Quantitative Analysis Of AUD/BHT: Factors And Regression Essay.

Factors that Affect Currency Exchange Rate

Discuss about the Essentials of Business Research Methods.

The exchange rate of currency tends to depend on a host of factors and several theories and empirical studies over the years have validated the same. In the Australian context, exchange rate has immense relevance considering the importance of trade for the Australian economy. It is noteworthy that a depreciating AUD rends to assist the Australian exporters while an appreciation AUD tends to adversely impact the Australian importers (Koutsoyiannis, 2013). For the purposes of this project, Thai Baht has been chosen as the appropriate foreign currency. The reason for choosing this currency is the growing importance of trade between Thailand and Australia especially after a FTA (Free Trade Agreement) was signed between the two countries in 2009. Currently, in terms of bilateral trade Thailand is sixth largest trading partner and hence is significant for the Australian economy (AusTrade, nd). Additionally unlike the developed countries, the country has lad various economic and political issues within the last two decades which makes this a suitable choice for the given project management.

Amongst the various factors on which currency exchange rate tends to differ is the differential with regards to interest rates, inflation and GDP growth. This is because these factors tend to impact the fund flow and therefore impact the exchange rate. For instance, an increase in the interest rate I one country relative to the other, tends to increase funds flow in the country with the higher interest rate and thus potentially lead to appreciation of currency. In wake of this, the objective is to conduct a quantitative research on AUD/BHT exchange rate taking into consideration the data for interest rate (reference), inflation and GDP growth rate for a period of 20 years on a quarterly basis. Further, a regression model has been constructed using the above data so as to determine which of the variables are significant and whether the respective sign of the coefficients are in accordance with the accepted theoretical framework in this regards.

For the given project, the following data has been collected.

• Quarterly Exchange Rate (1998-2018) – This has been obtained from Datastream and is expressed as AUD/BHT where AUD represents the Australian Dollar and BHT represents Thai Baht.
• Quarterly Inflation Data for Australia (1998-2018) – This has been obtained from Australian Bureau of Statistics (ABS) through their catalogue dealing with inflation and capture the CPI or Consumer Price Index.
• Quarterly Inflation Data for Thailand (1998-2018) – This has been obtained from Bank of Thailand website and captures the CPI or Consumer Price Index.
• Quarterly Interest Rate for Australia (1998-2018) - This data is essentially the historical cash rate which has been obtained from the RBA or Reserve Bank of Australia website.
• Quarterly Interest Rate for Thailand (1998-2018) - This has been obtained from Bank of Thailand website and captures the bank rate prevalent.
• Quarterly GDP growth rate for Australia (1998-2018) - This has been obtained from Australian Bureau of Statistics (ABS) through their catalogue dealing with quarterly GDP growth.
• Quarterly GDP growth rate for Thailand (1998-2018) - This has been obtained from Bank of Thailand website and quarterly GDP growth management.

In order to utilise the above data for the regression, transformation in the data has been done. Firstly, the quarterly change in exchange rate has been computed as essentially this would serve as the dependent variable for the regression analysis. Further, the average annual change in the currency has come out to be -0.15% which highlights that during the given 20 year period, AUD on an average has depreciated against the BHT. Besides, the more risky currency is Thai Baht considering the various crisis such as 1998 currency crisis besides the global financial crisis coupled with political stability that has been witnessed in the kingdom (World Bank, 2018). The political and economic risk associated with Australia are relatively less which contributes to a more stable value for AUD.Additionally, for the macroeconomic variables, the difference between the respective rate for Australia and Thailand has been computed which tend to serve as the relevant independent variables for further regression analysis.

Choice of Thai Baht for Regression Analysis

A regression analysis has been done using the above mentioned variables. The output in this regards is indicated below.

The requisite regression equation from the above model comes out as follows.

Percentage change in AUD/BHT exchange rate = -0.384 + 0.878(Differential Inflation) + 0.154*(Differential GDP growth rate) + 0.019*(Differential interest rates)

Also, it is apparent from the above analysis that R square comes out as 0.1877.This implies that the independent variables jointly can account for only 18.77% change observed in the dependent variable i.e. currency exchange rate. Clearly, this is quite low and since about 81.23% of the change in the dependent variables is not accounted for by the above variables, hence it makes sense to insert additional variables in order to improve the model (Flick, 2015).

Besides, the individual slope coefficients also need to be analysed here which is carried out below.

• Differential Inflation – The slope coefficient of differential inflation is positive. This implies that if Australia has a higher inflation in comparison with Thailand, then there would an increase in AUD/BHT. This would imply appreciation of AUD and corresponding depreciation of BHT. This is contrary to the general economic theory where higher inflation tends to erode the value of money and hence causes depreciation of currency. Thus , in accordance with that theory, a positive inflation differential should lead to decrease in the value of AUD/BHT (Mankiw, 2013). Considering, the p value of the regression coefficient, it is apparent that at 5% significance level, the slope coefficient is significant. This implies that differential inflation is a significant independent variable for determination of percentage movement in exchange rate (Hair et. al., 2015).
• Differential GDP growth rate - The slope coefficient of differential GDP growth rate is positive. This implies that if Australia has a higher GDP growth rate in comparison with Thailand, then there would an increase in AUD/BHT. This would imply appreciation of AUD and corresponding depreciation of BHT. This is in accordance with the accepted economic theory where higher GDP growth rate is typically accepted with currency appreciation and hence this slope coefficient is in line with the expectations (Krugman and Wells, 2012). However, this variable does not seem significant considering that the associated p value with the slope coefficient is higher than 0.05 (Hillier, 2016).
• Differential interest rate - The slope coefficient of differential interest rate is positive. This implies that if Australia has a higher interest rate in comparison with Thailand, then there would an increase in AUD/BHT. This would imply appreciation of AUD and corresponding depreciation of BHT. The sign of the coefficient makes sense according to the prevalent economic theory. This is because if Australia has a higher interest rate in comparison to Thailand, hence more fund flows would be seen in Australia causing appreciation of the currency (McConnell, Brue and Flynn, 2014). However, this variable does not seem significant considering that the associated p value with the slope coefficient is higher than 0.05. One of the reasons for insignificance of this coefficient could be the use of cash and bank rates rather than the prevalent real interest rates (Flick, 2015).

Also, based on the ANOVA output, it can be concluded that the linear regression model is significant in the given case as is apparent from the significance F or p value which is lower than 0.05. The model significance is owing to the inflation differential slope being significant for the determination of the exchange rate movement (Hillier, 2016).

Conclusion

It may be summarised from the above that linear regression model does not seem to serve the purpose in the given case, This is because on one hand the sign of the differential inflation is opposite to that expected according to the macroeconomic principles while on the other, the two independent variables in the form of interest rate differential and GDP growth rate differential are not significant for the determination of percentage change in the exchange rate. Further, only a small portion of the change in the currency exchange rate is explained by the current independent variables and hence it is essential to tweak the given model to insert other relevant variables based on empirical evidence and macroeconomic theoretical framework.

Also, it can be concluded that on an average that in the given period of 20 years, the AUD has depreciated against the BHT. Having said that, the more risky currency is BHT considering the 1998 economic crisis coupled with periods of political instability that country has witnessed. However, as apparent from the GDP growth rate data, Thailand during this period on an average has grown at a rate higher than the corresponding rate for Australia. For the given regression model, a major limitation is the use of cash rate instead of the real interest rates. Also, the results may be distorted to come extent on account of exclusion of 1998-2000 period where the Thai economy was facing a severe crisis.

References

AusTrade (n.d.) Market profile [online] Available at https://www.austrade.gov.au/Australian/Export/Export-markets/Countries/Thailand/Market-profile [Accessed on April 24, 2018]

Flick, U. (2015) Introducing research methodology: A beginner's guide to doing a research project. 4th ed. New York: Sage Publications.

Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., and Page, M. J. (2015) Essentials of business research methods. 2nd ed. New York: Routledge.

Hillier, F. (2016) Introduction to Operations Research. 6th ed. New York: McGraw Hill Publications.

Koutsoyiannis, A. (2013) Modern Macroeconomics. 4th ed. London: Palgrave McMillan.

Krugman, P. and Wells, R. (2012) Macroeconomics. 3rd ed. London: Worth Publishers.

Mankiw, G. (2013) Principles of Macroeconomics. 6th ed. London: Cengage Learning.

McConnell, C., Brue, S. and Flynn, S. (2014) Macroeconomics: Principles, Problems, & Policies. 20th  ed. New York:  McGraw Hill Publications.

World Bank (2018) The World Bank in Thailand [online] Available athttps://www.worldbank.org/en/country/thailand/overview [Accessed on April 24, 2018]