Modeling joint distribution of national stock indices

Kangina, Nataliya; Knyazev, A G; Lepekhin, O A; Shemyakin, Arkady

doi:10.3233/mas-150350

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Hal ini menyebabkan efek penularan terhadap integrasi ekonomi negara lain (Uchoa, 2018). Hasil penelitian Kangina, et al (2016) menunjukkan kointegrasi pasar saham antar negara yang memiliki dinamika sama, maka akan memiliki tingkat pengembalian yang diharapkan sama.…”

Section: Pendahuluanunclassified

Kointegrasi Bursa Efek Indonesia Dengan Bursa Efek Amerika Serikat, Jepang, Hongkong, Dan Malaysia

Setyawan

Rorlen

Ekadjaja

2021

JMIEB

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Penelitian ini bertujuan untuk menganalisis kointegrasi bursa efek di negara Amerika Serikat, Jepang, Hongkong, Malaysia, dan Indonesia dari tahun 2008-2020 dengan menggunakan model Vector Autoregressive Model. Penelitian ini dilakukan pada rentang waktu selama 156 bulan, di mana data yang diamati pasca krisis global di dunia (2008-2014) dan saat kemajuan ekonomi Cina yang berdampak pada perang dagang dengan USA (2014-2020). Berdasarkan hasil olah data dengan menggunakan aplikasi eviews 9.0 ditemukan adanya kointegrasi antara bursa efek di negara Amerika Serikat, Jepang, Hongkong, Malaysia, dan Indonesia yang diproksikan indeks DJIA, Hang Seng, Nikkei, KLCI, dan IHSG pada tahun 2008-2020. Hasil uji Vector Error Correction Model menunjukkan tidak adanya kausalitas jangka pendek antara pergerakan indeks Dow Jones, Nikkei, Hang Seng, KLCI, dan IHSG. Hasil uji impulse response menggambarkan impact dari perubahan pada indeks Dow Jones, Hang Seng, Nikkei, dan KLCI terhadap IHSG bersifat jangka panjang untuk kembali ke posisi setara dengan perlahan (slow response).Indeks DJIA yang menguat dipercaya dapat memberikan pengaruh positif bagi saham di Indonesia. Sehingga naik atau turunnya indeks DJIA akan diikuti pula naik atau turunnya IHSG. Implikasi dari penelitian ini adalah terkointegrasinya indeks bursa efek negara di Amerika Serikat, Jepang, Hongkong, Malaysia, dan Indonesia memberikan prediksi bagi investor terhadap fluktuasi indeks saham yang akan terjadi. This research is an empirical study regarding the cointegration of stock exchanges in US, Japan, Hongkong, Malaysia, and Indonesiafrom 2008-2020 using the Vector Autoregressive Model. This research was conducted over a period of 156 months, where data was observed after the global crisis in the world (2008-2014) and when Hongkong's economic progress had an impact on the trade war with the USA (2014-2020). Based on the results of data processing using the eviews 9.0 application, it was found that there was a cointegration between stock exchanges in the United States, Japan, Hongkong, Malaysia, and Indonesia, which were proxied by the DJIA, Hang Seng, Nikkei, KLCI, and IHSG indexes in 2008-2020. The results of the Vector Error Correction Model test show that there is no short-term causality between the movements of the Dow Jones, Nikkei, Hang Seng, KLCI, and Indonesia Composite index. The impulse response test results illustrate the long-term impact of changes in the Dow Jones, Hang Seng, Nikkei, and KLCI indices on the Indonesia Composite Index to return to an equivalent position slowly (slow response). The stronger DJIA index is believed to have a positive impact on stocks in Indonesia. So that the increase or decrease in the DJIA index will also be followed by an increase or decrease in the Indonesia Composite index. The implication of this research is the cointegration of stock exchange indexes in United States, Japan, Hongkong, Malaysia, and Indonesia can help investors to predict the fluctuation indexes.

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Section: Pendahuluanunclassified

Kointegrasi Bursa Efek Indonesia Dengan Bursa Efek Amerika Serikat, Jepang, Hongkong, Dan Malaysia

Setyawan

Rorlen

Ekadjaja

2021

JMIEB

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“…Notice that this analysis can be done using end-of-the-month index values or average-of-the-month values, where the latter choice corresponds to the "Asian option" approach, which is becoming increasingly popular in insurance modeling. One step with = 1, 2, , t T  can be decomposed into substeps (Ane et al, 2008;Kangina et al, 2016;Kniazev et al, 2016):…”

Section: Modeling Asset Variablesmentioning

confidence: 99%

“…However, in dimensions higher than = 2 d they require additional definition of the hierarchical structure (vines and nested copulas being two main options, see (Shemyakin, Kniazev, 2017)). We will consider Clayton's nested copula family as in (Hofert, Mächler, 2011;Kangina et al, 2016;Okhrin, Ristig, 2014) along with Student's t-copula as possible choices. Clayton's nested copula is built using a stepwise hierarchical procedure, such as from Clayton's pair copulas…”

Section: финансы Financementioning

confidence: 99%

“…Liability variables are added to the set at the next stage involving the simulation from a copula and the estimation of economic capital (see Section 4). In order to compare the performance of Gaussian copulas, t-copulas, and Archimedean copula structures, we will follow the model selection procedures outlined in (Gordeev et al, 2012;Kangina et al, 2016;Kniazev et al, 2016) for stock index data. At this point, the AIC analysis in Table 6 (the lower AIC value corresponds to the better model) suggests that, for the restricted set of variables = 1, , j k  , the t-copula outperforms the other two classes, which is consistent with the literature, see (Shim et al, 2011).…”

Section: Choice and Calibration Of Copulamentioning

confidence: 99%

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Copula models of economic capital for life insurance companies

Benson¹,

Burroughs²,

Ladyzhets³

et al. 2020

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The objective of the paper is to introduce a copula methodology of economic capital modeling, which is practically applicable for life insurance companies. Copula methods make it possible to address multiple dependent risk factors including both investment and underwriting risks in the framework of a portfolio approach. We identify a relevant set of asset and liability variables, and suggest a copula model for the joint distribution of these variables. Estimates of economic capital are constructed via VaR and TVaR calculations based on the tails of this joint distribution. This approach requires ARIMA and copula model selection followed by Monte Carlo simulation of the time series of the joint asset/liability portfolio. Models are implemented in open source software (R and MS Excel) and tested using historical and simulated asset/liability data. The results are applied to the construction of a software tool which can be utilized for customization and direct user application. The novelty of the approach consists in estimating interdependent underwriting and investment risks in one multivariate model taking into account short-term (daily or monthly) fluctuations of the market. In particular, we address the challenges that life insurance companies face in the low interest environment, using the market data for the 15-year period 2003-2018.

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“…Dalam kasus umum, integrasi pasar saham dapat dilihat sebagai situasi dimana ketika pasar saham menunjukkan dinamika yang sama maka akan memiliki hasil yang diharapkan sama (Kangina, Knyazev, Lepekhin, & Shemyakin, 2016).…”

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Analisis Kausalitas Antara Ihsg, Indeks Dow Jones Industrial Average Dan Nilai Tukar Rupiah/Us$

Natsir

Yusbardini

Bangun

2019

JMIEB

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Penelitian ini bertujuan untuk menginvestigasi hubungan kausalitas antara IHSG, nilai tukar rupiah/US$ dan Indeks Global yang diwakili oleh Indeks Dow Jones Industrial Average. Penelitian mengambil sampel nilai-nilai variabel yang diteliti dengan periode data bulanan dalam periode Juli 2005-Desember 2018. Alat analisis menggunakan uji Engle-Granger untuk menginvestigasi hubungan kausalitas. Hasil Uji kausalitas Granger memperlihatkan terdapat hubungan dua arah atau saling mempengaruhi antara IHSG dengan nilai rupiah/US$. Selain itu ditemukan pula bahwa pergerakan Indeks Dow Jones Industrial secara signifikan mempengaruhi kepada pergerakan IHSG dan nilai tukar rupiah/US$, tetapi sebaliknya pergerakan yang terjadi pada IHSG dan nilai tukar tidak mampu mempengaruhi gerakan indeks Dow Jones Industrial. Hasil Uji kointegrasi Johansen memperlihatkan bahwa semua variabel penelitian mempunyai hubungan keseimbangan jangka panjang yang signifikan. This study aims to investigate the causality relationship between the CSPI, the exchange rate of rupiah / US $ and the Global Index represented by the Dow Jones Industrial Average. The study sampled variable values studied with monthly data periods in the period July 2005-December 2018. The analysis tool uses the Engle-Granger test to investigate causality relationships. Granger causality test results show there is a two-way relationship or influence each other between the CSPI with the value of rupiah / US $. In addition it was also found that the movement of the Dow Jones Industrial Index significantly affected the movement of the JCI and the exchange rate of the rupiah / US $, but conversely the movements that occurred on the JCI and the exchange rate were unable to influence the movement of the Dow Jones Industrial index. Johansen's cointegration test results show that all research variables have a significant long-term balance relationship.

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Modeling joint distribution of national stock indices

Cited by 4 publications

References 12 publications

Kointegrasi Bursa Efek Indonesia Dengan Bursa Efek Amerika Serikat, Jepang, Hongkong, Dan Malaysia

Kointegrasi Bursa Efek Indonesia Dengan Bursa Efek Amerika Serikat, Jepang, Hongkong, Dan Malaysia

Copula models of economic capital for life insurance companies

Analisis Kausalitas Antara Ihsg, Indeks Dow Jones Industrial Average Dan Nilai Tukar Rupiah/Us$

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