foreign exchange rate forecasting_s.pptx

For eg it is possible to specify an equation

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For e.g., it is possible to specify an equation explaining X1 in terms of S, and the explanatory variables X2,…Xn. The equation for X1 may be written as This implies the presence of the feedback from S to X1. If we assume that X1 is i, this tells us that while the i affects S (for e.g. by attracting foreign investment), the S in turn affects i . For e.g. an excessively weak currency (S) may trigger a policy reaction leading to higher i . Multi-equation models can be large with large number of parameters to estimate, hence, they are costly due to needing longer dataset, more computing time, and arising problems of non-convergence. Even if we are able to estimate the multi-equation model precisely, there is
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10 Issues with econometric forecasting models: Single-equation model is generally in reduced form because it explains the value of exchange rate in terms of other variables without telling us how the explanatory variables are determined. This is called as endogenity issue. Forecasts of S may be conditional on the future values of the explanatory variables. Exchange rate is available on high frequency (for e.g. daily), while most explanatory variables are available at low frequency (for e.g. monthly inflation). Thus we cannot use PPP equation to forecast daily exchange rates. Equation is estimated under the assumption that the model’s parameter will remain the same in future? The structural changes can be adopted by updating parameter using the new data when available. Measurement errors in variables, for e.g., BoP positions is
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11 Time series models These are based entirely on the history of the exchange rate. For e.g. autoregressive models of order p for returns on exchange rate (S): AR(1) model for returns on exchange rate S: State space models for exchange rate (single source of error model, SSOEM, (stochastic seasonality, stochastic level, and stochastic cycle): The model can estimated by minimizing the sum of square of errors subject to the above and the following constraints α, β, and γ to be greater than zero and less than 1. Parameters: α, β, γ, φ, l_o, c_o, s_-3, s_-2,s_-1, s_0=-sum(s_-3:s_- SSOEM is not examinable 1 . . ) ( ) ( ) ( 4 1 1 4 1 1 t s y seasonalit e s s cycle e c c trend e l l e s c l S or c s l S t t t t t t t t t t t t t t t t t t
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12 Problems with time series models Time series forecasting is undermined by the market efficiency hypothesis, if the FX market is indeed efficient. If the FX market is weakly efficient, the exchange rate must follow a random walk, which means that we cannot forecast the exchange rate based on its past history. The random walk model implies that, given the level of the exchange rate today, it is as likely to be up tomorrow as it is to be down.
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