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dc.contributor.authorRorimpandey, Rebecca
dc.contributor.authorNugroho, Didit B.
dc.contributor.authorSusanto, Bambang
dc.date.accessioned2019-07-10T01:48:17Z
dc.date.available2019-07-10T01:48:17Z
dc.date.issued2019-03
dc.identifier.citationAbdalla, S. Z. S., & Winker, P. (2012). Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(8), 161–176. https://doi.org/10.5539/ijef.v4n8p161 Ahmed, R. R., Vveinhardt, J., Streimikiene, D., & Channar, Z. A. (2018). Mean reversion in international markets: evidence from G.A.R.C.H. and half-life volatility models. Economic Research-Ekonomska Istraživanja, 31(1), 1198–1217. Alexander, C. (2008). Market risk analysis II: Practical financial econometrics. Wiley. Chichester: John Wiley & Sons. Atchade, Y. F., & Rosenthal, J. S. (2005). On adaptive Markov chain Monte Carlo algorithms. Bernoulli, 11(5), 815–828. Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of Econometrics, 52(1–2), 5–59. Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological). Wiley. Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury. Chen, M.-H., & Shao, Q.-M. (1999). Monte carlo estimation of Bayesian credible and HPD intervals. Journal of Computational and Graphical Statistics, 8(1), 69. https://doi.org/10.2307/1390921 Christoffersen, P. F. (2012). Elements of financial risk management (2nd ed.). New York: Academic Press. Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873–889. https://doi.org/10.1002/jae.800 Hentschel, L. (1995). All in the family nesting symmetric and asymmetric GARCH models. Journal of Financial Economics, 39(1), 71–104. Nugroho, D. B. (2018). Comparative analysis of three MCMC methods for estimating GARCH models. In IOP Conference Series: Materials Science and Engineering. IOP Publishing. Nugroho, D. B., & Morimoto, T. (2014). Realized non-linear stochastic volatility models with asymmetric effects and generalized student’s t-distribution, 44(1), 83–118. Nugroho, D. B., Susanto, B., & Rosely, M. M. M. (2018). Penggunaan MS Excel untuk estimasi model GARCH(1,1). Jurnal Matematika Integratif, 14(2), 71–81. Teräsvirta, T. (2009). An introduction to univariate GARCH models. In T. G. Andersen, R. A. Davis, J.-P. Kreib, & T. Mikosch (Eds.), Handbook of Financial Time Series (pp. 17–42). Berlin, Heidelberg: Springer Berlin Heidelberg. Tsiotas, G. (2009). On the use of non-linear transformations in Stochastic Volatility models. Statistical Methods and Applications, 18(4), 555–583. https://doi.org/10.1007/s10260-008-0113-9 Tung, H. K. K., Lai, D. C. F., & Wong, Mi. C. S. (2010). Professional financial computing using Excel and VBA. Singapore: John Wiley & Sons. Zivot, E. (2009). Practical issues in the analysis of univariate GARCH models. In T. G. Andersen, R. A. Davis, J.-P. Kreib, & T. Mikosch (Eds.), Handbook of Financial Time Series (p. 113). Berlin, Heidelberg: Springer-Verlag.id_ID
dc.identifier.issn2656-0615
dc.identifier.urihttp://hdl.handle.net/11617/11115
dc.description.abstractStudi ini mengusulkan klas baru dari model GARCH dengan mengaplikasikan keluarga transformasi Box–Cox ke volatilitas lag-1. Model GARCH telah banyak digunakan untuk mendikripsikan tingkah laku volatilitas suatu runtun waktu keuangan, terutama pada kurs mata uang. Tingkah laku dari volatilitas return dipelajari berdasarkan model yang mengasumsikan distribusi normal untuk inovasi. Model diestimasi menggunakan alat bantu Solver Excel dan Matlab. Analisis empiris didasarkan pada data simulasi dan data kurs beli EUR, JPY, dan USD terhadap IDR atas periode harian dari 2010 sampai 2017. Dalam kasus data simulasi dan data riil, ditemukan bahwa Solver Excel memiliki kelemahan. Hasil empiris untuk data simulasi menunjukkan bahwa model BC(1)-GARCH(1,1) bisa dikatakan tidak lebih baik dari model GARCH(1,1). Sedangkan untuk kasus data riil dengan inovasi berdistribusi normal menunjukkan bahwa model BC(1)-GARCH(1,1) mengungguli model GARCH pada data kurs beli USD terhadap IDR.id_ID
dc.language.isootherid_ID
dc.publisherProsiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya (KNPMP) IV 2019id_ID
dc.titlePemodelan Volatilitas menggunakan GARCH(1,1) dengan Volatilitas LAG-1 Ditransformasi Box–Coxid_ID
dc.typeArticleid_ID


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