ANALISIS RISIKO VALUE AT RISK (VAR) VARIANS-KOVARIANS DENGAN PENDEKATAN NONPARAMETRIK KERNEL (STUDI KASUS : FAKTOR-FAKTOR YANG MEMPENGARUHI RETURN INDEKS SAHAM SYARIAH INDONESIA (ISSI) PERIODE JULI 2018 – DESEMBER 2023)

Tiara Afrista, NIM.: 21106010058 (2025) ANALISIS RISIKO VALUE AT RISK (VAR) VARIANS-KOVARIANS DENGAN PENDEKATAN NONPARAMETRIK KERNEL (STUDI KASUS : FAKTOR-FAKTOR YANG MEMPENGARUHI RETURN INDEKS SAHAM SYARIAH INDONESIA (ISSI) PERIODE JULI 2018 – DESEMBER 2023). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

ISSI is an index that describes the movement of stock prices listed on the Indonesia Stock Exchange. Return is the rate of return on investment that shows the change in the value of an asset or investment in a certain period. The movement of stock return values requires a strategy in analyzing stock return risk. This study aims to determine the steps for analyzing the Value at Risk (VaR) Variance-Covariance risk, to determine the best ISSI return prediction model with nonparametric regression, and to determine the maximum return and loss of the best regression model for the period July 2018 to December 2023. This study uses two kernel functions, namely Gaussian and Epanechnikov. The selection of bandwidth uses minimum Generalized Cross Validation (GCV) with the best model based on the smallest MSE, AIC and BIC values. After the VaR estimate is obtained, a validity test is carried out to embed the resulting VaR estimate. Based on the analysis obtained, the best model in predicting ISSI returns is the Gaussian kernel regression model with an MSE value of 2,40×10−4, AIC of -5,42×102, and BIC of −5,33×102. ISSI return predictions fluctuate as the investment period increases. In the 1-3 month period, the return is in the negative zone, but in the 12 month period, the return is positive by 4.33%. The maximum loss of the best model (Gaussian function) in predicting ISSI returns for the July 2018 - December 2023 period with a 95% confidence level in a 12 month period is Rp88.980,46 or 8.89%.

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