The Macroeconomic Surprise Effects on Lq45 Stock Return Volatility

Penelitian ini bertujuan untuk memodelkan volatilitas return indeks saham perusahaan dividen tertinggi di Indonesia (DIV 20) sebelum dan sesudah pandemi COVID 19. Model keluarga ARCH (Autoregressive Conditional Heteroscedasticity) digunakan dalam hal ini. Periode penelitian diperpanjang dari 18 Mei 2018 hingga 18 Februari 2022. Batas waktu dimulainya pandemi adalah 1 April 2020. Data pengembalian adalah pengembalian mingguan. Hasilnya menunjukkan bahwa sebelum pandemi, GJR-GARCH(1,1) dapat memetakan dan melacak volatilitas dengan sangat baik karena mencetak AIC dan SIC pra-pandemi terendah. Oleh karena itu, penelitian ini menguatkan bukti adanya reaksi asimetris dari partisipasi pasar terhadap kemunculan dan penyebaran berita baik dan buruk di pasar. Setelah pandemi, efek ARCH menjadi kurang jelas. Angka signifikansi menurun meskipun efek ARCH masih signifikan pada 0,15. Performa model ARCH(1) secara signifikan lebih tinggi daripada model lain pasca-pandemi. Hasil tersebut menjadi bukti bahwa pascapandemi ketidakpastian yang dihadapi pelaku pasar sangat tinggi. Hal ini mengakibatkan meningkatnya volatilitas. Model keluarga ARCH menjadi kurang signifikan karena pengembaliannya lebih acak. Analisis lebih lanjut, bagaimanapun, menunjukkan bahwa pengembalian belum mengikuti model random walk meskipun keacakan meningkat. Oleh karena itu, ARCH(1) masih sesuai untuk memodelkan volatilitas setelah Pandemi. This research aims at modeling the volatility of Indonesian highest paying dividend companies stock index (DIV 20) returns before and after pandemic COVID 19. The ARCH (Autoregressive Conditional Heteroscedasticity) family models were employed in this regard. The research period extended from 18 May 2018 to 18 February 2022. The cutoff for the commencement of pandemic was 1st April 2020. The return data were weekly returns. The results suggested that before pandemic, GJR-GARCH(1,1) could map and trace the volatility very well since it scored the lowest AIC and SIC pre-pandemic. Therefore, this research corroborated the evidence that there existed asymmetric reaction from the market participation toward the emergence and spread of good and bad news in the market. After pandemic, the ARCH effect became less obvious. The significance number was decreasing although the ARCH effect was still significant at 0.15. ARCH(1) model performance was significantly higher than the other models post-pandemic. The result presented evidence that after pandemic the uncertainty facing the market participants was very high. This resulted in the increase of the volatility. The ARCH family model was becoming less significant because the returns were more random. Further analysis, however, showed that the returns did not yet follow the random walk model despite the increasing randomness. Therefore, ARCH(1) was still appropriate to model the volatility after Pandemic.

show abstract

“…From the index available during the time period, return was calculated. The equation for index return proxy is (Andika, et al 2019):…”

Section: Methodsmentioning

confidence: 99%

The Analysis of Highest Paying Dividend Companies Stock Returns Volatility in Indonesia

Putri

Lasar

Ponziani

2023

JMIEB

View full text Add to dashboard Cite

show abstract

“…Investors will consider the change in returns as an indicator of risks. High return volatility will render a stock riskier and low return volatility will indicate low risk investment (Andika et al, 2019). Risky investment can cause decrease in wealth when the investment value decreases.…”

Section: Introductionmentioning

confidence: 99%

Modeling the returns volatility of Indonesian stock indices: The case of SRI-KEHATI and LQ45

Ponziani

2022

JEM

View full text Add to dashboard Cite

The purpose of this research is to model the volatility of Stock Indices in Indonesian capital market. This research focuses on two stock indices namely SRI-KEHATI and LQ45. SRI_KEHATI is a stock index that consists of companies whose operations are sustainable and environmentally friendly. This stock index is also known as â€œgreen indexâ€ due to its environment and sustainability concern. This is the novelty of this research that fills in the gap in the literature in which not much known regarding this green index. As the comparison, LQ45 stock index was modeled. The data used in this model were daily returns data of both index. The research period extended from 2 January 2019 to 1 November 2021. The research employed four models i.e. ARCH (1), ARCH (2), GARCH (1,1) and GJR-GARCH (1,1) for both indices returns. The ARCH and GARCH model were employed to capture the conditional variance of the indices return, while GJR-GARCH was specifically chosen to investigate whether there exists asymmetric effect in which return reacts more to bad news than good news. Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) were chosen as the parameters for choosing the best models. Data analysis showed that GJR-GARCH was the best model for modeling the returns volatility of SRI-KEHATI and LQ45. This model was able to capture the essential property of asymmetric effect present in both models. The second best model was ARCH (2). Apparently, returns variance of Indonesian stock indices are affected more by lagged residuals. The limitation of this research lies in its research period that covered both pre-pandemic and post-pandemic period. Stock market behavior might be very different between these two periods. Future research may endeavor to investigate how the volatility of stock differs between pre-pandemic and post-pandemic period.

show abstract

The Macroeconomic Surprise Effects on Lq45 Stock Return Volatility

Cited by 2 publications

References 14 publications

The Analysis of Highest Paying Dividend Companies Stock Returns Volatility in Indonesia

The Analysis of Highest Paying Dividend Companies Stock Returns Volatility in Indonesia

Modeling the returns volatility of Indonesian stock indices: The case of SRI-KEHATI and LQ45

Contact Info

Product

Resources

About