An Introduction to the Box-Jenkins Method
BOX & JENKINS METHOD
I. Introduction
Many market participants, namely, international investors, banks, non-bank financial institutions, portfolio managers, are interested in coming up with a model, which accurately predicts exchange rates. Managers of multinational corporations are interested in accuracy of such foreign exchange prediction models as it directly impacts their activities relating to exposure management, hedging, arbitraging, investing and financing decisions. Policymakers frequently monitor exchange rates to better understand their impact on trade positions, and consequently, domestic employment, business and revenue prospects. Nowadays, more attention is being focused on foreign exchange rate prediction models since the foreign exchange market is considered to be the world 's biggest financial market, with an average daily trading of $ 1.2 trillion.
The failure of standard economic models to display any out-of-sample forecasting ability over horizons of up to one year "continues to exert a pessimistic effect on the field of empirical exchange rate modeling in particular and international finance in general" (Frankel and Rose, 1994). 1 As a result of this lack of success, many economists have turned to alternative approaches to modeling exchange rates over shorter horizons. One important line of research considers the effect that technical analysts or noise traders may have on the market. Technical analysts ignore fundamental variables (such as money supplies, income levels or interest rates) and instead use statistical, graphical or, in some cases, astrological techniques to predict exchange rates. Many economists argue that dealing by noise traders may be sufficient to drive a wedge between the market price and the `true ' fundamental price. The market price only returns to the fundamental price in the long run when the random effects of the supposedly irrational noise traders wash out. It is argued, therefore, that economic models may
I. Introduction
Many market participants, namely, international investors, banks, non-bank financial institutions, portfolio managers, are interested in coming up with a model, which accurately predicts exchange rates. Managers of multinational corporations are interested in accuracy of such foreign exchange prediction models as it directly impacts their activities relating to exposure management, hedging, arbitraging, investing and financing decisions. Policymakers frequently monitor exchange rates to better understand their impact on trade positions, and consequently, domestic employment, business and revenue prospects. Nowadays, more attention is being focused on foreign exchange rate prediction models since the foreign exchange market is considered to be the world 's biggest financial market, with an average daily trading of $ 1.2 trillion.
The failure of standard economic models to display any out-of-sample forecasting ability over horizons of up to one year "continues to exert a pessimistic effect on the field of empirical exchange rate modeling in particular and international finance in general" (Frankel and Rose, 1994). 1 As a result of this lack of success, many economists have turned to alternative approaches to modeling exchange rates over shorter horizons. One important line of research considers the effect that technical analysts or noise traders may have on the market. Technical analysts ignore fundamental variables (such as money supplies, income levels or interest rates) and instead use statistical, graphical or, in some cases, astrological techniques to predict exchange rates. Many economists argue that dealing by noise traders may be sufficient to drive a wedge between the market price and the `true ' fundamental price. The market price only returns to the fundamental price in the long run when the random effects of the supposedly irrational noise traders wash out. It is argued, therefore, that economic models may
References: •Engel, C. (1994) "Can the Markov switching model forecast exchange rates?," Journal of International Economics, 36, 151-165. •Frankel, J.A. and Rose, A.K. (1994) "A survey of empirical research on nominal •Exchange rates," NBER working paper No. 4865. •Kahn, R.N. and Rudd, A. (1995) "Does historical performance predict future performance?" Financial Analysts Journal, 51, 43-52. •Leitch, G. and Tanner, J.E. (1991) "Economic forecast evaluation: Profits versus the conventional error measures," American Economic Review, 81, 580-590. •Meese, R.A. and Rogoff, K. (1983) "Empirical exchange rate models of the seventies: Do they fit out of sample?," Journal of International Economics, 14, 3-24.