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Numerous scholars have examined the relationship between stock returns and metal commodities, but there has been less emphasis on specific metal commodities such as coal and nickel. This study sought to investigate the correlation between coal and nickel price fluctuations and their impact on stock market volatility. The researchers employed the GARCH and EGARCH models for analysis. This study utilized data on coal price, nickel price, and two stock market indices in Indonesia, specifically LQ45 and IDX30, spanning from January 2020 to December 2023. The results indicate that there was no substantial correlation between coal shock and nickel shock on stock market volatility. The EGARCH model would be more suitable for prediction. The significance of this research lies in its discovery of the relationship between coal shock and nickel shock on stock return throughout a dynamic period.

The relationship of volume and price is always a concern for market participants, whether volume has an influence or not on price changes that exist in financial markets. This study aims to determine the effect of Trading Volume on Stock Price. Market participants and researchers always want to find out the relationship of trading volume to stock prices. The method used in this study is a quantitative analysis method using GARCH (1.1). GARCH is a model for generating forecasts based on historical data using functions from its own past lag plus past innovations. Research conducted in Indonesia still very rarely uses GARCH (1.1) to analyze the relationship between volume and stock price. In this study, the author uses data from the Composite Stock Price Index (JCI) on the Indonesia Stock Exchange (IDX) for the period 04 January 2010 - 29 September 2023. This study also found the best model results, namely the GARCH-M model (1.1). The results of the research conducted found that trading volume has a positive and significant effect on stock prices or the Composite Stock Price Index (JCI). The conclusion of this study JCI and Volume data after the root test ADF test proved to be stationary; GARCH analysis (1.1) shows no ARCH Effect, with trading volume having a positive and significant effect on JCI; The best models are GARCH-M (1.1) with R-Squared 0.426526 and AIC -1.929132. [ABSTRACT FROM AUTHOR]

Financial markets have an important role in the economy of a country including Indonesia. One of the activities chosen by investors in the financial market is investing. In the world of investment, especially in stocks, there is a phenomenon of volatility, which is a situation where a stock price value increases and decreases. Volatility in this financial market is something that is very interesting for investors because of its impact on the existence of global financial markets. The purpose of this study is to model the LQ45 index data using a model that can overcome the problem of heteroscedasticity and changes in data structure. The commonly used model for heteroscedasticity problem is ARCH/GARCH. Furthermore, a model that can account for structural changes is the Markov Switching model. The model that can overcome the problem of heteroscedasticity as well as structural changes is the MS GARCH model. The financial data used in this study are daily data for the LQ45 Index from 10 June 2019 to 28 May 2020. Based on the results of data analysis conducted using the MS GARCH model is the best model in modelling the volatility of the LQ45 index. The best model selection uses the criteria for the AIC and BIC values with the smallest value

The influence of foreign exchange on all spheres of any economy is germane to determining its extent of development. Most developing countries have been subjected to managing the integrity of the exchange rate arising from their disadvantageous position in world trade. Nigeria is not an exemption either; this is due to its constant interventions in the foreign exchange market from time to time and by the policy of the existing government in power. However, the interventionist policy framework has been more harmful to Naira as the currency depreciates unabatedly. The pertinent question is whether monetary policy should be the only approach to effective exchange rate management or be combined with fiscal and income policies. Irrespective of the policy choice, the aim, target, and instrument must be complementary without unnecessary disruptions midway, as usually experienced in Nigeria. These inconsistencies have come to bear enormously on the country's exchange rate management. Rather than focusing on the impact of exchange rate volatility on industrial development that could warrant foreign exchange inflows, this paper considers industrialization as an approach to effectively managing exchange rates in Nigeria. This analysis employs a univariate GARCH and BEKK-MGARCH model, using high-frequency monthly time series data from 2000 to 2019, to examine the volatility transmission between the foreign exchange market and the industrial sector. The model estimation uses the Conditional Maximum Likelihood Technique (CMLT). It was discovered that industrial development positively influenced the official market exchange rate compared to the parallel market exchange rate, reducing its volatility. The study thus suggests the vigorous pursuit of foreign exchange earnings and usage policy by earning entities, floating Diaspora bonds, enhancing and encouraging remittances and repatriation of illicit financial outflows to enhance industrialization. This will discourage arbitraging, increasing foreign exchange inflows and stabilizing the economy.

The correlation between the capital market of G20 member countries is important to analyze. Depending on a country’s economy, capital market integration may have different effects. A more intense bilateral relationship (trade intensity) can significantly affect the movement of capital market shares between countries. The current research used the Multivariate GARCH Model/DCC-GARCH method. The condition of capital market integration before the Indonesian G20 Presidency showed that Indonesia (JKSE) had the strongest integration with Australia (ASX) (0.563814) and South Korea (KOSPI) (0.542470). After the G20 presidency, Indonesia (JKSE) had the strongest capital market integration with India (NSE) (0.507229) and the USA (NYSE). It was also found that China (SSE) had an integration with South Korea (KOSPI), while Germany (DAX) and Australia (ASX) had an integration with the UK (FTSE100). The conclusion is that the higher autocorrelation, the higher the effect of the volatility of stock market movements in the two countries involved. Furthermore, capital market integration can be influenced by geospatial and economic relations.

Numerous variants of the basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been proposed to provide good volatility estimating and forecasting. Most of the study does not work Excel’s Solver to estimate GARCH-type models. The first purpose of this study is to provide the capability analyze of the GRG non-linear method built in Excel’s Solver to estimate the GARCH models in comparison to the adaptive random walk Metropolis method in Matlab by own codes. The second contribution of this study is to evaluate some characteristics and performance of the GARCH-M(1,1), GJR(1,1), and log-GARCH(1,1) models with Normal and Student-t error distributions that fitted to financial data. Empirical analyze is based on the application of models and methods to the DJIA, S&P500, and S&P CNX Nifty stock indices. The first empirical result showed that Excel’s Solver’s Generalized Reduced Gradient (GRG) non-linear method has capability to estimate the econometric models. Second, the GJR(1,1) models provide the best fitting, followed by the GARCH-M(1,1), GARCH(1,1), and log-GARCH(1,1) models. This study concludes that Excel’s Solver’s GRG non-linear can be recommended to the practitioners that do not have enough knowledge in the programming language in order to estimate the econometrics models. It also suggests to incorporate a risk premium in the return equation and an asymmetric effect in the variance equation.

The Indonesian capital market is one of the investment destination countries for investors in developed countries. The development of economic conditions in Indonesia itself is considered suitable for investors to invest. Insurance sector stocks are one of the sectors that are the target of investors. This study predicts the share price of insurance companies. Data in daily form from 2010 to 2020 uses the Autoregressive Conditional Heteroskedasticity - Generalized Autoregressive Conditional Heteroscedasticity (ARCH - GARCH) method. The results showed that forecasting that was carried out until 2025 all the insurance companies studied experienced an upward trend in stock prices. Investors can manage their funds by increasing or decreasing the insurance stock portfolio and adjusting the asset allocation with the investment strategy.

A recession is part of the economic cycle that occurs in a certain period. This article investigates the effects of volatility spillover in the COVID-19 pandemic that occurred in 2020 using stock market index data from the US and ASEAN countries: Indonesia, Malaysia, Singapore, Thailand, and the Philippines. Investigations in the prior, during, and post of the 2008 crisis period were also investigated to analyze the differences between the two. This study used the BEKK-MGARCH model to analyze the spillover effect of volatility between stock indices. The results are not much different from previous research from Vo (2020), where all ASEAN stock markets except the Philippines were affected by the volatility spillover by the US market. In general, from the two periods, each ASEAN index also gives a bidirectional influence of volatility to other ASEAN indices, with the JKSE, KLSE, and SET indices having the most volatility integrated with other indices and the PSE index being the least integrated.

Perdagangan saham suatu negara memiliki karakteristik yang sama ataupun berbeda dengan negara lainnya. Karakteristik dari pasar tersebut merupakan cerminan karakter dari investor yang berperan dalam perdagangan di bursa saham tersebut. Meskipun ada perbedaan ataupun persamaan karakter pada bursa saham suatu negara, terdapat suatu hal yang dialami oleh semua bursa saham di berbagai negara, yaitu pergerakan nilai harga saham dan volume pada perdagangan saham secara dinamis yang dikenal dengan istilah volatilitas. Volatilitas sebagai risiko yang harus dihadapi seorang investor dalam berinvestasi memerlukan kemampuan memprediksi volatilitas sehingga risiko kerugian yang ditanggung investor bisa diperkecil. Model peramalan volatilitas dengan metode Garch dan E Garch diharapkan mampu menjadi salah satu pertimbangan investor dalam membuat keputusan investasi yang rasional. Kata Kunci: Volatilitas, Garch, E Garch, Saham

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The study aimed to analyze the effects of oil and gold price volatility on stock returns in Indonesia by comparing the period before and during the Covid-19 pandemic. The study took secondary data from the daily closing prices of oil (Brent and WTI), gold, and JCI. The analysis technique used was GARCH (1,1). The study found that oil and gold price volatility did not affect stock returns in the two periods. The impact of the Covid-19 pandemic on financial markets had yielded uncertain results. This finding supported the concept of gold as a safe haven during the financial crisis. The limitations in the study were focusing on the Indonesian capital market, and future research can compare the impact of the Covid-19 pandemic on developing countries with developed countries.

This literature aims to analyze the impact of the Dollar Index and Gold Price returns and volatility on stock market volatility of India and China, viz., Shanghai Stock Exchange and Bombay Stock Exchange Sensex, during the period of Covid-19. This study employs daily time-series data from January up to August for 2019, 2020, and a merged data of 2019-2020, i.e., Pre-Pandemic, Mid-Pandemic and Pre through Mid-Pandemic periods, respectively; to avoid possible abnormalities and heteroscedasticity, the GARCH (1,1) model is utilized to scrutinize the data depending on which distribution is more acceptable, GED or Gaussian, which is decided based on the Unit-Root and normality test results. The findings of this study prove that Gold Price mostly does have a significant effect on both markets, especially during times of financial crisis like the Covid-19 epidemic. Whereas Dollar Index has a significant impact on emerging markets such as India and China though significant effects persist in some cases, it is not valid in most cases. DOI: 10.26905/jkdp.v25i3.5142

The stock market is continuously changing with uncertainties that can create risks. Prompt information dissemination and rapid capital flow will cause stock price fluctuations, causing volatility in stock prices. This research examines the behavior of volatility patterns in the infrastructure, utility, and transportation sectors using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This study uses monthly data from January 2014 to December 2019. The results show that the volatility of all stocks in the study is influenced by the previous month's error and volatility return. Investors and securities analysis can use these results in making decisions to invest in the infrastructure, utilities, and transportation sectors.

The aim of this study is to examine variables that influenced the Jakarta Composite Index (JCI) volatility during the outbreak of COVID-19. The independent variables that are used are gold return volatility and USD/IDR return volatility. This analysis uses daily-time series data which are divided into three periods, those are; before the pandemic (January 2019-August 2019), during the pandemic (January 2020-August 2020), and from January 2019 through August 2020. The method that is used in this study is GARCH analysis in order to dodge data abnormality and heteroscedasticity. Before conducting GARCH analysis, unit root and normality test are conducted to know if the data are stationaries and to decide which GARCH distribution is the most suitable for the data analysis (Gaussian, t-student, or GED distribution). It is proven in this study that during the COVID-19 pandemic, gold return volatility positively affected the JCI volatility and USD/IDR volatility negatively affected JCI volatility. This research can be used as a consideration for investors in choosing their investment during the pandemic or financial crisis by examining gold and USD/IDR volatility effect on JCI volatility. DOI: https://doi.org/10.26905/jkdp.v25i1.4980

Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst losses that occur in the stock portfolio with a certain level of confidence and in certain period of time. In general, financial data has a high volatility value, which is caused the variance of residual model is not constant and nonnormally distributed. In this case, Copula-GARCH can be used to calculate the VaR. The Generalized Autoregressive Conditional Heterocedasticity (GARCH) model can resolve the time series models that have non-constant residual variance. This research use the t-Copula to model the dependency structure in the combined distribution of stock returns. The t-copula function is good in terms of reaching the extreme value state that often occurs in the financial data of stock returns and has heavytails. The empirical data uses the stock return data of PT. Indofood Sukses Makmur, Tbk (INDF) and Bank Mandiri (Persero) Tbk (BMRI) in the period of October 8, 2012 - October 8, 2017. In this research, Value at Risk is calculated using the period 1 day ahead at 90% confidence level that is 0.042, at 95% confidence level that is 0.025 and at 99% confidence level that is 0.017 with weight of each stock is 50%.

Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARFIMA models.It is also assumed that the stock risk premium correlates with market risk premiums, and stock risk premiums some time before. The correlation will be analyzed using the ARMAX-GARCH model approach. The Modified Value-at-Risk was then formulated based on the Capital asset Pricing Model with the ARMAX-GARCH model approach.To measure the performance of Modified Value-at-Risk that has been formulated is done with back testing. Back testing is carried out based on the Lopez II method. As a case study, analyzed some data on 10 stocks traded on the capital market in Indonesia.The results of the analysis show that the market index return risk premium significantly follows the ARFIMA model, and the 10 share risk premium significantly follows the ARMAX-GARCH model. Based on the results of back testing calculations indicate that the Value-at-Risk Adjustedor Modified Value-at-Risk is very suitable to be used to measure investment risk in the 10 stocks analyzed.

This study proposed two new classes of GARCH(1,1) model by applying the Tukeytransformations to the returns and to the lagged variance. The behavior of return volatility was investigated on the basis of models with normal and Student-t distributions for return error. The competing models were estimated by using the Excel Solver and Matlab tools. The empirical analysis is based on simulated data, daily exchange rates of the IDR/USD, and daily stock indices of FTSE100 and TOPIX. This study recommends the use of Excel Solver for finance academics and practitioners working on volatility using GARCH(1,1) models. Our empirical findings conclude that GARCH(1,1) models under Tukey transformations should be considered in risk management decisions since the models are more appropriate than standard for describing returns and volatility of financial time series and its stylized facts including fat tails and mean reverting. The Tukey transformed returns imply a shorter volatility half-life, and thus this study suggests that investors should invest the observed assets in a shorter time period to obtain higher returns.

The purpose of this study is to analyze the role of gold as safe haven or hedge for mining stocks in Indonesia during the COVID-19 pandemic period. The data used in this study are mining stock index data (JASICA) daily closing on the Indonesia Stock Exchange and daily closing gold price data on the international market during the period January 2020 - May 2020. Data analysis was performed using QREG to see the potential of gold as a safe haven and GARCH 1.1 to see the potential of gold as a hedge. The results of this study are gold can serve as a robust safe haven for Indonesian mining stocks when there is extreme shocks occur in the stock market. In addition, in this study gold also can serve as a hedge for mining stocks in Indonesia. So investors are advised when the Indonesian Capital Market conditions are experiencing uncertainty as it is today and investors want to move their assets into safe and liquid instruments, gold is the first choice for investors to secure their assets because gold is proven to be the safe haven.

This research is motivated by Bitcoin that tends to be used as an alternative investment tool, and stock which is one of the most popular investment tools in the investment world. Both of these investments have characteristics of high fluctuating price movements. Thus, this study agrees to examine how much risk is faced by investors when choosing to invest in Bitcoin and Jakarta Composite Index (JKSE), using the Value at Risk (VaR) method. This research is using VaR calculation to estimates losses on an asset with a certain level of confidence and in a certain period and involves the GARCH model to estimate volatility. This research is used secondary data consisting of daily price data of Bitcoin obtained from coingecko.com and JKSE obtained from finance.yahoo.com. The research period is from January 1, 2017, to December 31, 2019. The results showed that Bitcoin has a higher risk compared to JKSE, which is about five times higher.

Penelitian ini membahas tentang pembentukan portofolio optimal menggunakan model Mean Absolute Deviation (MAD) dan model Conditional Mean Variance (CMV). Pada model MAD risiko portofolio diukur menggunakan rata–rata deviasi standar sehingga portofolio optimal dapat diperoleh dengan menggunakan pemrograman linear. Sedangkan portofolio model CMV, rata–rata return diestimasi menggunakan model Autoregressive (AR) dan risiko (variansi) diestimasi menggunakan model GARCH. Selanjutnya kedua model portofolio diterapkan dalam membentuk portofolio optimal pada saham–saham yang terdaftar dalam Indeks Saham Syariah Indonesia (ISSI) periode 4 Juli 2016 sampai 4 Juli 2018. Kinerja kedua portofolio dianalisis menggunakan indeks Sortino. Hasilnya menunjukan bahwa kinerja portofolio model CMV lebih baik dibandingkan model portofolio MAD. [This study discusses the formation of optimal portfolios using the Mean Absolute Deviation (MAD) model and the Conditional Mean Variance (CMV) model. The MAD portfolio model measures portfolio risk by using average standard deviations so that optimal portfolios solved by using linear programming. Meanwhile the CMV portfolio model, the average return estimated by using the Autoregressive (AR) model and the risk (variance) estimated by using the GARCH model. Furthermore, both portfolio models applied in forming optimal portfolios for stocks listed in the Indonesian Syariah Stock Index (ISSI) for the period 4 July 2016 to 4 July 2018. The performance of both portfolios analyzed by using the Sortino index. The results show that the portfolio performance of the CMV model is better than MAD portfolio model.]

Relatif tidak stabilnya kondisi ekonomi global dikarenakan krisis global tahun 2008 membuat para investor cenderung lebih berhati-hati dalam memilih sektor saham untuk dapat menghindari risiko yang terjadi. Untuk dapat meminimalkan terjadinya risiko tersebut, investor dapat melakukan pengukuran risiko, salah satunya adalah dengan metode Value at Risk (VaR). Investor dapat menggunakan nilai VaR sebagai salah satu tolak ukur untuk dapat menetapkan seberapa besar target risiko di masa yang akan datang. Perhitungan VaR dalam penelitian ini berbasis model ARCH, GARCH, dan EGARCH, tujuannya untuk membandingkan kinerja ketiga model tersebut untuk memilih model mana yang paling efektif diterapkan pada kondisi pasar normal maupun saat krisis. Untuk menguji validitas dari model VaR ini, dilakukan sebuah uji yang dinamakan uji backtesting dengan menggunakan metode Kupiec dengan cara membandingkan actual return dengan VaR harian. Penelitian ini bertujuan untuk mengetahui tingkat efektifitas Model Value at Risk dengan Metode Kupiec pada JKSE, KLSE, PSEI, HSI, KOPSI, SSE, dan N225. Jenis penelitian yang digunakan pada penelitian ini adalah deskriptif komparatif. Pengambilan sampel penelitian dilakukan dengan metode nonprobability sampling jenis purposive sampling. Hasil penelitian ini menyatakan pengujian Value at Risk dengan tingkat kepercayaan 99% dan 95% untuk periode tahun 2005 sampai dengan tahun 2012 tidak dapat diterima (ditolak) untuk seluruh indeks saham yang diteliti, menggunakan model ARCH dan GARCH dan dapat diterima sebagian untuk model EGARCH

Surprise macroeconomic news causes high volatility in stock market return to the stock market becomes riskier. This study aims to analyze the effects of surprise from the announcement of the United States (US) and domestic macroeconomic news on the LQ45 stock returns volatility. There are 25 stocks chosen because consistently in LQ45 during the 2013 - 2018 research period. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is used to analyze the volatility of returns for each stock. The analysis shows that negative surprise from the Bank Indonesia benchmark interest rate, positive surprise from Indonesia’s trade balance, positive surprise from Consumer Price Index US, and positive surprise from ISM Manufacturing US have a significant effect in reducing volatility return and making most LQ45 Stocks return more stable and less risky. Other macroeconomic surprises show different directions of influence. Finally, this study also provides recommendations for the investor to choose stocks according to their respective risk profiles. The risk averse investor can invest in PT Astra International Tbk (ASII), PT Lippo Karawaci Tbk (LPKR) and PT AKR Corporindo Tbk (AKRA) which have low volatility during the release of surprise macroeconomic, while the risk taker investor can invest to PT Astra Agro Lestari Tbk (AALI), PT Vale Indonesia Tbk (INCO), and PT. Media Nusantara Citra Tbk (MNCN) which respond to many surprises of macroeconomic news.

The objective of investment in the capital market is to acquire dividends and capital gain. The fact proves that the advantage of investation risky assets is uncertain . This is because of the difficulty in analyzing and predicting Return and stock losses due to factors that affect the movement of the stock price , such as economic factors , political , social , and security. The model can be used by investors in predicting stock returns expected that Generalized Autoregressive Conditional Heteroscedaticity (GARCH). In this study calculations beta value of some leading stocks in Indonesia by using Generalized Autoregressive Conditional Heteroscedaticity (GARCH) are presented . The data used this search is secondary data covering daily data sampled 5 shares of PT Unilever Indonesia Tbk , PT Indosat Tbk , PT Indofood Sukses Makmur Tbk , PT Telkom Indonesia Tbk , PT Holcim Indonesia Tbk. From the results described fifth beta value of these shares using the method GARCH beta greater than the market in the period from 23 September 2013 until 24 September 2014.

Dalam pemodelan harga saham, sering dihadapkan pada suatu pertanyaan, apakah model GARCH atau GJR yang lebih tepat merepresentasikan pergerakan harga saham? Secara teori, model GJR adalah perbaikan dari model GARCH, karena model GJR melibatkan parameter ketaksimetrisan. Untuk menjawab pertanyaan ini, maka dipandang perlu untuk melakukan kajian terhadap kinerja kedua model tersebut khususnya dalam mengestimasi risiko pasar. Tujuan dari makalah ini adalah mengestimasi nilai VaR dan AVaR menggunakan model model GARCH dan GJR kemudian membandingkan kinerja kedua model tersebut dalam memodelkan risiko investasi pada saham PT. Telkom. Hasil kajian menunjukan bahwa VaR dan AVaR dengan pendekatan model GJR memberikan agresifitas yang lebih tinggi jika dibandingkan dengan model GARCH.

Salah satu alat ukur yang digunakan untuk menghitung risiko portofolio adalah Value at Risk (VaR). Beberapa metode pengukuran VaR mengasumsikan return berdistribusi normal dan ukuran dependensi antar saham menggunakan korelasi linear. Faktanya, asumsi normalitas pada data finansial jarang terpenuhi dan terdapat indikasi adanya heteroskedastisitas. Selain itu, kebergantungan antar saham yang non-linear tidak sesuai apabila diukur dengan korelasi linear. Penyimpangan ini menyebabkan tidak validnya estimasi VaR. Tujuan dari penelitian ini adalah untuk mengetahui penerapan metode GARCH-Vine Copula untuk estimasi VaR pada portofolio. Vine Copula adalah fungsi distribusi multivariat yang menggabungkan distribusi marginal return univariat dalam portofolio, dan dapat menggambarkan struktur kebergantungan non-linearnya. Vine Copula dapat dilakukan dengan menentukan dekomposisi Vine Copula dan fungsi keluarga copulanya. Dekomposisi Vine Copula dilakukan dengan menggunakan C-Vine dan D-Vine Copula. Kemudian dengan menggunakan fungsi copula keluarga Archimedean, yaitu Clayton, Gumbel, dan Frank dapat ditentukan distribusi bersamanya. Pembentukan distribusi marginal menggunakan model GARCH berdistribusi Student-t digunakan untuk mengatasi adanya heteroskedastisitas. Hasil penerapan dari tiga saham perbankan, yaitu BBNI, BBRI, dan BMRI periode 26 Agustus 2013 hingga 20 November 2017 diperoleh model D-Vine Copula dengan fungsi copula Frank adalah model terbaik untuk memodelkan data, dengan nilai VaR sebesar 1,86%, 2,56%, dan 4,49% dari dana investasi pada tingkat kepercayaan 90%, 95%, dan 99%. [One of the measurement instrument that are used to calculate the risk of portfolio is Value at Risk (VaR). Several methods of measuring VaR assumes normal and the size of dependencies return between the stock using a linear correlation. Basically, the assumption normal in financial data is violated and the possibility of heteroscedasticity is indicated. In addition, dependences non-linear is not appropriate when measured with a linear correlation. This deviation causes invalidity VaR estimation. The purpose of this research is to know the application of GARCH-Vine Copula method for estimation of VaR on portfolio. Vine Copula is a multivariate distribution function that combines the univariate marginal distribution of return in portfolio, and it can describe the structure of dependencies non-linear. Vine Copula can be done by determining the decomposition of Vine Copula and its copula family function. Vine Copula decomposition is using C-Vine and D-Vine Copula. Then by using the copula function of the Archimedean family, namely Clayton, Gumbel, and Frank can be determined the joint distribution. The facts, the formation of the marginal distribution of GARCH model using the student-t distribution used to overcome the presence of heteroscedasticity. The result of the application of these stocks namely BBNI, BBRI, and BMRI from 26 August 2013 to 20 November 2017 has shown model D-Vine Copula copula functions with Frank is the best one to model the data. So, the VaR estimation at 90%, 95%, and 99% confidence levels are 1,86%, 2,56%, and 4,49% respectively of the invested funds.]

Value at Risk (VaR) is a concept which was used to measure a risk on risk management. VaR explained the worst amount of financial loss in a financial product with the horizon and certain degree of believe. In the calculation of VaR, it was needed a prediction in volality, volality from a series of time which can be homokedasticity (constant) or heterokedasticity (ever changed). Changed volality can be found on the stock and stock index. One of the method which was done in modeling of changed volality was GARCH. In this research, GARCH was used to estimate VaR’s Value from IHSG and LQ45 to be sold in Jakarta Stock Exchange on 4 January to 23 August 2012 (650 observations) VaR can be calculated with a periode of horizon, 1 day, 10 days, and 22 days with the degree of believe 95%

Agricultural sector plays an important role in Indonesia‟s economy; especially for the plantation sub-sector contributing high revenues to Indonesia‟s exporting sectors. The primary agricultural commodities in Indonesian export discussed in this study would be Crude Palm Oil (CPO), Natural Rubber TSR20, Arabica Coffee, Robusta Coffee, Cocoa, White Pepper and Black Pepper. Meanwhile, the returns volatility nature of agricultural commodity is famous. The volatility refers to heteroscedasticity nature of the returns which can be modeled by GARCH-type models. The returns volatility can be describe by the residual of the mean equation and volatility of error variances in the previous periods. The aims of this study are to examine the predictability of GARCH-type models on the returns volatility of those seven agricultural commodities and to determine the best GARCH-type models for each commodity based on the traditional symmetric evaluation statistics. The results find that the predictability of ARCH, GARCH, GARCH-M, EGACRH and TGARCH, as type of GARCH models used in this study, are different for each commodity.

Cocoa plays an important role in generating Indonesian foreign exchange revenues since it is one of Indonesia’s primary commodity exports. Meanwhile, as part of plantation commodity, cocoa’s price also has volatility nature. This study has two aims: to examine the predictability of GARCH-type models (ARCH, GARCH, GARCH-M, EGARCH, and TGARCH) on the cocoa’s returns volatility and to determine the best predictability model among the significant GARCH-type models. Two independent variables used in this study are the residual from the mean equation and volatility of error variances in the previous periods. The prices used are spot price series in periods of January 2005 to June 2011 from BAPPEBTI (Indonesian Commodity Futures Trading Regulatory Agency – CoFTRA). The results show that GARCH-M and EGARCH models are the best prediction models.

In this paper we formulated mean-VaR portfolio optimization through CAPM Koyck transformation. We assumed that lagged of risk premium which have highly influence on stock returns is infinite, while model parameters decrease geometrically. We also assumed that rate of return in risk premium market index is not constant, in other word has a non-constant volatility rate, and also has a long memory effect. The later was analyzed using ARFIMA. Non constant volatility rate was modeled via GARCH model. The portfolio optimization was constructed using Langrangian multiplier and the Kuhn-Tucker theorem was employed to obtain the solution by the least square method. Finally, we provide a numerical example of the optimization model based on several stocks traded in Indonesian capital market.

The main objective of this paper is to estimate parameters in the heteroskedasticity models, particularly in Auto Regressive Conditional Heteroskedasticity - ARCH(1) and Generalized Autoregressive Conditional Heteroskedasticity- GARCH(1,1). These models will be used to fit, to forecast and to update the volatility of Rupiah Vs US.Dollar rate. In order to get the estimation of fitting and updating parameters of ARCH(1) and GARCH(1,1), here will be used iterative method which is derived from the standard maximum likelihood estimation and the initial values are taken from the result of Yule Walker Estimation. The updating parameters will be estimated by using the approach of ARIMA(p,d,q) updating parameters models. The heteroskedasticity models will give a good fitting even a good forecast in near stasioner condition, however this models can not detect the jump that can be happend due to the changes of political situation that happend in Indonesia. Abstract in Bahasa Indonesia : Tujuan utama dari penelitian ini adalah untuk menentukan nilai estimasi pada parameter-parameter yang terdapat pada model-model heteroskedastik, khususnya dalam Auto Regressive Conditional Heteroskedasticity - ARCH(1) dan Generalized Autoregressive Conditional Heteroskedasticity- GARCH(1,1). Model-model ini akan digunakan untuk menentukan, meramalkan dan memperbaharui nilai parameter dari nilai tukar mata uang Rupiah terhadap Dollar Amerika. Nilai estimasi pada model ARCH(1) dan GARCH(1,1) diperoleh dengan metode iteratif yang diturunkan dari estimasi maksimum likelihood baku dan nilai awalnya didapat dari pendekatan Yule Walker. Penentuan nilai parameter yang diperbaharui akan diestimasi dengan menggunakan pendekatan model ARIMA(p,d,q). Model-model heteroskedastik memberikan nilai pendekatan nilai tukar yang baik bahkan memberikan nilai peramalan yang baik pula, namun demikian model ini belum dapat mendeteksi terjadinya loncatan yang terjadi yang diakibatkan oleh perubahan situasi politik di Indonesia. Kata kunci: ARCH, GARCH, YWE, MLE, Heteroskedasticity