Exchange rates within an economy affect international trade as they influence the price of goods and services sourced from another country and the attractiveness of the local produce to international consumers. This brings forth an interdependence relationship between exchange rates and market value of share products listed in Security Exchange Markets. This study evaluates the dependence structure between extreme exchange rates of the Kenyan Shilling against the US Dollar and the Nairobi Securities 20 price index using archimedean copulas. The Peak Over Threshold method was used to determine extreme values of the daily log returns of the KSH/USD exchange rate whose dependence structure was analyzed against the NSE20 price index. Parameter Estimation was via the Maximum log-Likelihood Estimation technique. Descriptive statistics showed that the minimum and maximum Ksh/Usd exchange rates were at Ksh. 79.44 and Ksh. 116.07 respectively. The highest NSE 20 price index was at Ksh.5500 with the lowest value of Ksh. 1724. This study found a negative correlation between Ksh/Usd extreme exchange rate data and the NSE20 price index. The Clayton copula was found as the best archimedean copula in modeling the dependence structure as it had the lowest standard error and a parameter estimate close to zero.
Published in | International Journal of Data Science and Analysis (Volume 9, Issue 3) |
DOI | 10.11648/j.ijdsa.20230903.11 |
Page(s) | 50-59 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Extreme Value, Copula Analysis, Exchange Rates, NSE20 Price Index, Clayton Copula, Archimedean Copula
[1] | Bank for International Settlements. (2019). Triennial Central Bank Survey of Foreign Exchange and Over-the- counter Derivatives Markets in 2019. |
[2] | T. Aven. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research 253 (1): 1-13. |
[3] | N. Mwendwa, J. Omagwa, and L. Wamugo. (2021). Systematic risk and performance of stock market in Kenya. International journal of research in business and social science 10 (4): 2147-4478. doi: 10.20525/ijrbs.v10i4.1180. |
[4] | E. Kitati, Z. Evusa, and H. Maithya. (2015). Effect of Macro-Economic Variables on Stock Market Prices for the Companies Quoted on the Nairobi Securities Exchange in Kenya. International Journal of Sciences:Basic and Applied Research 21 (2): 235-265. |
[5] | S. K. Tristan, H. Reinout, and S. C. Michael.(2022). Modeling Production Efficiency and Greenhouse Gas Objectives as a Function of Forage Production of Dairy Farms Using Copula Models. Environment Model Assess (15): 1-16. DOI:10.1007/s10666-021-09812-3. |
[6] | G. Zofia, G. M., K. Danuta, S. Anna, S. Jakub, K. Maciej, N. Marcin, and K. Joanna. (2019). Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function. Sustainability, 2019 15 (11): 1-14. DOI: 10.3390/su11154144. |
[7] | Q. Li, G. Deng, and X. Tan. (2019). Analysis of the Dependence of Stock Risk Based on Copula Theory. Journal of Financial Risk Management 8 (4). DOI: 10.4236/jfrm.2019.84015. |
[8] | J. B. Kamal and A. E. Haque. (2016). Dependence Between Stock Market and Foreign Exchange Market in South Asia, A Copula-Garch Approach. The Journal of Developing Areas 50 (1): 175-194. |
[9] | R. A. Fisher and L. H. C. Tippett. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Mathematical Proceedings Of The Cambridge Philosophical Society 24 (2): 180-190. DOI: 10.1017/S0305004100015681. |
[10] | A. Balkema and D. L. Haan.(1974). Residual Life Time at Great Age. The Annals of Probability 2 (5): 792-804. DOI: 10.1214/aop/1176996548. |
[11] | J. Pickands. (1975). Statistical inference using extreme order statistics. The Annals of Probability 3 (1): 119-131. DOI: 10.1214/aos/1176343003. |
[12] | A. Sklar. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications del’Institut de Statistique de l’Université de Paris 229-231. |
[13] | T. Kularatne, J. Li, and D. Pitt. (2021). On the use of Archimedean copulas for insurance modelling. Annals of Actuarial Science 15 (1): 57-81. DOI: 10.1017/S1748499520000147. |
[14] | M. D. Smith. (2003). Modelling Sample Selection Using Archimedean Copulas. The Econometrics Journal 6 (1): 99-123. http://www.jstor.org/stable/23113651. |
[15] | H. Cramer. (1928).On the Composition of Elementary Errors. Scandinavian Actuarial Journal. 1: 13-74. DOI: 10.1080/03461238.1928.10416862. |
[16] | R. V. Mises. (1928). Wahrscheinlichkeit, Statistik und Wahrheit. Julius Springer 3. DOI: 10.1007/978-3-662- 41863-5. |
[17] | A. Kolmogorov. (1933). Sulla determinizione empirica di una legge di distribuzione. Giornale dell’Istituto Italiano degli Attuari 4: 83-91. |
[18] | V. O. Andreev, S. E. Tinykov, O. P. Ovchinnikova, and G. P. Parahin. (2012) Extreme Value Theory and Peaks Over Threshold Model in the Russian Stock Market. |
[19] | P. Osei and M. Anokye. (2020). Copula-Based Assessment of Co-Movement and Tail Dependence Structure Among Major Trading Foreign Currencies in Ghana. Risks 8 (2): 1-20. DOI: 10.3390/risks8020055. |
APA Style
Rosemary Wanjiru Ng’ethe, Thomas Mageto, Joseph Mungatu. (2023). Copula Analysis of Dependencies Between Extreme Exchange Rates and NSE20 Price Index. International Journal of Data Science and Analysis, 9(3), 50-59. https://doi.org/10.11648/j.ijdsa.20230903.11
ACS Style
Rosemary Wanjiru Ng’ethe; Thomas Mageto; Joseph Mungatu. Copula Analysis of Dependencies Between Extreme Exchange Rates and NSE20 Price Index. Int. J. Data Sci. Anal. 2023, 9(3), 50-59. doi: 10.11648/j.ijdsa.20230903.11
AMA Style
Rosemary Wanjiru Ng’ethe, Thomas Mageto, Joseph Mungatu. Copula Analysis of Dependencies Between Extreme Exchange Rates and NSE20 Price Index. Int J Data Sci Anal. 2023;9(3):50-59. doi: 10.11648/j.ijdsa.20230903.11
@article{10.11648/j.ijdsa.20230903.11, author = {Rosemary Wanjiru Ng’ethe and Thomas Mageto and Joseph Mungatu}, title = {Copula Analysis of Dependencies Between Extreme Exchange Rates and NSE20 Price Index}, journal = {International Journal of Data Science and Analysis}, volume = {9}, number = {3}, pages = {50-59}, doi = {10.11648/j.ijdsa.20230903.11}, url = {https://doi.org/10.11648/j.ijdsa.20230903.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20230903.11}, abstract = {Exchange rates within an economy affect international trade as they influence the price of goods and services sourced from another country and the attractiveness of the local produce to international consumers. This brings forth an interdependence relationship between exchange rates and market value of share products listed in Security Exchange Markets. This study evaluates the dependence structure between extreme exchange rates of the Kenyan Shilling against the US Dollar and the Nairobi Securities 20 price index using archimedean copulas. The Peak Over Threshold method was used to determine extreme values of the daily log returns of the KSH/USD exchange rate whose dependence structure was analyzed against the NSE20 price index. Parameter Estimation was via the Maximum log-Likelihood Estimation technique. Descriptive statistics showed that the minimum and maximum Ksh/Usd exchange rates were at Ksh. 79.44 and Ksh. 116.07 respectively. The highest NSE 20 price index was at Ksh.5500 with the lowest value of Ksh. 1724. This study found a negative correlation between Ksh/Usd extreme exchange rate data and the NSE20 price index. The Clayton copula was found as the best archimedean copula in modeling the dependence structure as it had the lowest standard error and a parameter estimate close to zero. }, year = {2023} }
TY - JOUR T1 - Copula Analysis of Dependencies Between Extreme Exchange Rates and NSE20 Price Index AU - Rosemary Wanjiru Ng’ethe AU - Thomas Mageto AU - Joseph Mungatu Y1 - 2023/11/01 PY - 2023 N1 - https://doi.org/10.11648/j.ijdsa.20230903.11 DO - 10.11648/j.ijdsa.20230903.11 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 50 EP - 59 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20230903.11 AB - Exchange rates within an economy affect international trade as they influence the price of goods and services sourced from another country and the attractiveness of the local produce to international consumers. This brings forth an interdependence relationship between exchange rates and market value of share products listed in Security Exchange Markets. This study evaluates the dependence structure between extreme exchange rates of the Kenyan Shilling against the US Dollar and the Nairobi Securities 20 price index using archimedean copulas. The Peak Over Threshold method was used to determine extreme values of the daily log returns of the KSH/USD exchange rate whose dependence structure was analyzed against the NSE20 price index. Parameter Estimation was via the Maximum log-Likelihood Estimation technique. Descriptive statistics showed that the minimum and maximum Ksh/Usd exchange rates were at Ksh. 79.44 and Ksh. 116.07 respectively. The highest NSE 20 price index was at Ksh.5500 with the lowest value of Ksh. 1724. This study found a negative correlation between Ksh/Usd extreme exchange rate data and the NSE20 price index. The Clayton copula was found as the best archimedean copula in modeling the dependence structure as it had the lowest standard error and a parameter estimate close to zero. VL - 9 IS - 3 ER -