چکیده:
The main reason for introducing uncertainty models to conventional models is the documented and valid evidence indicating the various failures of rational expectations models in explaining certain characteristics of the foreign exchange market, particularly exchange rate fluctuations and future rate forecasting. For this reason, in this research, a conventional monetary model is introduced in which market participants learn about the surrounding economic structure using adaptive learning rules. Although it is assumed in the introduced model that market participants are aware of the stochastic process that drives the fundamental variables, here too, market participants are unaware of the parametric values obtained through least squares learning. Under such conditions, an attempt was made to compare the model's predictions under adaptive learning with conventional forms of expectation formation. The findings obtained in this research for the parity rates of the Rial against several different currencies show that the model under adaptive learning has superiority over both conventional expectation models in providing a closer-to-reality explanation of exchange rate behavior. Finally, the results of this research showed that even in conditions where the market faces limited and guided fluctuations and uncertainty in the market adds to its complexities, market participants still use learning methods to improve their predictions.
خلاصه ماشینی:
Since it is assumed that market participants know the parameter values of the monetary variable process, they predict future money variables by applying the conditional mathematical expectation to the random walk rule and use the logarithm of the nominal exchange rate at time t to obtain the following values: (Mark, 2001: 77-68) (4)(Refer to page image) Under conditions where the growth rate of monetary variables follows a continuous stationary process, the monetary model can provide a better explanation regarding higher fluctuations in exchange rates: (5)(Refer to page image) This implies that the forecast formula k periods ahead in time is as follows: (6)(Refer to page image) By converting the above relationship to levels, we will have: (7)(Refer to page image) By placing this forecast into relationship (4), we will have: (8)(Refer to page image) By simplifying the above relationship, we will have: (9)(Refer to page image) In the above relationships, (Refer to page image) and (Refer to page image) are present.
At the beginning, the rational expectations state was simulated, in which market participants know the parameter values of the monetary variables process and determine the logarithm of the nominal exchange rate at time t based on equation (10).
Then, the adaptive expectations state is considered, in which market participants have no knowledge of the functional form of the monetary variables process nor the parameter values; therefore, the logarithm of the exchange rate at time t is determined by the past observations of the monetary variables, similar to equation (21).
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