MODEL LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) PADA ANALISIS RISIKO SAHAM DENGAN VALUE AT RISK (VaR) (Studi Kasus : Harga Saham Harian Bank Rakyat Indonesia (BBRI) Periode 12 Februari 2014 sampai 22 Agustus 2018)

Annisa Dihan Yudantri, NIM. 14610026 (2019) MODEL LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) PADA ANALISIS RISIKO SAHAM DENGAN VALUE AT RISK (VaR) (Studi Kasus : Harga Saham Harian Bank Rakyat Indonesia (BBRI) Periode 12 Februari 2014 sampai 22 Agustus 2018). Skripsi thesis, UIN Sunan Kalijaga.

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Abstract

In this era of globalization, investment has been carried out by the community. The type of investment that is in demand in the financial sector, for example stocks. Stock data is data in the form of time series and has a nonlinear tendency. So that a nonlinear time series model is needed to model the stock return. Nonlinear time series can be modeled using Logistic Smoothing Transition Autoregressive (LSTAR). With the Value at Risk (VaR) approach, the greatest possible risk is known. In this research discusses the LSTAR model with the VaR approach was implemented at Bank Rakyat Indonesia's daily stock price for the period 12 February 2014 to 22 August 2018. The results of this study to showing that LSTAR (1,1) is the best model. Calculation of VaR-LSTAR (1,1) is obtained that if it is assumed that the initial investment fund is Rp10,000,000.00, the return for the period of 1 day, 5 days and the next 20 days in the amount of Rp3,796.00, Rp3,571.00 and Rp3,145.00. With the biggest risk on 1 day, 5 days, and the next 20 days respectively are Rp.129,732.00, Rp290,089.00, and Rp580,179.00. Keywords: AIC, LSTAR, Nonlinearitas Return, Risiko, STAR, Value at Risk (VaR)

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