Rate of Exchange - Essay Example

RATE OF EXCHANGE Location Rate of Exchange Table of contents Research Questions 6 Results 15 The followingtable show a regression for Qatar bank and Qatar SE. 15 Spectral Analysis for Qatar bank 17 Sequence plot 24 References 25 Rate of Exchange Introduction The rate of exchange is of crucial consideration when it comes to the performance of companies. This importance witnessed in the macroeconomic environment demands quite a lot in return. The cash flows and values of equity run based on the given rate of exchange. The translation of the risk exposure is presently a matter of great importance to a company. The need to measure the foreign exchange is a huge task in terms of the international environment. Several studies show that the rate of exchange can be understood using the regression exchange rates on the prices of stock (John, 1990). Other researchers say that the investigation of the exchange rate will not reflect the Stock market because the variety of macroeconomic channels is key operation. Basing on such ideas, the search for risk trade basing on various data will add to the risk management of several companies. There are several types of regression. The OLS type of regression has both the indicator foe exchange rates such as the United States Dollar, Euro, Japanese Yen, Chinese Yuan, and Great Britain Pound. The regression came into effect after DKK, and the statistical software failed to get a meaningful exposure rate of exchange. The mentioned five rates of exchange show that the Qatar Commercial Bank and Masraf Alrayan Company are in the same pipeline of the same market rate of exchange to one of the five rates. The findings show that these two companies having only one rate of exchange sign fail to get a substantial additional market environment. The return of finances happens to depend on several factors such as the variance and flexibility of movements of the prices in markets. The name is Volatility in terms of econometrics. With precise intervals in finance, the variance will have a value at wider intervals in periods not satisfactory and at narrower intervals in good years. Garch models invented by Bollerslev in 1986 model the conditional variances correctly to an adequate level. The regression is such that the results show improved findings from four companies basing on the OLS way of doing things. The eight companies that the Garch method uses to get the additional market rate of exchange help one understand the Garch way of analysis. The implication is that the latter method shows a fair evident regression of financial analysis. For one to get a structural change in a given period of 10 years, the monthly data was in use. Determination of the coefficients for regression was necessary in the understanding the operation of the rate of exchange and its effect on the companies. The software used was SPSS, which helped in the provision of regression coefficients and provided a good understanding of the two groups in term of the rate of exchange is of considerable effect on the performances of companies. The rates of exchange had several currencies such as the Dirham of Doha, which is a city in Qatar. The companies were banks whose inputs, outputs, price indexes a were found to get a comfortable understanding of the effects of rate of exchange on the performance of Qatar Commercial Bank and Masraf Alrayan Company. The two companies are from Qatar. One is financial, and the other is nonfinancial. They are Qatar Commercial Bank and Masraf Alrayan Company majorly. These are the two sub-companies. Other banks used are Doha and Qatar Islamic Bank. The parameters involved are opening price; total assets and price index of the banks for example Doha Bank. Others are the stock rate of return. The code used is CBQ (PO), CBQ (O), DHQ (PO), and CBQ (O). The letter O stands for Output, Li for the price index, and PO for the opening price. CB is a short term for Commercial bank, and DHB is for Doha Bank. The total assets have a code of WC (02999). Pick up a sample of companies from a particular country. The sample should include two sub-samples: the first is financial and the second is nonfinancial. As financial companies have different characteristics, please analyze them separately. The required data should include: monthly share prices, national stock market index, and the firm exchange rate rates. The data should cover the period of at least the last ten years on a monthly basis. Research Design The design of research in this document and study is in line with deductive reasoning. The way of solving the challenge begins with cooperate finance theory using it to guide empirical analyzes. The research design is in Figure 1. There is a broad view of understanding the standard practices used in the examination of the foreign exchange exposure. The full knowledge of foreign exchange rate has a foundation which when got, this thesis will undertake an individual opinion getting its focus into units of analysis while taking into concern methods used in the hypothesis. The choice of which way to take heavily depends on more knowledge in the mind of standard procedures which normally are hard to get in a good way of understanding. As for the analysis, the hypothesis undergoes testing to get the rate of exchange in Qatar especially in the banking sector where several banks such as the Doha and Qatar commercial banks are. Figure 1: Research design. Research Questions The primary objective of the study was to show how the rate of exchange affects the performance of companies. In this case, the study concentrated on several sub sample such as Qatar Commercial Bank and Masraf Alrayan Company, Qatar Islamic to mention a few in order to get the following answered; 1. What is the estimate of the exposure of the two companies in terms of the rate risk in ten years? 2. Discuss the results and comment on the exposure coefficients as compared to the field? 3. What is the effect of firm size on the foreign exchange exposure? 4. How does hedging increase firm value? 5. Show empirical evidence on the exchange rate? Literature Review The primary concern in companies is the rate of exchange of companies. The issue here is that a company’s financial reputations and management are at stake given the rate of exchange of the company is not doing well. Over the years, software such as Spss has come up to provide an insight into the life of the companies financially basing in the how the companies perform on the rate of exchange in a given period. One such instance is the fact-floating rate affect the cash flow of a business so seriously that there is a risk of exposure developed in the end. The result was the development of much research in this area in order to come to a better understanding into the way the companies operated financially. Researchers came up with a way to have an improved rate of exchange in the end basing on results got from software such as Spss. Past researchers of statistics came up with regression results of several United States companies that were on offer for study. There were time series that helped to get several coefficients. Where Rit is the Rate of Return on Common Stock, Rst = change in trade-weighted exchange. The result showed significant trends for few companies that were under study intensively (Jorion, 1990). There was a model developed to understand the risk of exchange in terms of sensitivity for four hundred United States multinational. The study shows that about sixty percent of the firms examined had a significant risk of exposure. There was a variation in the risk of exchange in terms of the specification of operating variables (Prasad, 1995). The rate of exchange for exposure for thirty United Sates bank holding companies and one hundred and ten for Japanese banks had both daily and monthly data. The coverage had results that are more significant for data done on monthly basis. The reason was that the number of the examined banks increased from five to nine in the case of the United States. For Japan, the increment was from eight to nine for daily data with a five percent level of significance. The suggestion was that daily data was less appropriate to test for sensitivity (Choi, 1997). The rate of exchange has a statistical significance. The value increases for longer returns in the horizon where one hundred and ninety out of a possible two hundred and thirteen United States multinationals in a given sample prefer a sixty-month horizon. Large firms moderately exposed in a positive way are present as compared to small firms that lie in the negative exposure in terms of rate of exchange given and present time horizons (Chow, 1997). Currently, there is literature mostly on United States empirical outputs in terms of regression that shows the rates of exchange. There was a sample of one hundred and seventy-one Japanese multinational companies that showed only twenty-five percent of the firms were in the positive sector for exposure from January 1979 to December 1993. Independent firms with stronger financial limits to hedge happen that less exposure to changes in rates of exchange occurs for them. The measurement of the rate of exchange exposure at the level of companies is possible with for a single trade indicator and several exchanges for Danish non-financial entities. If there is one business sign in the regression (usually called DKK), significant exchange rates can fail. On the opposite side of this, is the fact that analysis for Garch regression having several rates improves the finding out of risk exposure for Danish blue chip entities (Tom, 1999). The investigation for the overall exchange exposure for the Australian market had a sample period from 1988 to 1998. The period was often years. There were companies that played significant roles in the development, and so whose market and financial standings were of the great importance of the study made back then in time. The Australian stocks got to the bottom line because of lagged responses as compared to the contemporaneous rates. This market is so much into the Australian Dollar– United States movements than the Australian Dollar - Japanese Yen. There are high exchange rates in terms of cross-sectional parameters in the market (Faff, 2001). Another study made in Europe for one hundred and eighteen companies, thirteen percent of the companies used the Japanese Yen, fourteen percent for United States Dollar, and twenty-two percent for United Kingdom. There was a suggestion that the exchange rate is not so important taken together by understanding single firms having different European interests. Short-term risk exposure is efficient in hedging (Muller, 2006). Data Description and Methodology Data description The rate of exchange for nonfinancial Masraf Alrayan company is in the excel sheets. The financial company is Qatar Commercial Bank. The study starts from 1st January 2004 to 3rd January 2015, a period of slightly more than ten years. The stock rate of return was -1 on 1st January 2014 to -0.014 for 3rd January 2015. The opening price started from 38.08 Dirham on 3rd January 2004 to 69.3 Dirham on 3rd January 2015. The price index on the first day of study is 3946.7 to 12450.3 on the last day. The marketing rate of return = (New price index - Old price index) / Old price index. The first value was 0.0300344 as of 2nd January 2004 to -0.014 on 3rd January 2015. So it interesting how companies from Qatar especially the banks have different business operations. The banks use their financial strength so well that they happen to be in the primary index due to their massive turnover as banks in Qatar. The banks have significant international activities in terms of finance meaning that they have exposure to exchange rate risk as they operate internationally. The results give implications for the exchange rate of Qatar in terms of Stock process. Methodology The method will involve an analytical approach showing an empirical study of the foreign rate of exchange in Qatari banks and companies. There was the use of Spss software in the analysis of the data. Several coefficients were present. Objective one A time series regression model based on the national market index as a control variable provided the estimate for the exposure for a period of ten years. The tables are below in the section for Results. Objective Two Spitting the result as obtained to see the effect of firm size on the exchange rate occurred. The firm size reduced from 120 people to 50 people, rate of exchange was small, and so the companies in consideration had a smaller rate of exchange and a higher risk of exposure because the labour was less. Method The method used to achieve the result of the study is in line with the foreign exchange rate. There is a two-stage regression analysis. Time series is the first one. Next is the cross-sectional regression. The time series developed by Adler and Dumas in 1984 termed exposure as the effect of rates of exchange movements on the worth of an entity after controlling the gains of the portfolio of the market. Since the value of the company already has exchange rates, the method throws light on the effect of the exchange rate and the worth of the company. Cross-sectional regression is the second type of regression that considers the estimated exposure above the first regression method. The explanatory variables apply specifically to the company and severely affect the exposure. Time series With help of OLS, the following time series worked for the exchange rates: Where Rit is the Rate of return on Common stock, Rst = change in trade weighted exchange. With help of OLS, the following time series worked for the exchange rates: Rit is the Common stock, Rst = change in trade-weighted exchange. The reduction of residual variances of regression is due to the inclusion of the market portfolio. The measurement adds value to the exposure in order to get a good interpretation of the exposure. The unexpected changes in the rates of foreign exchange have deviations of the realized rates as compared to the forward rates. Based on Johnsons arguments, the study had returns on the rates of exposure. Nonlinear Exposure As clearly, seen above, there is a relationship between the fluctuations and the gains on the prices of the stock is linear. The primary parameter and overall display may not have to be direct and correct but, an argument arises. Consider the Figure 2 for illustration. Figure 2: Linear and Nonlinear foreign exchange exposure. (Source: Priestly, 2005). The line that is straight and black in Figure 2 shows the linear relationship between the appreciation and depreciation. The hyperbola will give the same meaning when it comes to a nonlinear relationship between the two variables. There are two lines that have an intersection at the zero point. For one to estimate the exposure of the way of exchange there has to be a slope similar to the one for nonlinear exposure around the zero mark. To put it better, when the rate of exchange movements are few, both the linear and nonlinear way give the same outputs. The result is nonlinear way matters too. There are convex exposure files that are significantly more important than their concave counterpart in terms of statistics. Garch Method There are several important features I the financial time series, but one such important one is the Heteroskdasticity. The point here is that the ever-constant variance undergoes rejection to get the favoured volatility clustering. The OLS does not in any way become invalid because of the volatility difference, but a better estimator comes in play. Conditional Heteroscedasticity is not under consideration by most studies (Koutmos, 2003). For one to handle the variances of time, a GARCH (1, 1) unique method applies. The choice of one such method in statistics has much support from various studies based on the financial time series. The studies show the importance of such an approach in the modelling phase. The method is also present in another form called the ARCH model. The most interesting feature of the GARCH process is the fact there is avoidance of significant number estimation for coefficients into a higher order. (Verbeek, 2008). Cash Flow The stock market is questionable in terms of statistics. The drawback is that manager’s focus on cash flows instead of market values. The cash flows are independent variables. The theory of finance in this approach is that evaluation gives the theoretical sense and the fact that effects of the exchange rate is independent of the view of the market’s participation. Results The following table show a regression for Qatar bank and Qatar SE. Table 1: Squares for QATAR SE. Model Sum of Squares df Mean Square F Sig. 1 Regression 2795.374 1 2795.374 15.124 .003a Residual 1848.292 10 184.829 Total 4643.667 11 a. Predictors: (Constant), Qatar SE b. Dependent Variable: Qatar bank Table 2:Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .776a .602 .562 13.595 a. Predictors: (Constant), Qatar SE Table 3:Squares for Qatar bank Model Sum of Squares df Mean Square F Sig. 1 Regression 2795.374 1 2795.374 15.124 .003a Residual 1848.292 10 184.829 Total 4643.667 11 a. Predictors: (Constant), Qatar SE b. Dependent Variable: Qatar bank Table 4:Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.274 6.245 .364 .723 Qatar SE .761 .196 .776 3.889 .003 a. Dependent Variable: Qatar bank The following tables show regression for Qatar bank and Islamic Holding Table 5:Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 Islaimc Holdinga . Enter a. All requested variables entered. b. Dependent Variable: Qatar bank Table 7: Model for Islamic Holding Model Sum of Squares df Mean Square F Sig. 1 Regression 757.784 1 757.784 1.950 .193a Residual 3885.883 10 388.588 Total 4643.667 11 a. Predictors: (Constant), Islaimc Holding b. Dependent Variable: Qatar bank Table 8: Qatar bank Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 10.077 9.770 1.031 .327 Islaimc Holding .246 .176 .404 1.396 .193 a. Dependent Variable: Qatar bank Spectral Analysis for Qatar bank The following spectra were from Spss for Qatar bank. Table 9: Model Description Model Name MOD_1 Analysis Type Univariate Series Name 1 Qatar bank Range of Values Reduced by Centering at Zero Periodogram Smoothing Spectral Window Tukey-Hamming Window Span 5 Weight Value W(-2) 1.460 W(-1) 2.015 W(0) 2.240 W(1) 2.015 W(2) 1.460 Applying the model specifications from MOD_1 Figure 4:Periodiogram of bank Frequency. Table 10: Model Description for Ahli bank Model Name MOD_2 Serie s Name 1 Ahli bank 2 YEAR, not periodic 3 Qatar National bank Transformation Natural logarithm Non-Seasonal Differencing 0 Seasonal Differencing 0 Length of Seasonal Period No periodicity Range of Lags From -8 To 8 Display and Plot All lags Applying the model specifications from MOD_2 Table 11: Case Processing Summary for AHLI BANK Series Length 12 Number of Excluded Cases Due to Negative or Zero Value Before Log Transform 0 User-Missing Value 0 System-Missing Value 0 Number of Valid Cases 12 Number of Computable Zero-Order Correlations After Differencing 12 Table 12:Cross Correlations Series Pair:Ahli bank with YEAR, not periodic Lag Cross Correlation Std. Errora -8 -.292 .500 -7 -.158 .447 -6 -.243 .408 -5 -.084 .378 -4 -.198 .354 -3 -.045 .333 -2 .249 .316 -1 .449 .302 0 .708 .289 1 .594 .302 2 .465 .316 3 .327 .333 4 .138 .354 5 .140 .378 6 -.093 .408 7 -.094 .447 8 -.389 .500 a. Based on the assumption that the series are not cross-correlated and that one of the series is white noise. Figure 5: Ahli bank lag number Table 13: Ahli bank with Qatar National Cross Correlations Series Pair:Ahli bank with Qatar National bank Lag Cross Correlation Std. Errora -8 -.281 .500 -7 -.036 .447 -6 -.206 .408 -5 .165 .378 -4 -.179 .354 -3 .127 .333 -2 .334 .316 -1 .480 .302 0 .780 .289 1 .342 .302 2 .256 .316 3 -.051 .333 4 -.076 .354 5 -.185 .378 6 -.118 .408 7 -.397 .447 8 -.288 .500 a. Based on the assumption that the series are not cross-correlated and that one of the series is white noise. c Figure 7: Lag number for Ahli bank not periodic with Qatar National Sequence plot Table 14: Qatar Int. Islamic Model Description Model Name MOD_4 Series or Sequence 1 Qatar Int. Islamic Transformation None Non-Seasonal Differencing 0 Seasonal Differencing 0 Length of Seasonal Period No periodicity Horizontal Axis Labels YEAR, not periodic Intervention Onsets YEAR, not periodic=2004 Reference Lines None Area Below the Curve Not filled Applying the model specifications from MOD_4 Table 15: Case Processing Summary Qatar Int. Islamic Series or Sequence Length 12 Number of Missing Values in the Plot User-Missing 0 System-Missing 0 Figure 8: Sequence plot for Qatar Int. Islamic. References Amihud, Y., & Levich, R. M. (2003). Exchange Rates and Corporate Performance. Washington, D.C., Beard Books. Birds, J. (2010). Annotated companies legislation. Oxford, Oxford University Press. Brillinger, D. R. (2001). Time series: data analysis and theory. Philadelphia, Pa, Society for Industrial and Applied Mathematics. Cao, L., & Soofi, A. S. (2002). Modelling and forecasting financial data: techniques of nonlinear dynamics. Boston, Mass. [u.a.], Kluwer Academic Publ. Copeland, L. S. (2008). Exchange rates and international finance. Harlow, England, Prentice Hall / Financial Times. Gagnon, J. E., & Hinterschweiger, M. (2011). Flexible exchange rates for a stable world economy. Washington, DC, Peterson Institute for International Economics. Galitz, L. (2013). Financial times handbook of financial engineering using derivatives to manage risk. Harlow, U.K., Pearson Education. Ghatak, S., & SáNchez-Fung, J. R. (2007). Monetary economics in developing countries. Goodhart, C. A. E., & Mizen, P. (2003). Monetary history, exchange rates and financial markets essays in honour of Charles Goodhart ; volume two. Cheltenham, UK, Edward Elgar. Mills, T. C. (1990). Time series techniques for economists. Cambridge [England], Cambridge University Press Salas, J., Delleur, J., & Yevjevich, V. (1988). Applied modelling of hydrologic time series. Littleton, Colorado, Water Resources Publications. Schaefer, H. G. (1995). International economic trend analysis. 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