Tail Dependence Analysis Between the Idx Composite and the USD/IDR Exchange Rate Using the Quantile Correlation Coefficient
Kata Kunci:
quantile correlation, tail dependence, IHSG, USD/IDR, applied statisticsAbstrak
This study examines tail dependence between the IDX Composite (IHSG) and the USD/IDR exchange rate using the Quantile Correlation Coefficient. The analysis employs cleaned daily data from 2 January 2014 to 30 December 2024. After matching trading dates and calculating log returns, the final sample comprises 2,361 return observations. To facilitate economic interpretation, the exchange-rate variable is transformed into a rupiah return, defined as the negative of the USD/IDR return; consequently, a lower rupiah return indicates rupiah depreciation. Pearson correlation is used as a benchmark, while quantile correlations are estimated at τ = 0.1, 0.5, and 0.9 to compare dependence in the lower tail, at the median, and in the upper tail. The Pearson correlation between IHSG and rupiah returns is 0.188899. The estimated quantile correlations at τ = 0.1, 0.5, and 0.9 are 0.214986, 0.153802, and 0.178168, respectively. These results indicate that the relationship between the IHSG and the rupiah is stronger in the lower tail than at the median. The study contributes to applied statistics by demonstrating the usefulness of quantile-based dependence measures for financial-risk analysis in an emerging-market context.
Referensi
[1] Indonesia Stock Exchange, "IDX Composite (Indeks Harga Saham Gabungan/IHSG)," Indonesia Stock Exchange, 2026.
[2] Bank Indonesia, "JISDOR, Non-USD/IDR Reference Rates, and Bank Indonesia Transaction Rates," Bank Indonesia, 2026.
[3] R. E. Rahman, "Understanding Indonesia's exchange rate behavior," Studies in Economics and Finance, vol. 38, no. 2, pp. 189-206, 2021.
[4] R. D. Handoyo, K. H. Ibrahim, and F. Y. Indrawan, "Stock returns response to internal and external shocks during the COVID-19 pandemic in Indonesia: A comparison study," Industrial Engineering & Management Systems, vol. 21, no. 1, pp. 85-109, 2022.
[5] G. Syamni, D. Wahyuddin, Damanhur, and C. Ichsan, "Exchange rate, stock return, and bond return in Indonesia," Jurnal Keuangan dan Perbankan, vol. 26, no. 2, pp. 394-407, 2022.
[6] B. Prananta and C. Alexiou, "Exchange rates, bond yields and the stock market: Nonlinear evidence of Indonesia during the COVID-19 period," Asian Journal of Economics and Banking, vol. 8, no. 1, pp. 83-99, 2024.
[7] N. N. Marpaung and I. R. D. Pangestuti, "Macroeconomic factors and Jakarta Stock Exchange: A comparative analysis pre- and until the COVID-19 pandemic," SAGE Open, vol. 14, no. 2, Art. no. 21582440241247894, 2024.
[8] K. Gokmenoglu, B. M. Eren, and S. Hesami, "Exchange rates and stock markets in emerging economies: New evidence using the quantile-on-quantile approach," Quantitative Finance and Economics, vol. 5, no. 1, pp. 94-110, 2021.
[9] M. Tian, R. El Khoury, and M. M. Alshater, "The nonlinear and negative tail dependence and risk spillovers between foreign exchange and stock markets in emerging economies," International Review of Financial Analysis, vol. 82, Art. no. 101712, 2023.
[10] J. Qin, X. Cong, D. Ma, and X. Rong, "Dynamic quantile connectedness between oil and stock markets: The impact of the interest rate," Energy Economics, vol. 136, Art. no. 107741, 2024.
[11] O. O. S. Yaya, M. Zhang, H. Xi, and F. Furuoka, "How do leading stock markets in America and Europe connect to Asian stock markets? Quantile dynamic connectedness," Quantitative Finance and Economics, vol. 8, no. 3, pp. 502-531, 2024.
[12] M. Ben Salem, "Quantile connectedness between stock market and macroeconomic uncertainty," International Journal of Financial Studies, vol. 13, no. 4, Art. no. 224, 2025.
[13] S. Chancharat and N. Chancharat, "Quantile connectedness analysis and portfolio strategies among crude oil, gold, exchange rate, the US stock market, traditional and sustainable indices: Evidence from Thailand," Sustainable Futures, Art. no. 101055, 2025.
[14] J.-E. Choi and D. W. Shin, "Quantile correlation coefficient: A new tail dependence measure," Statistical Papers, vol. 63, pp. 1075-1104, 2022.
[15] O. B. Adekoya and J. A. Oliyide, "How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques," Resources Policy, vol. 70, Art. no. 101898, 2021.
[16] E. Bouri, T. Saeed, X. V. Vo, and D. Roubaud, "Quantile connectedness in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, vol. 71, Art. no. 101302, 2021.
[17] T. Ando, M. Greenwood-Nimmo, and Y. Shin, "Quantile connectedness: Modeling tail behavior in the topology of financial networks," Management Science, vol. 68, no. 4, pp. 2401-2431, 2022.
[18] A. K. Tiwari, S. K. Jena, N. Trabelsi, and S. Hammoudeh, "Conditional transmission of global shocks to emerging stock markets: Evidence from the quantile connectedness network analysis," Applied Economics, vol. 54, no. 31, pp. 3621-3634, 2022.
[19] X. Su, B. Lin, and C.-C. Lee, "Quantile connectedness among fintech, carbon futures, and energy markets: Implications for hedging and investment strategies," Energy Economics, vol. 138, Art. no. 107820, 2024.
[20] S. Zhang, J. Zhang, and Y. Wang, "Multi-scale quantile connectedness networks under fintech integration," Humanities and Social Sciences Communications, vol. 12, pp. 1-17, 2025.
[21] A. Herwany, E. Febrian, M. Anwar, and A. Gunardi, "The influence of the COVID-19 pandemic on stock market returns in Indonesia Stock Exchange," Journal of Asian Finance, Economics and Business, vol. 8, no. 3, pp. 39-47, 2021.
[22] Lina, Hesniati, and Robin, "Stock return effect during COVID-19: Evidence from Indonesia," Journal of Applied Business and Economics, vol. 8, no. 3, pp. 290-301, 2022.
[23] E. Bouri, O. Cepni, D. Gabauer, and R. Gupta, "Return connectedness across asset classes around the COVID-19 outbreak," International Review of Financial Analysis, vol. 73, Art. no. 101646, 2021.
[24] L. T. N. Le, "Did COVID-19 change spillover patterns between fintech stocks and other asset classes?" Research in International Business and Finance, vol. 58, Art. no. 101441, 2021.
[25] I. A. Raifu, T. T. Kumeka, and A. Aminu, "Reaction of stock market returns to COVID-19 pandemic and lockdown policy: Evidence from Nigerian firms stock returns," Future Business Journal, vol. 7, Art. no. 35, 2021.
[26] S. A. Athari, "Time-frequency return co-movement among asset classes around the COVID-19 outbreak: Portfolio implications," Journal of Economics and Finance, vol. 46, pp. 736-756, 2022.
[27] S. A. Nusair and J. A. Al-Khasawneh, "On the relationship between Asian exchange rates and stock prices: A nonlinear analysis," Economic Change and Restructuring, vol. 55, no. 1, pp. 361-400, 2022.
[28] I. Chatziantoniou, D. Gabauer, and A. Stenfors, "Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach," Economics Letters, vol. 204, Art. no. 109891, 2021.
[29] C. Cornell, L. Mitchell, and M. Roughan, "Enhancing causal discovery in financial networks with piecewise quantile regression," arXiv preprint arXiv:2408.12210, 2024.
[30] R. Romieo, H. Hesniati, and Robin, "Stock return during Pandemic Covid-19: A comparison between Indonesia and Singapore stock market," Jurnal Ekonomi Modernisasi, vol. 18, no. 1, pp. 1-12, 2022.
[31] Y.-H. Yang, Y.-H. Shao, and W.-X. Zhou, "Quantile connectedness across BRICS and international grain futures markets: Insights from the Russia-Ukraine conflict," arXiv preprint arXiv:2409.19307, 2024.
[32] T. Adrian and M. K. Brunnermeier, "CoVaR," American Economic Review, vol. 106, no. 7, pp. 1705-1741, 2016.
[33] Aulele, S. N., Ilwaru, V. Y. I., Wuritimur, E. R., & Matdoan, M. Y. (2021). Analisis Jumlah Penduduk Miskin di Provinsi Maluku dengan Menggunakan Pendekatan Regresi Spasial. Jurnal Aplikasi Statistika & Komputasi Statistik, 13(2), 23-34.
[34] R. Koenker, Quantile Regression. Cambridge, U.K.: Cambridge University Press, 2005.
[35] Muhammad, I., Lesnussa, Y. A., Patty, H. W. M., Van Delsen, M. S. N., & Matdoan, M. Y. (2020). Peramalan jumlah mahasiswa baru menggunakan metode double exponential smoothing (studi kasus: mahasiswa baru Universitas Pattimura ambon tahun 2017). Variance, 2(1), 27-33.
