotic Modeling and Simulation (CMSIM), 1, 123-145.
Prenga, D., & Ifti, M. (2016, March). Complexity methods used in the study of some real systems with weak characteristic properties. In AIP Conference Proceedings (Vol. 1722, No. 1). AIP Publishing.
Prenga, D., Kovaçi, S., & Kushta, E. (2020). An econo-physics view on the historical dynamics of the Albanian currency vs. Euro exchange rates. Acta Universitatis Danubius. Economica, 16(1).
Prenga, D., Peqini, K., & Osmani, R. (2021, November). The analysis of the dynamics of the electorate system by using q-distribution-a case study. In Journal of Physics: Conference Series (Vol. 2090, No. 1, p. 012073). IOP Publishing.
Rangvid, J., & Sørensen, C. (2001). Determinants of the implied shadow exchange rates from a target zone. European Economic Review, 45(9), 1665–1696. doi:10.1016/s0014-2921(00)00082-9
Schneider, F., & Enste, D. H. (2000). Shadow Economies: Size, causes, and consequences. Journal of Economic Literature, 38(1), 77–114.
Siregar, R. Y. (2011). The Concepts of Equilibrium Exchange Rate: A Survey of Literature. An Extended version of the Report prepared for the 2006-2007 Exchange Rate Policy Evaluation Project of the Independent Evaluation Office (IEO), the International Monetary Fund, Washington, D.C.
Sornette, D. (1998). Discrete-scale invariance and complex dimensions. Physics Reports, 297(5), 239-270.
Sornette, D. (2003). Critical market crashes. Physics Reports, 378(1), 1-98.
Sornette, D., & Johansen, A. (2001). Significance of Log- periodic Precursors to Financial Crashes. Quantitative Finance, 1, 452.
The Global Economy. (n.d.). https://www.theglobaleconomy.com/Albania/remittances_percent_GDP
Tsallis, C. (2011). The Nonadditive Entropy Sq and its Applications in Physics and Elsewhere: Some Remarks. Entropy, 13, 1765-1804. DOI: 10.3390/e13[8]765
Tsallis, C. (2017). Economics and Finance Features Galore: q-Statistical Stylized. Entropy, 19, 457. DOI: 10.3390/e19090457.
Tsallis. C. (2017). Economics and Finance: q-Statistical Stylized Features Galore. Entropy, 19(9), 457. https://doi.org/10.3390/e19090457
Umarov, S., Tsallis, C., & Steinberg, S. (2008). On a q-Central Limit Theorem Consistent with Nonextensive Statistical Mechanics. Milan Journal of Mathematics, 76(1), 307–328. DOI: 10.1007/s00032-008-0087-y.
Xu, C., Ke, J., Peng, Z., Fang, W., & Duan, Y. (2022). Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices. Entropy, 24, 969. https://doi.org/10.3390/e24070969
Yakovenko, V.M. (2009). Econophysics, Statistical Mechanics Approach to. In: Meyers, R. (Ed.), Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_169
Zhou, W. X., & Sornette, D. (2002). Generalized q analysis of log-periodicity: Applications to critical ruptures. Physical Review E, 66(4), 046111.
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