ANALISIS VOLATILITAS SAHAM BANK DI INDONESIA DENGAN MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS (PCA)
DOI:
https://doi.org/10.71277/sv060730Abstract
Penelitian ini mengkaji return volatility lima saham bank di Indonesia, yaitu BBCA, BBNI, BBRI, BMRI, dan BJBR menggunakan alaisis korelasi dan analisis Komponen Utama (PCA) untuk mengetahui korelasi dan struktur varians pada data saham perbankan. Hasil penelitian menunjukan return saham BBCA, BBNI, BBRI, BMRI stabil mengikuti tren volatilitas dalam kisaran -0,1 hingga 0,1 sedangkan BJBR lebih volatil. Saham BBCA, BBNI, BBRI, dan BMRI berkorelasi tinggi dengan nilai korelasi diatas 0,6. Korelasi BJBR 0,4 dengan saham-saham bank besar lainnya di bawah 0,4. Hasil Principal Component Analysis (PCA) menunjukkan bahwa komponen PC1 berpengaruh signifikan sebesar 66,04% terhadap trend keseluruhan pasar saham perbankan. PC2 sebagai faktor khusus untuk BJBR, berbeda secara signifikan dari yang lain dengan nilai PC2 yang tinggi 0,95. Hal ini menunjukkan bahwa BJBR memiliki komponen khusus yang tidak terkait dengan saham bank besar lainnya, seperti atribut bisnis yang lebih lokal atau fokusnya yang berbeda dalam strategi perbankan.
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References
Ali, M., Bouri, E., & Roubaud, D. (2022). Dynamic spillovers between the COVID-19 pandemic and financial markets: Evidence from time-frequency analysis. Finance Research Letters, 45, 102123.
Baele, L. (2005). Volatility spillover effects in European equity markets. Journal of Financial and Quantitative Analysis, 40(2), 373-401.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Bouri, E., Jain, A., Roubaud, D., Kristoufek, L., & Lucey, B. (2021). Cryptocurrencies as hedge and safe-haven for international stock markets: A multivariate GARCH approach. Economic Modelling, 94, 335-346.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
Engle, R., & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. NBER Working Paper No. 8554.
Mishkin, F. S. (2019). The Economics of Money, Banking, and Financial Markets. Pearson.
Yarovaya, L., Elsayed, A. H., & Hammoudeh, S. (2022). Stock market contagion during the COVID-19 pandemic: A dependence network and transfer entropy framework. International Review of Financial Analysis, 81, 102064.
Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36, 101528.
Connor, G., & Korajczyk, R. A. (1993). A test for the number of factors in an approximate factor model. Journal of Finance, 48(4), 1263-1291.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. Journal of Finance, 57(5), 2223-2261.
Jolliffe, I. T. (2002). Principal Component Analysis. Springer.
Kenourgios, D., Samitas, A., & Paltalidis, N. (2011). Financial crises and stock market contagion in a multivariate time-varying asymmetric framework. Journal of International Financial Markets, Institutions and Money, 21(1), 92-106.
Mensi, W., Hammoudeh, S., Reboredo, J. C., & Nguyen, D. K. (2017). Do global factors impact BRICS stock markets? A quantile regression approach. Emerging Markets Review, 31, 54-68.
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