International Bond and Currency Markets - Essay Example

?International Bond and Currency Markets Table of Contents Introduction 3 Challenges in Forecasting Exchange Rate in Short Run 4 Challenges in Forecasting Exchange Rate in Long Run 6 Conclusion 8 References 10 Introduction Effective and accurate forecasting of the exchange rate fluctuations is an important and fundamental problem in international financial decisions. Exchange rate forecasting is necessary to evaluate the foreign denominated cash flows involved in international transactions. It is also important to analyse the risks attached to the international business environments and the investors correspondingly. Moreover, the accuracy of forecasted results, being a determinant in investment decisions of the international bond and the currency markets, are deemed to assist in maintaining stability of the exchange rate, further complying with economic growth requirements. In precise, it also helps in pre-determining the fluctuation of the currency appreciation or depreciation in respect to countries and influenced cross border trade prospects by a substantial extent. It is noteworthy that the volatility of exchange rate, in the short run and also in the long run depends on multiple factors such as the demand and supply prospects in the financial market. Arguably, forecasting in the long run and short run is considered as quite difficult, which can be explained with reference to the Theory of Speculation in the market and the collective belief of the investors about the future prospects (Andreou & Zombanakis, 2006). Based on these underpinnings about the importance of forecasting exchange rate fluctuations, this essay will aim at emphasising the challenges commonly witnessed by analysts when obtaining the intended forecasting results in the short run as well as in the long run performance of the exchange rate. Challenges in Forecasting Exchange Rate in Short Run Arguably, in the short run, the forecasting of exchange rate is nearly impossible. Forecasts, which are delivered by the macroeconomic factors, are generally less accurate than the results obtained through Random Walk theory application. In general, the Random Walk Theory presumes that market changes, in terms of stock-prices changes, are unpredictable. Even though in the long run forecasting, the theory has been considered by many financial investors and analysts, the short run implications of Random Walk theory remains under considerable scrutiny. It is in this context that no claims to substantiate a perfect Random Walk model in the short-run stock price fluctuation were firmly made. On the contrary, arguments centralised on the theory that forecasting stock-prices changes in the short run is challenging owing to the fact that in the short run, the volatility of the exchange rate is less but the speed of convergence based on Purchasing Power Parity (PPP) is slower than that recorded in the long run (Babazadeh & Farrokhnejad, 2012). Correspondingly, it has been argued that the level of exchange rate in the short run is not very predictable, but is also not entirely unpredictable, as the volatility of the currency and the correlation between them vary with time and hence, forecasting becomes challenging (Mitra, 2008). The current account balances, real income of the people, interest rates, the preferences of the consumers regarding the domestic or foreign products, are all signified as market fundamentals influence the stock-prices in the short run, as per the conceptual framework of PPP. As explained by Taylor & Taylor (2004: 135), “PPP is a disarmingly simple theory that holds that the nominal exchange rate between two currencies should be equal to the ratio of aggregate price levels between the two countries, so that a unit of currency of one country will have the same purchasing power in a foreign country”. Subsequently, it is the monetary policies, the fiscal policies and the market speculations affect the forecasting decisions in the short run. These factors are important when considering the financial transfers with regards to the differences in the interest rates. Notably, the monetary indices and foreign exchange trends impose a negative effect on the stock-prices fluctuations in the short run, wherein forecasting these attributes in an isolated manner becomes quite complex and challenging. Also, the forecasting procedure in the short-run normally tends to be concerned with the changes in the nominal exchange rate and hence, if the country is facing high inflation, the disparity of the exchange increases from the nominal rate consequently. Such influences often lead to the misinterpretation of the exchange rate in the short run (Alter & Oppenheimer, 2006; Taylor & Taylor, 2004). Along with the applications of the Random Walk Theory and PPP, the forecasting of short run stock-price changes is also conducted using the mathematical technique, commonly through the linear regression analysis. Additionally, factors that affect the forecasting result accuracy in the short run, depends significantly on the interest rates of the country, for example if the UK interest rate is relatively higher than elsewhere, foreign investors are likely to become more attracted towards the UK market. This will give rise to the demand for Sterling, which is trendily referred as “Hot Money Flow” in the UK market. Correspondingly, it can be presumed as a significant short run factor determining the value of currency in one economy and therefore, causing immense effects on the short run performance of the stock market. The continuous speculations in the market and the government policies are also one of the challenges faced while forecasting the exchange rate in the short run (Bianco & et. al., 2012; Dornbusch & Krugman, 1976). However, it is often argued that forecasting stock-price fluctuations in the short-run, with the assistance of Random Walk theory is much simple and less challenging. It is thus assumed that the exchange rate in the short run is generated by the Unit Root Process: yt+1 = yt +?t+1; where ?t+1 ? NID (0, ?2) is white noise. In the sample of length (T), the RW y?T+h is equal to the last available observation yT:y? T+h = yT. Correspondingly, yt refers to the logarithm of the exchange rate (Muck & Skrzypczynski, 2012). Forecasting short run changes in the stock-market is also affected due to the hedging activities, imbalances in trade and the interest rate alterations, and thus can be deemed as significant challenges in this context. It is worth mentioning in this context that the objective of the currency market micro structure has always been to enhance the understanding of short run exchange dynamics. While it argues that the short run forecast fails due to PPP and the uncovered interest parity (Carlson & et. al., 2008). Challenges in Forecasting Exchange Rate in Long Run The long run forecasting procedure of the exchange rate also involves various challenges in providing accurate data. The exchange rates are highly sensitive to current demand and supply trends observable with any economic periphery. Hence, the long term technique might lead to misinterpretation of data. Forecasting foreign exchange in the long run is therefore considered to be quite tricky; nevertheless it can be quite useful for investors, bankers and foreign exchange dealers in making safe investment decisions. Emphasising this particular aspect, it can be affirmed that the long run forecast is difficult owing to the influence of continuously changing money value in the international context. Additionally, in the long run, data and information based on market fluctuations tend to become out-dated after a certain period of time, recording which, on a continuous basis becomes a major challenge in forecasting long run stock-price changes. It is also noteworthy that in the long run, the prices of the identical goods change at varying proportions in different countries and makes it difficult to be forecasted because of the convergence of prices in the times to come. Due to these obstructions in the speculation of market attributes, the forecast of the exchange rate in the long run faces significant challenges, in comparison to the short run (Hopper, 1997). The factors that affect the forecasting of the exchange rate in the long run are the relative price levels, trade barriers and the productivity of the countries. The inflation rates of the country also tend to affect the currency market fluctuations either appreciating or depreciating the value of money within given economic contexts. Another factor that influences the exchange rate is the Law of One Price, which assumes that the price of an identical good will be same throughout the world. The change in competitiveness degree, as a major factor in market exchange fluctuations, is also an important determinant in the long run. For example, if the goods of the UK market become attractive and competitive, it is quite likely to cause the exchange rate to rise. The relative strength of the currencies, balance of payments, government debt and interventions are also few of the key factors that influence the exchange rate within a given period in a given economic context (Ca’Zorzi & et. al., 2013). Additionally, the long run forecasting procedure of stock-prices becomes challenging because of the regular acquisitions and mergers in the market, the changes of the product type and the employment rates. Notably, the real income of country also differs to a substantial extent. Emphasising this particular theory, it can be affirmed that a country with rapid economic growth will have depreciating value of currency and the import will rise faster than its export, and hence, the prediction process will face challenges. The forecasting process also depends on the judgement, technical analysis and fundamental analysis procedures, increasing the level of complexities and components in the speculation affecting the accuracy of the results. It is worth mentioning in this context that the long run forecasting requires past data to understand the trend and then analyse the market, wherein, inaccuracy of recorded data may also lead to errors in the forecasted results. The changes in the money supply and the shift in the policy however does not affect the long run exchange rate (Melvin & et. al., 2013). In order to process the speculation in a systematic manner, the Vector Auto Regression (VAR) method is commonly used. The method implies a multivariate time series model. The VAR model is referred as: yt = A0 + A1yt?1 +···+ Apyt?p +?t [where yt is a vector of variables observed at time (t), A0 is a vector of constants and ?t is a vector of residuals. The parameters of the vector A0 and matrices A1 ···Ap are estimated using OLS] (Sarantis, n.d.). Conclusion The short run and long run forecasting of exchange rate, affects the macro-economic elements and financial stability of the business environment to a substantial extent. The investors are also impacted based on the allocation of resources in the business. The level of risk and the business decision of the investors altogether depend substantially on the exchange rate predictions. The misinterpretation is certainly quite likely to lead to greater damage, also due to the fact that the forecasting of the exchange rate is based much on the judgemental factor of the investor. The interest rates, the inflation of the country and the real income factors are few of the factors that tend to affect the exchange rate and the related decision of investments of the investors to a significant extent. It is worth mentioning in this context that the forecasting of the exchange rate is important when dealing with the financial shifts in the long run. The stock market, under the influence of foreign exchange, also reacts to the news or rumours within the market, which subsequently hampers the accuracy of forecasting results. It is to be noted that the short run exchange rate is based on the shift of the financial asset prices that the investor wants to hold. It is in this context that arbitrage tends to affect the financial asset pricing to a significant extent and thereby affect the investor’s decisions. Conceptually, arbitrage is referred to the process of buying and selling, which exploits the price level and helps in deterring riskless profit. Speculation in the market also affects the investor’s decision to change and affect the forecasting of the long run and short run. The importance of the exchange rate forecasting is to maintain the stability, trading of currency market and to aid the foreign investors and corporate firms to take decisions regarding the business environment. The volatility and the correlation forecasts also have considerable significance in the determination of accurate forecast results. The forecasting of the exchange rate is also challenging because of the economic differences persisting between countries along with the variances in terms of real income of the people, inflation biasness and trade barriers. These challenges affect the decision of the investor and thus restrict them from outperforming in the financial market based on the predictions. References Alter, A. L. & Oppenheimer, D. M., 2006. Predicting Short-Term Stock Fluctuations By Using Processing Fluency. PNAS, Vol. 103, No. 24, pp. 9369-9372. Andreou, S. A. & Zombanakis, A. G., 2006. Computational Intelligence in Exchange-Rate Forecasting. Working Paper, No. 49, pp. 5-44. Babazadeh, M. & Farrokhnejad, F., 2012. Effects of Short-run and Long-run Changes in Foreign Exchange Rates on Banks' Profit. International Journal of Business and Management; Vol. 7, No. 17, pp. 1-8. Bianco, M. D. & et. al., 2012. Short-Run Forecasting Of The Euro-Dollar Exchange Rate With Economic Fundamentals. BBVA, Working Papers. Ca’ Zorzi, M. & et. al., 2013. Real Exchange Rate Forecasting. European Central Bank, pp.7-28. Carlson, A. J. & et. al., 2008. Short-run Exchange-Rate Dynamics:Theory and Evidence. CREATES Research Paper, pp. 1-58. Dornbusch, R. & Krugman, P., 1976. Flexible Exchange Rate in the Short Run. Brookings Papers on Economic Activity, 3, pp. 537-584. Hopper, G. P., 1997. What Determines the Exchange Rate: Economic Factors or Market Sentiment? Exchange Rate, 17-29. Mitra, A., 2008. Forecasting Daily Spot Foreign Exchange Rates Using Wavelets And Neural Networks. Forecasting, pp. 1-30. Muck, J. & Skrzypczynski, P., 2012. National Bank of Poland. Working Paper No. 127, pp. 3-10. Melvin, M. & et. al., 2013. Forecasting Exchange Rates: An Investor Perspective. Monetary Policy and International Finance, No. 4238, pp. 1-40. Taylor, A. M. & Taylor, M. P., 2004. The Purchasing Power Parity Debate. Journal of Economic Perspectives, Vol. 18, No. 4, pp. 135–158. Sarantis, N., No Date. On the Short-Term Predictability of Exchange Rates: A BVAR Time-Varying Parameters Approach. Abstract, pp. 2-31. Read More