Judul: A Comparative Study of GARCH, LSTM, and Hybrid GARCH-LSTM Models for Analyzing the Dynamical Pattern of Stock Price Volatility
Abstrak: Most of the statistical methods used in time series analytics assume that the data properties be stationary. In real cases, this assumption may be violated. The stock price is one of the highly volatile data or has a time-dependent variance. GARCH model is a model that overcomes the limitation of the stationary assumption on most statistical methods. In addition, artificial intelligence methods are widely used and have promising performance. This study aims to examine the performance of the GARCH, LSTM, and hybrid GARCH–LSTM models in analyzing the dynamical pattern of volatility data on simulation and empirical data. This study uses two kinds of data, simulation and empirical data. The simulation data are obtained from the simulation process by generating data from the GARCH model with ten scenarios. The scenarios are the combination of two factors: (1) variance with two levels, high and low; (2) dataset with five levels, GARCH (1,1), GARCH (1,3), GARCH (3,1), GARCH (3,3), and Contaminated GARCH. The design for the simulation study is the randomized complete block factorial design with three factors (i.e., variance, dataset, and model), and the response is the MAPE value. The effects of the factors on the MAPE values are analyzed using ANOVA and Tukey's multiple comparisons. Meanwhile, the stock closing prices of PT Bumi Resources Minerals Tbk. in 2021 are used as the empirical study. For the modeling process in empirical study, data are transformed into returns and then modeled using three proposed models. Seven days ahead, predictions are carried out after the model validation process. The ANOVA results show that there are different effects of variance on MAPE values, but these variance effects depend on the kind of dataset. For high variance, the MAPE values of Contaminated GARCH are low, while for the other datasets, the MAPE values are high. According to Tukey's multiple comparisons, the Contaminated GARCH dataset from high variance has different effects on the MAPE value than other datasets from low variance. In addition, the mean MAPE values of GARCH (3,1) and GARCH (3,3) datasets from high variance are higher than those of the GARCH (1,1) dataset from low variance. Another result shows that the GARCH (1,3) and GARCH (3,3) datasets from high variance have different effects on the MAPE value than the Contaminated GARCH dataset from low variance. Furthermore, all comparisons show no significant effect on the MAPE value for the same datasets but with different variances. Another significant factor that affects the MAPE value is the model. The effect of the model does not depend on the variance or the dataset type. Tukey's multiple comparisons show that the Hybrid GARCH–LSTM model outperforms all the other models, followed by the LSTM and GARCH models. For the empirical study, the results show that all models can capture the volatility pattern of the BRMS stock closing prices, but there are some different performances. The GARCH and LSTM models provide the MAPE values of 3.18% and 3.06%, respectively. Meanwhile, for the Hybrid GARCH–LSTM model, the MAPE value is 0.93%. The Hybrid GARCH–LSTM is the best model among others. Based on the three
Keyword: GARCH, Hybrid, LSTM, Stock price, Volatility

Judul: Implementation methods of ARCH/GARCH model at closing price of stock in indonesian stock exchange period 2005-2013
Abstrak: Saham merupakan bukti kepemilikan seseorang terhadap suatu Perseroan Terbatas. Harga saham bergerak secara fluktuatif setiap harinya sehingga menyebabkan terjadinya volatilitas. Volatilitas merupakan sebuah pola ragam dari deret waktu, khususnya deret waktu keuangan dan disebut tidak stasioner karena keragamannya yang tidak konstan. Kondisi tersebut memungkinkan terjadinya heteroskedastisitas yang menyebabkan perlu dianalisis lebih lanjut setelah melakukan pemodelan ARIMA Box-Jenskin yaitu memodelkan ragam dengan menggunakan metode Autoregressive Conditional Heteroscedasticity (ARCH) dan Generalized Conditional Heteroscedasticity (GARCH) yang diperkenalkan oleh Robert Engle dan Tim Bollerslev untuk mengatasi masalah heteroskedastisitas. Dari hasil pemodelan data harga penutupan saham diperoleh model terbaik yaitu model rataan ARIMA(1,1,2) dan model ragam GARCH(1,1) yaitu Zt = 1.9999Zt-1 – 0.9999Zt-2 + 0.9072et-1 – 0.0897et-2 + εt. Hal ini berarti ragamnya dipengaruhi oleh kuadrat sisaan dan ragam bersyarat satu periode yang lalu. Hasil dari validasi model didapatkan nilai MAPE sebesar 1.31% dan nilai MAD sebesar 63.8411 sehingga dapat disimpulkan bahwa model yang dihasilkan valid. Kata kunci : ARCH, GARCH, heteroskedastisitas, volatilitas., Stock is proof of person’s ownership to a limited company liability. Stock prices have daily fluctuate moves basis thus causing volatility. Volatility is a variances pattern of time series, especially financial time series, and then that is not stationary causing the variances are not constant. These condition allow for heteroscedasticity, and then need further analysis after doing ARIMA Box-Jenskin’s modeling then doing variances modeling with Autoregressive Conditional Heteroscedasticity (ARCH) and Generelized Autoregressive Conditional Heteroscedasticity (GARCH) introduced by Robert Engle and Tim Bollerslev to solve the problem of heteroscedasticity. From the results of closing prices data modeling we get ARIMA(1,1,2) for the best mean model and GARCH(1,1) for the best variance model. This means that the variances are affected by square residuals and conditional variance. The results obtained from the model validation MAPE value is 1.31% and MAD value is 63.8411 so it can be concluded that the resulting model is valid. Keywords : ARCH, GARCH, heteroscedasticity, volatility
Keyword: ARCH, GARCH, heteroskedastisitas, volatilitas

Judul: Kebiasaan makanan ikan endemik bonti-bonti (Paratherina striata) di danau Towuti, Sulawesi Selatan
Abstrak: Ikan bonti-bonti (Paratherina striata) merupakan salah satu jenis ikan endemik di perairan Danau Towuti. Populasi ikan tersebut cenderung terbatas karena hanya terdapat di perairan Danau Towuti dan Danau Mahalona, ditambah dengan kegiatan penangkapan yang tinggi oleg nelayan setempat. sehingga perlu diupayakan kegiatan pengelolaan dengan cara konservasi agar kelestarian dan keberadaan ikan tersebut tetap terjaga.
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