Estimasi volatilitas dan prediksi return indeks LQ45 dengan markov switching garch model = Volatility estimation and prediction of index LQ45 using markov switching garch model

Hana, Illaria (2014) Estimasi volatilitas dan prediksi return indeks LQ45 dengan markov switching garch model = Volatility estimation and prediction of index LQ45 using markov switching garch model. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Dalam berinvestasi khususnya pada saham, kita melihat dua faktor yaitu faktor tingkat pengembalian saham (return) dan faktor tingkat resiko. Tingkat pengembalian saham diketahui dengan persentasi perubahan yang acak (random walk) pada return saham, sedangkan tingkat resiko digambarkan dalam volatilitas. Penelitian ini bertujuan untuk mengestimasi nilai volatilitas dan prediksi return saham di masa depan. Data yang digunakan dalam penelitian ini adalah indeks harga saham penutupan harian (closing price) dari indeks harga saham LQ 45 periode Januari 2007- Januari 2013. Untuk kepentingan itu dikembangkan basis model estimasi yaitu model Markov Switching GARCH. Markov Switching adalah model yang mampu mendeteksi perubahan struktur pada volatilitas. Hasil penelitian menunjukkan bahwa model Markov Switching mampu melakukan estimasi volatilitas dan prediksi return yang lebih baik dibandingkan ARIMA, dan GARCH(1,1). Sedangkan perhitungan pada volatilitas menunjukkan tingkat resiko investasi yang tidak tinggi pada indeks LQ45. / Two important factors in stock investment are stock returns and risk factor levels. Stock returns are determined by the percentage change in the random walk on stock returns, while the level of risk is described in volatility. This study aims to estimate the predictive value and the volatility of stock return in the future. The data used in this study are daily closing stock price of index LQ 45 period January 2007 - January 2013. In this study, the Markov Switching GARCH model was developed. Markov switching model is able to detect structural and regime changes in volatility. The results showed that the Markov Switching Model is able to predict value of return better than ARIMA, and GARCH(1,1). While the volatility calculation implied that the level of investment risk is not high on index LQ45.

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