Document Type : Research Article
Authors
1 Department of Management, Faculty of financial management, University of Tehran, Tehran, Iran
2 Telfer School of Management, University of Ottawa
3 Postdoc of Finance, Faculty of Economics, University of Tehran, Tehran, Iran
4 Azman Hashim International Business School (AHIBS), Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
5 Ph.D. student in Financial Engineering, Allameh Tabataba'i University, Tehran, Iran.
Abstract
This study examines the dynamics of the Iranian foreign exchange market and its impact on the exchange rate used by traders, and not the official rate in Iran. The study aims to extend Fama's theory of market efficiency and proposes a new model to define the opposite point called "Historical bias". The study applied the ARIMA and Markov switching models and dynamic conditional correlation to measure the speed of information circulation and to investigate the origin of the Iranian foreign exchange market's impact on the trader rate of the Dollar market. The study analyzed the convergence of the Iranian foreign exchange market based on different rates, the exchange rate used by traders, and the official rate and its effect on developing CBDC in Iran. The results of this study show that based on Fama's theory of market efficiency the foreign exchange market in Iran could have a 15% history-oriented bias, which is significant and would be an important problem for the launching of CBDC in Iran.
Keywords
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96-113.
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173-204, https://doi.org/10.1111/1468-0262.00393.
[3] D. Andolfatto, Assessing the impact of central bank digital currency on private banks, The
Economic Journal, 131(634) (2021), 525-540.
[4] S. Allen, S. Apkun, I. Eyal, G. Fanti, B. A. Ford, J. Grimmelmann, A. Juels, K.
Kostiainen, S. Meiklejohn, A. Miller, E. Prasad, and F. Zhang, Design choices for
central bank digital currency: Policy and technical considerations, NBER Working Paper,
National Bureau of Economic Research, 27634 (2020), Cambridge, Massachusetts.
[5] R. Ball, The Global Financial Crisis and the Efficient Market Hypothesis: What
Have We Learned?, Journal of Applied Corporate Finance, 21(4) (2009), 816,
https://doi.org/10.1111/j.1745-6622.2009.00246.x.
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the Business and Economic Statistics, American Statistical Association, Washington DC,
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Fabio, P. (2020).
[8] T. Bollerslev, and R. Hodrick, Financial Market Efficiency Tests,
https://doi.org/10.3386/w4108 (1992).
[9] Castren et al ´ , Digital currencies in financial networks, Journal of Financial Stability,
https://doi.org/10.1016/j.jfs.2022.101000 (2022).
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FEDS Notes, https://www.federalreserve.gov/econres/notes/feds-notes/central-bank-digitalcurrency-a-literature-review-20201109.htm (2020).
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Implications for Monetary, Regulatory, and International Policies, MIT Press (2002).
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Journal of Innovative Research and Development, 5(4) (2016), 272-281.
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https://doi.org/10.1111/j.1540-6261.1993.tb04702.x.
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central banks, Service Industries Journal (UK), 2 (2020), 62-79.
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Econometrica, 47(2) (1979), 263, https://doi.org/10.2307/1914185.
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Stability, Journal of Money, Credit and Banking (2022).
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Journal of Economic Dynamics and Control, 142 (2022), 104501.
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/media/frbp/assets/working-papers/2019/wp19-26.pdf (2019).
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6261.1970.tb00518.x.
[47] S. L. Na t’nez Alonso, J. Jorge-Vazquez, and R. F. Reier Forradellas, Detection of
financial inclusion vulnerable rural areas through an access to cash index: solutions based
on the pharmacy network and a CBDC. Evidence based on Avila t (Spain), Sustainability,
12 (2020), 7480, https://doi.org/10.3390/su12187480.
[48] D. B. Nelson, and C. Q. Cao, Inequality constraints in the univariate GARCH model,
Journal of Business Economic Statistics, 10(2) (1992), 229-235.
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of Portsmouth. Manuscript (2020).
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Journal of Money Laundering Control (2022b).
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provisions, https://mpra.ub.uni-muenchen.de/116412/ MPRA Paper No. 116412 (2023).
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Markets and World Stock Markets, Jurnal Pengurusan, 52 (2018a), 181-192,
https://doi.org/10.17576/pengurusan-2018-52-15.
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Egyptian Stock Market, International Research Journal of Finance and Economics, 78 (2011),
96-113.
[2] D. Abreu, and M. K. Brunnermeier, Bubbles and Crashes, Econometrica, 71(1) (2003),
173-204, https://doi.org/10.1111/1468-0262.00393.
[3] D. Andolfatto, Assessing the impact of central bank digital currency on private banks, The
Economic Journal, 131(634) (2021), 525-540.
[4] S. Allen, S. Apkun, I. Eyal, G. Fanti, B. A. Ford, J. Grimmelmann, A. Juels, K.
Kostiainen, S. Meiklejohn, A. Miller, E. Prasad, and F. Zhang, Design choices for
central bank digital currency: Policy and technical considerations, NBER Working Paper,
National Bureau of Economic Research, 27634 (2020), Cambridge, Massachusetts.
[5] R. Ball, The Global Financial Crisis and the Efficient Market Hypothesis: What
Have We Learned?, Journal of Applied Corporate Finance, 21(4) (2009), 816,
https://doi.org/10.1111/j.1745-6622.2009.00246.x.
[6] F. Black, Studies of Stock Price Volatility Changes, Proceedings of the 1976 Meeting of
the Business and Economic Statistics, American Statistical Association, Washington DC,
177-181 (1976).
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Fabio, P. (2020).
[8] T. Bollerslev, and R. Hodrick, Financial Market Efficiency Tests,
https://doi.org/10.3386/w4108 (1992).
[9] Castren et al ´ , Digital currencies in financial networks, Journal of Financial Stability,
https://doi.org/10.1016/j.jfs.2022.101000 (2022).
[10] F. Carapella, and J. Flemming, Central bank digital currency: A literature review,
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(2019).
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Implications for Monetary, Regulatory, and International Policies, MIT Press (2002).
[13] K. D. Daniel, D. A. Hirshleifer, and A. Subrahmanyam, A Theory of Overconfidence,
Self-Attribution, and Security Market Under- and Over-reactions, SSRN Electronic Journal,
https://doi.org/10.2139/ssrn.2017 (1997).
[14] W. F. M. de Bondt, Y. G. Muradoglu, H. Shefrin, and S. Staikouras, Behavioral
Finance: Quo Vadis?, Journal of Applied Finance, 18(2) (2008).
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Dimensions: The Good, the Better and the Ugly, Journal of Financial Econometrics, 19(2)
(2021), 236-257, https://doi.org/10.1093/jjfinec/nby033.
[16] C. Dritsaki, An Empirical Evaluation in GARCH Volatility Modeling: Evidence from
the Stockholm Stock Exchange, Journal of Mathematical Finance, 07(02) (2017a), 366-390,
https://doi.org/10.4236/jmf.2017.72020.
[17] C. Dritsaki, An Empirical Evaluation in GARCH Volatility Modeling: Evidence from
the Stockholm Stock Exchange, Journal of Mathematical Finance, 07(02) (2017b), 366-390,
https://doi.org/10.4236/jmf.2017.72020.
[18] S. M. Davoodalhosseini, Central bank digital currency and monetary policy, J. Econ. Dyn.
Control, 142 (2022), 104150.
[19] R. Engle, Dynamic Conditional Correlation, Journal of Business Economic Statistics, 20(3)
(2002), 339-350, https://doi.org/10.1198/073500102288618487.
[20] R. Engle, and K. Sheppard, Theoretical and Empirical properties of Dynamic Conditional
Correlation Multivariate GARCH, https://doi.org/10.3386/w8554 (2001).
[21] B. Fakhry, and C. Richter, Is the sovereign debt market efficient? Evidence from the US
and German sovereign debt markets, International Economics and Economic Policy, 12(3)
(2015), 339-357, https://doi.org/10.1007/s10368-014-0304-9.
[22] E. F. Fama, Random Walks in Stock Market Prices, Financial Analysts Journal, 51(1) (1995),
75-80, https://doi.org/10.2469/faj.v51.n1.1861.
[23] M. Farajnezhad, S. A/L Ramakrishnan, and M. Shehni Karam Zadeh, Analyses the
Effect of Monetary Policy Transmission on the Inequality in OECD Countries, Journal of
Environmental Treatment Techniques, Volume 8 , Issue 2 (2020), 589-596.
[24] A. Garct’a, B. Lands, X. Liu, and J. Slive, The potential effect of a central bank digital
currency on deposit funding in Canada, Bank of Canada, No. 2020-15 (2020).
[25] L. Glosten, R. Jagannathan, and D. Runkle, On the relation between the expected value
and the volatility of the nominal excess return on stocks, Journal of Finance, 48 (1993),
1179-801.
[26] Z.-Y. Guo, Models with Short-Term Variations and Long-Term Dynamics in Risk Management of Commodity Derivatives, EconStor Preprints from ZBW - Leibniz Information Centre
for Economics (2017a).
[27] A. George, T. Xie, and J. D. A. Alba, Central bank digital currency with adjustable
interest rate in small open economies, SSRN, 3605918 (2020).
[28] H. Hong, and M. Kacperczyk, The price of sin: The effects of social norms on markets, Journal of Financial Economics, 93(1) (2009), 15-36,
https://doi.org/10.1016/j.jfineco.2008.09.001.
[29] H. Hong, and J. C. Stein, A Unified Theory of Underreaction, Momentum Trading,
and Overreaction in Asset Markets, The Journal of Finance, 54(6) (1999), 2143-2184,
https://doi.org/10.1111/0022-1082.00184.
[30] M. Hussain, G. F. Zebende, U. Bashir, and D. Donghong, Oil price and
exchange rate co-movements in Asian countries: Detrended cross-correlation approach, Physica A: Statistical Mechanics and Its Applications, 465 (2017), 338-346,
https://doi.org/10.1016/j.physa.2016.08.056.
[31] A. Intaz, D. Subhrabaran, and R. Niranjan, Stock Market Volatility, Firm Size and
Returns: A Study of Automobile Sector of National Stock Exchange in India, International
Journal of Innovative Research and Development, 5(4) (2016), 272-281.
[32] N. Jegadeesh, and S. Titman, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, The Journal of Finance, 48(1) (1993), 65-91,
https://doi.org/10.1111/j.1540-6261.1993.tb04702.x.
[33] R. Juks, Central bank digital currencies, supply of bank loans and liquidity provision by
central banks, Service Industries Journal (UK), 2 (2020), 62-79.
[34] D. Kahneman, and A. Tversky, Prospect Theory: An Analysis of Decision under Risk,
Econometrica, 47(2) (1979), 263, https://doi.org/10.2307/1914185.
[35] Y. S. Kim, and O. Kwon, Central Bank Digital Currency, Credit Supply, and Financial
Stability, Journal of Money, Credit and Banking (2022).
[36] T. Keister, and C. Monnet, Central bank digital currency: Stability and information,
Journal of Economic Dynamics and Control, 142 (2022), 104501.
[37] T. Keister, and D. Sanches, Should Central Banks Issue Digital Currency?,
Philadelphia Fed working papers, WP 19-26, https://www.philadelphiafed.org/-
/media/frbp/assets/working-papers/2019/wp19-26.pdf (2019).
[38] S. Laurent, J. V. K. Rombouts, and F. Violante, On the forecasting accuracy of
multivariate GARCH models, Journal of Applied Econometrics, 27(6) (2012), 934-955,
https://doi.org/10.1002/jae.1248.
[39] H.-C. Liu, and J.-C. Hung, Forecasting SP-100 stock index volatility: The role of volatility
asymmetry and distributional assumption in GARCH models, Expert Systems with Applications, 37(7) (2010), 4928-4934, https://doi.org/10.1016/j.eswa.2009.12.022.
[40] A. W. Lo, and A. C. MacKinlay, The size and power of the variance ratio test in finite samples, Journal of Econometrics, 40(2) (1989), 203-238, https://doi.org/10.1016/0304-
4076(89)90083-3.
[41] C. Mackay, Memoirs of extraordinary popular delusions and the madness of crowds, Dover
Publication Inc. (2003).
[42] B. G. Malkiel, Expectations, Bond Prices, and the Term Structure of Interest Rates, The
Quarterly Journal of Economics, 76(2) (1962), 197, https://doi.org/10.2307/1880816.
[43] B. G. Malkiel, The Efficient Market Hypothesis and Its Critics, Journal of Economic Perspectives, 17(1) (2003), 59-82, https://doi.org/10.1257/089533003321164958.
[44] B. G. Malkiel, Reflections on the Efficient Market Hypothesis: 30 Years Later, The Financial Review, 40(1) (2005), 19, https://doi.org/10.1111/j.0732-8516.2005.00090.x.
[45] B. G. Malkiel, and E. F. Fama, EFFICIENT CAPITAL MARKETS: A REVIEW OF
THEORY AND EMPIRICAL WORK*, The Journal of Finance, 25(2) (1970a), 383-417,
https://doi.org/10.1111/j.1540-6261.1970.tb00518.x.
[46] B. G. Malkiel, and E. F. Fama, Efficient Capital Markets: A review of Theory and Empirical Work, The Journal of Finance, 25(2) (1970b), 383-417, https://doi.org/10.1111/j.1540-
6261.1970.tb00518.x.
[47] S. L. Na t’nez Alonso, J. Jorge-Vazquez, and R. F. Reier Forradellas, Detection of
financial inclusion vulnerable rural areas through an access to cash index: solutions based
on the pharmacy network and a CBDC. Evidence based on Avila t (Spain), Sustainability,
12 (2020), 7480, https://doi.org/10.3390/su12187480.
[48] D. B. Nelson, and C. Q. Cao, Inequality constraints in the univariate GARCH model,
Journal of Business Economic Statistics, 10(2) (1992), 229-235.
[49] E. Y. Oh, and S. Zhang, Central Bank digital currency and informal economy, University
of Portsmouth. Manuscript (2020).
[50] P. K. Ozili, Central bank digital currency research around the World: a review of literature,
Journal of Money Laundering Control (2022b).
[51] P. K. Ozili, Central bank digital currency and bank earnings management using loan loss
provisions, https://mpra.ub.uni-muenchen.de/116412/ MPRA Paper No. 116412 (2023).
[52] R. Robiyanto, Indonesian Stock Markets Dynamic Integration with Asian Stock
Markets and World Stock Markets, Jurnal Pengurusan, 52 (2018a), 181-192,
https://doi.org/10.17576/pengurusan-2018-52-15.
[53] R. Robiyanto, The Dynamic Correlation between ASEAN-5 Stock Markets and World Oil Prices, Jurnal Keuangan Dan Perbankan, 22(2) (2018b),
https://doi.org/10.26905/jkdp.v22i2.1688.
[54] M. H. Ronaghi, A contextualized study of blockchain technology adoption as a digital currency platform under sanctions, Management Decision, (ahead-of-print) (2022).
[55] P. Sadorsky, Modeling volatility and conditional correlations between socially responsible investments, gold and oil, Economic Modelling, 38 (2014), 609-618,
https://doi.org/10.1016/j.econmod.2014.02.013.
[56] S. Sarkar, and A. Banerjee, Modeling daily volatility of the Indian stock market using
intra-day data.
[57] R. J. Shiller, Do Stock Prices Move Too Much to be Justified by Subsequent Changes in
Dividends?, The American Economic Review, 71(3) (1981), 421-436.
[58] R. J. Shiller, Bubbles, Human Judgment, and Expert Opinion, Financial Analysts Journal,
58(3) (2002), 18-26, https://doi.org/10.2469/faj.v58.n3.2535.
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