International Banking and Finance Market Volatility and Learning

Question: Describe about the International Banking and Finance for Market Volatility and Learning. Answer: The daily FX rate changes have been recorded for China (CNY), Europe (EUR), Japan (JPY), Malaysia (MYR), New Zealand (NZD) and USA (USD) The calculation of the Average return, Standard deviation and Value At Risk of 95% confidence level has been calculated in the table below Average 5.8 0.7 87.7 3.0 1.2 0.9 Standard deviation 0.8 0.1 6.9 0.1 0.1 0.1 Value At Risk 6.8 1916.2 1816.4 0.9 0.0 6.3408 Annual Average return depicted above is highest in case of Japanese Yen which is given as 87.7, which implies that percentage used in reporting the daily FX rate form of the four year. Standard Deviation from the given calculation is 6.9 which is also in case of Japanese Yen which implies that risk in case of Japanese Yen is the highest, which further says that If the investment is done in the Japanese Yen the person will be facing a high degree of risk. Moreover, in Europe, Malaysia, New Zealand and USA currencies have very less risk. Someone can think of investing on that currency easily as people only goes for those investments whose risk will be less. In the view of Correlation, it can be said that there is negative relationship existing in case Europe and Japan as well as Japan and Malaysia which says that these two currency do not depend on one another, whereas rest of the Correlations are positive which says that they are depending on one another. The highest FX rate is 150 JPY in 12 April 2013 and lowest FX rate is 0.6169 EUR in 21 September 2015. VAR represents or measure risk of volatility of the given currency. One of the most important issues with the volatility is that it does not care about the direction of the investment. VAR of European currency is coming to be highest so at 95% of the confidence level EUR has the highest risk. Reference List: Adam, K., Marcet, A. and Nicolini, J.P., 2016. Stock market volatility and learning.The Journal of Finance,71(1), pp.33-82. Chkili, W. and Nguyen, D.K., 2014. Exchange rate movements and stock market returns in a regime-switching environment: Evidence for BRICS countries.Research in International Business and Finance,31, pp.46-56. Cohen, J., Cohen, P., West, S.G. and Aiken, L.S., 2013.Applied multiple regression/correlation analysis for the behavioral sciences. Routledge. Della Corte, P., Ramadorai, T. and Sarno, L., 2016. Volatility risk premia and exchange rate predictability.Journal of Financial Economics,120(1), pp.21-40. Gravetter, F.J. and Wallnau, L.B., 2016.Statistics for the behavioral sciences. Cengage Learning. Gyntelberg, J., Loretan, M., Subhanij, T. and Chan, E., 2014. Exchange rate fluctuations and international portfolio rebalancing.Emerging Markets Review,18, pp.34-44. Simmons, B.E., 2014. Average rate of change.Mathwords: Terms and Formulas. Tong, Y.L., 2012.The multivariate normal distribution. Springer Science Business Media. Willick, K., Storer, B. and Wesolkowski, S., 2013, June. A new principal curve algorithm and standard deviation clouds for non-parametric ordered data analysis. In2013 IEEE Congress on Evolutionary Computation(pp. 1459-1466). IEEE. Zhang, S., Liu, J., Kato, N., Ujikawa, H. and Suzuki, K., 2015, June. Average rate analysis for a D2D overlaying two-tier downlink cellular network. In2015 IEEE International Conference on Communications (ICC)(pp. 3376-3381). IEEE.