• Tidak ada hasil yang ditemukan

Repositori Institusi | Universitas Kristen Satya Wacana: Studi Pada Distribusi Alpha Skew-t dan Komponen Kontinu Lompatan Dari Volatilitas Return Pada Model Aparch

N/A
N/A
Protected

Academic year: 2023

Membagikan "Repositori Institusi | Universitas Kristen Satya Wacana: Studi Pada Distribusi Alpha Skew-t dan Komponen Kontinu Lompatan Dari Volatilitas Return Pada Model Aparch"

Copied!
2
0
0

Teks penuh

(1)

15

DAFTAR PUSTAKA

Acitas, A., Şenoğlu, B., & Arslan, O. (2015) Alpha-Skew Generalized t Distribution, Revista Colombiana de Estadistica, 38, 353–370.

Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

Anggita Mega Kusumawati (2019) Estimasi volatilitas melalui model EGARCH(1,1) berdistribusi student-t dan alpha skew normal. Skripsi, UKSW.

Atchade, Y. F., & Rosenthal, J. S. (2005). On adaptive Markov chain Monte Carlo algorithms. Bernoulli, 11(5), 815–828. https://doi.org/10.3150/bj/1130077595 Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal

of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 Chen, M.-H., & Shao, Q.-M. (1999). Monte Carlo estimation of Bayesian credible and

HPD intervals. Journal of Computational and Graphical Statistics, 8(1), 69–92.

Ding, Z., C. W. J. Granger, and R. F. Engle (1993): “A Long Memory Property of Stock Market Returns and a New Model,” Journal of Empirical Finance, 1, 83–106.

Engle, R. (2002). New Frontiers for ARCH Models. Journal of Applied Econometrics, 17(5), 425–446. https://doi.org/10.1002/jae.683

Francq, C. and Thieu, L.Q. QML inference for volatility models with covariates.

Econometric Theory, 35, (2019), 37-72.

Fracq, C. & Zakoian, J.M., 2019. GARCH Model Structure, Statistical Inference and Financial Application. France: A John Wiley and Sons, Ltd.

Hoffman, J. I. E. (2015). Normal Distribution. Biostatistics for Medical and Biomedical Practitioners, 101–119. https://doi.org/10.1016/B978-0-12-802387-7.00006-8

Hwang, S., and S.E. Satchell. ‘‘GARCH Model wit Cross-sectional Volatility: GARCHX Models.’’Working Paper, Faculty of Finance, City University Business School, UK, 2001.

Marliana, R. R., & Padmadisastra, S. (2018). Poisson Regression of Damage Product Sales Using Mcmc. Indonesian Journal of Statistics and Its Applications, 2(1), 1–12.

https://doi.org/10.29244/ijsa.v2i1.53

Nugroho, D. B., & Morimoto, T. (2014). Realized non-linear stochastic volatility models with asymmetric effects and generalized student’s t-distribution. Journal of The Japan Statistical Society, 44(1), 83–118. https://doi.org/10.14490/jjss.44.83

Sadiq, M., Ahmad, S., Anjum, M. J., Suliman, M., Abrar, S. U., & Khan, S. (2013). Stock price volatility in relation to dividend policy; A case study of Karachi Stock Market.

MiddleEast Journal of Scientific Research, 13(3), 426–431.

Sultonov, M. (2021). External Shocks and Volatility Overflow among the Exchange Rate of the Yen, Nikkei, TOPIX and Sectoral Stock Indices. Journal of Risk and Financial Management, 14(11), 560. https://doi.org/10.3390/jrfm14110560

Taylor, S., 1986, Modeling financial time series, New York, John Wiley & Sons.

Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons.

Wang, Yuling, Yunshuang Xiang, Xinyu Lei, dan Yucheng Zhou. 2022. “Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market.”

Economic Research-Ekonomska Istrazivanja 35(1): 2530–54.

doi:10.1080/1331677X.2021.1967771.

(2)

16 Zhang, H., & Lan, Q. (2014). GARCH-type model with continuous and jump variation

for stock volatility and its empirical study in China. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/386721

Referensi

Dokumen terkait

Research conducted by Fuady & Rahmawati (2018), the effect of cash turnover, accounts receivable turnover, inventory turnover on profitability in cement companies listed

Furthermore, in Figure 2, it is known that the results of the questionnaire indicators of the optimal implementation of learning are differentiated from the needs of students in