1. Home
  2. Vol 5, No 1 (2024)
  3. Kushta

Investigating Statistical Features of the FX Bid Ask Series in a Small Economy with a Sizeable Informal Economy

Elmira Kushta, Erarda Vuka, Dode Prenga, Ines Dika

Abstract


The assessment of the effect of the informal use of a foreign currency on the corresponding FX rates toward the national currency is a very difficult task, requiring direct calculation and modeling. It comes mostly because of the unknown quantity of foreign money used in the informal sector, but also because of the lack of quantitative calculation models. To overcome this gap and to realize a qualitative description of this effect in a concrete economic environment, we propose herein a comparative analysis between the behavior of two typical FX rate series recorded in the Albanian currency market, the Euro-ALL and USD-ALL, providing that the Euro is used commonly as a national currency substitute in the informal economy and the USD is not. So, we have evidenced that the un-stationarity degree of the Bid and Ask spread distribution for the Euro-ALL FX series is higher than for the corresponding USD-ALL case, but with a lower variance. Those features occurring simultaneously can be explained by assuming that informal use of the Euro acts as an additional perturbation on the FX system, imposing high nonstationary, but at the same time it provides reservoir or source features for the money disbalances, reducing the average fluctuations. Next, the depth of the market measured by the average Bid-Ask Spread has resulted in a smaller price for the Euro currency, indicating a lower cost of the transactions and reinforcing the assumption regarding the distribution’s in-stationarity features. Based on those indicatory findings, we propose to realize indirect evidence for our assumptions by comparing the reproduction of the corresponding distribution using autoregressive models. In this case, we have evidenced that the distribution of the FX Euro-ALL spread can be reproduced better if we include in standard ARCHX (m, n, p) models a term that mimics the informality measure. When applying the same procedure for the USD-ALL spread, the resulting distribution has not matched equally well with the original ones. Those findings have been discussed in the framework of an alternative description of the effect of the informal use of foreign currency in a small economy with a sizeable informal sector, which convene our current system under analysis, but we believe that they can be applicable for similar economic environments.

 

Doi: 10.28991/HEF-2024-05-01-02

Full Text: PDF


Keywords


Exchange Rates; q-Gaussian; Autoregressive Model; Informal Economy.

References


Haque, N. U., Khan, M. S., & Montiel, P. J. (1991). Macroeconomic models for adjustment in developing countries. International Monetary Fund, New Hampshire, United States.

Betts, C., & Devereux, M. B. (2000). Exchange rate dynamics in a model of pricing-to-market. Journal of International Economics, 50(1), 215–244. doi:10.1016/S0022-1996(98)00036-1.

Siregar, R. Y. (2011). The concepts of equilibrium exchange rate: a survey of literature. Munich Personal RePEc Archive, MPRA Paper No. 28987. Available online: https://mpra.ub.uni-muenchen.de/28987/ (accessed on May 2023).

Hamilton, A. (2018). Understanding Exchange Rates and Why They Are Important. Reserve Bank of Australia Bulletin, December 2018, 1–18.

Kuzmin, A. (2022). Mathematical Exchange Rates Modeling: Equilibrium and Nonequilibrium Dynamics. Mathematics, 10(24), 4672. doi:10.3390/math10244672.

Prenga, D., & Denaj, A. (2023). An econophysics analysis of the effect of informal economy on FX rates in small and transition economies. IC-MSQUARE, August 28-31, Belgrade, Serbia.

Bollen, N. P. B., Smith, T., & Whaley, R. E. (2004). Modeling the bid/ask spread: Measuring the inventory-holding premium. Journal of Financial Economics, 72(1), 97–141. doi:10.1016/S0304-405X(03)00169-7.

Bollerslev, T., & Melvin, M. (1994). Bid-ask spreads and volatility in the foreign exchange market. An empirical analysis. Journal of International Economics, 36(3–4), 355–372. doi:10.1016/0022-1996(94)90008-6.

Prenga, D.S.K., & Kushta, E. (2020). An Econo-Physics View on the Historical Dynamics of the Albanian Currency vs. Euro Exchange Rates. Acta Universitatis Danubius: Œconomica, 16(1), 238-251.

Ceyhun, M. E., Kose, A., Ohnsorge, F., & Yu, S. (2021). DP16497 Understanding Informality. CEPR Press Discussion Paper No. 16497. Available online: https://cepr.org/publications/dp16497 (accessed on May 2023).

World Economics (2023). Informal Economy Sizes: Informal Economy Size as a Percentage of GDP. World Economics, London, United Kingdom. Available online: https://www.worldeconomics.com/Informal-Economy (accessed on May 2023).

The Global Economy (2023). Albania: Remittances, percent of GDP. Global Economy: Business and economic data for 200 countries. Available online: https://www.theglobaleconomy.com/Albania/remittances_percent_GDP (accessed on June 2023).

Bank of Albania. (2023). Bank of Albania. Tirana, Albania. Available online: https://www.bankofalbania.org/ (accessed on May 2023).

Minister of Finance. (2023). Ministry of Finance and Economy of Albania. Tirana, Albania. Available online: https://financa.gov.al (accessed on May 2023).

Kovaçi, S., Prenga, D., and Ramosaço, M. (2022). Using mixed approaches for a comparative study of the properties of the exchange rate for Albanian Lek to Euro during the 2020-2022 economic crunch. 22nd IC on: Social and Natural Sciences. 25 June 2022, Brussels, Belgium.

Dika, I., & Prenga, D. (2023). A statistical analysis of the impact of the informal economy on the comportment of the exchange rates in Albania. 22nd International Conference 20-23 August 2023, Burgas, Bulgaria.

Di Matteo, T. (2007). Multi-scaling in Finance. Quantitative Finance, 7(1), 21-36.

Schneider, F., Enste, D., & H. (2000). Shadow Economies: Size, causes, and consequences. Journal of Economic Literature, 38(1), 77–114.

Dell’Anno, R., & Schneider, F. (2009). A complex approach to estimate shadow economy: The structural equation modelling. New Economic Windows, 7, 111–130. doi:10.1007/978-88-470-1083-3_7.

Dell’Anno, R. (2023). Measuring the unobservable: estimating informal economy by a structural equation modeling approach. International Tax and Public Finance, 30(1), 247–277. doi:10.1007/s10797-022-09742-0.

Dornbusch, R. (1976). Expectations and Exchange Rate Dynamics. Journal of Political Economy, 84(6), 1161–1176. doi:10.1086/260506.

Urrutia, A., and Urrutia, C. (2019). Does informality facilitate inflation stability”? Enrique Monetary and Economic Department April 2019. Bank for International Settlements, Basel, Switzerland.

Gray, M.R., & Turnovsky, S.J. (1979). The Stability of Exchange Rate Dynamics under Perfect Myopic Foresight. International Economic Review, 20(3), 643. doi:10.2307/2526263.

Obstfeld, M., & Stockman, A.C. (1985). Exchange-rate dynamics. Handbook of International Economics, 2, 917-977. doi:10.1016/S1573-4404(85)02009-3.

Lera, S.C., & Sornette, D. (2019). An Explicit Mapping of Currency Target Zone Models to Option Prices. International Review of Finance, 19(4), 919–927. doi:10.1111/irfi.12196.

Bertola, G., & Caballero, R.J. (2018). Target Zones and Realignments. The American Economic Review, 82(3), 520-536.

Krugman, P. R. (1991). Target Zones and Exchange Rate Dynamics. The Quarterly Journal of Economics, 106(3), 669–682. doi:10.2307/2937922.

Rangvid, J., & Sorensen, 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.

Quinn, B., Gallagher, R., & Kuosmanen, T. (2023). Lurking in the shadows: The impact of CO2 emissions target setting on carbon pricing in the Kyoto agreement period. Energy Economics, 118, 106338. doi:10.1016/j.eneco.2022.106338.

Treepongkaruna, S., Brailsford, T., & Gray, S. (2014). Explaining the bid-ask spread in the foreign exchange market: A test of alternate models. Australian Journal of Management, 39(4), 573–591. doi:10.1177/0312896213499028.

Wdowiński, P. (2007). A Note on the Dornbusch Overshooting Model under Nominal and Real Interest Rates. FindEcon Monograph Series: Advances in Financial Market Analysis, 41–60.

Frankel, J. A., Galli, G., & Giovannini, A. (2013). The Microstructure of Foreign Exchange Markets. The Microstructure of Foreign Exchange Markets. doi:10.7208/chicago/9780226260235.001.0001.

Eurostat. (2023). European statistics: Eurostat, European Union. Available online: https://ec.europa.eu/eurostat/statistics-explained (accessed on June 2023).

Feder, J. (2013). Fractals. Springer Science & Business Media, Berlin, Germany.

Ranaldo, A., & Somogyi, F. (2021). Asymmetric information risk in FX markets. Journal of Financial Economics, 140(2), 391–411. doi:10.1016/j.jfineco.2020.12.007.

Tsallis, C. (2017). Economics and finance: q-statistical stylized features galore. Entropy, 19(9), 457. doi:10.3390/e19090457.

Umarov, S., Tsallis, C., & Steinberg, S. (2008). On a q-central limit theorem consistent with non-extensive statistical mechanics. Milan Journal of Mathematics, 76(1), 307–328. doi:10.1007/s00032-008-0087-y.

Kushta, E., Prenga, D., Denaj, A., & Tahiraj, V. (2023). An empirical approach in the study of heterogenous Na-tech systems. IAI Academic Conference Proceedings, 1-62, Corvinus University, Budapest, Hungary.

Borland, L. (2002). Option Pricing Formulas Based on a Non-Gaussian Stock Price Model. Physical Review Letters, 89(9), 98701. doi:10.1103/PhysRevLett.89.098701.

Tsallis, C. (2011). The non-additive entropy SQ and its applications in physics and elsewhere: Some remarks. Entropy, 13(10), 1765–1804. doi:10.3390/e13101765.

Guirguis, M. (2018). Application of a GARCH, TGARCH, and EGARCH, Models to Test the Spot GBP/USD Exchange Rate Volatility. SSRN Electronic Journal, 1-24. doi:10.2139/ssrn.3253608.


Full Text: PDF

DOI: 10.28991/HEF-2024-05-01-02

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Dode Prenga, Ines Dika, Erarda Vuka