Gold Return Volatility Modeling Using Garch

Hasanah, Primadina; Nasir, Siti Qomariyah; Subchan, Subchan

doi:10.31002/ijome.v2i1.1222

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Both ARCH and GARCH model are reasonably good models for analyzing volatility financial time series variables. They are essential tools to capture heteroscedastic behavior or volatility clustering without the requirement of higher order models in various financial markets (Hasanah et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Modeling the Rwanda Exchange Rates by GARCH Models

Mudahogora¹,

Ndanguza²

2021

ajas

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Volatility modeling and forecasts are essential tools to all financial sectors. This paper focuses on weekly exchange rate returns of the FRW versus USD from 2012 until 2018 obtained from the National Bank of Rwanda. The aim of this paper is to formulate an appropriate GARCH model which fits the data. The GARCH(1,1) model has been selected after using required techniques of model selection.Parameters have been estimated using Least Squares method first and then validated using MCMC method. Once the chain of parameters are found, both visual inspection and basic statistics are computed and in this study, they have illustrated a good compatibility between simulation and observations. Diagnostic of convergence of the chains of parameters has been checked and ensured the model to beaccurate. The results obtained from the LSQ and MCMC methods have been compared and found to be almost similar. An agreement between the model solution and actual data is obtained and a forecast is done by concluding that the estimated values are almost similar to the real data. Hence, the identified model is accepted for forecasting and recommended for further applications.

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Section: Methodsmentioning

confidence: 99%

Modeling the Rwanda Exchange Rates by GARCH Models

Mudahogora¹,

Ndanguza²

2021

ajas

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“…Research [14] explained that the forecasting model with GARCH obtained statistically significant results compared to other volatility forecasting models such as MLP, GRNN, GMDH, RF, QRRF, and QRNN on eight financial data sets. It was also found that ARCH / GARCH could be used to study voltages in other fields such as agriculture ( [15] & [16]), and gold by [17]- [26] are similar in relation to the use of the ARCH / GARCH method in dealing with the symptoms of volatility. and [27].…”

Section: Introductionmentioning

confidence: 92%

Analysis of Rewards Results of Banking Shares Book III Registered in Indonesia Stock Exchange

Artanto¹,

Kurniasih²

2021

Proceedings of the 1st MICOSS Mercu Buana International Conference on Social Sciences, MICOSS 2020, September 28-29, 2020, Jaka

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Stock is one of the investment instruments that many investors choose. One of the industrial sectors on the Indonesia Stock Exchange is the financial sector. Banking is one of the financial sub-sectors. Book III banking stocks are included in the top 50 market capitalization category on the IDX in 2019. Book III bank stock returns fluctuate, indicating volatility that occurs. This study analyzes the stock returns of Book III using the ARCH / GRACH approach. The study also aims to find the best ARCH / GARCH model to solve the volatility problem that occurs in Book III stock returns. The results showed that the highest average return was generated by MAYA shares. For the ARCH / GARCH approach, the best model in the volatility of return of banking stocks in Book III, the majority use the GARCH model (1,1).

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“…Several previous studies have been conducted to observe and test the volatility of gold prices including Hasanah et al (2019), Bratha et al (2017), Kristjanpoller & Minutolo (2015), Bentes (2015), Basher & Sadorsky (2016), Kaminski (2013) and Ahmad & Sara (2012). In addition, there are also several studies that focus on the comparison of the Black Scholes and GARCH models.…”

Section: Hypotheses Developmentmentioning

confidence: 99%

Hedging strategy in emerging market: Application long straddle option in gold price index

Hendrawan

Akbar

Yuniarti

2020

J. Keuang. dan Perbank.

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This research was conducted to test the implementation of gold price index option contracts using the Black Scholes and GARCH models with a long straddle strategy. The testing is done by looking at the comparison of the results of the calculation from the historical volatility value and the GARCH volatility. The results of the study are displayed by looking at the comparison of the Average Mean-square Error (AMSE) percentage values of the two models. From the research that has been done, it shows that the Black Scholes model has a better gold price index option contract than the GARCH model for maturities of 1 month, 2 months and 3 months.This is shown from the AMSE value of call options and put options in the Black Scholes model which is always smaller than the GARCH model for each contract maturity period. In addition, the potential for maximum profit by implementing the long straddle strategy in gold price index option contracts in the range of 2008-2018 is 54.98 percent with an average profit potential of around 25-30 percent. Abstrak Penelitian ini dilakukan untuk menguji implementasi kontrak opsi indeks harga emas menggunakan model Black Scholes dan GARCH dengan strategi long straddle. Pengujian dilakukan dengan melihat perbandingan hasil perhitungan dari nilai volatilitas historis dan volatilitas GARCH. Hasil penelitian ditampilkan dengan melihat perbandingan nilai persentase Average Mean-square Error (AMSE) kedua model. Dari penelitian yang telah dilakukan didapatkan hasil bahwa model Black Scholes memiliki kontrak opsi indeks harga emas yang lebih baik dari pada model GARCH untuk jangka waktu 1 bulan, 2 bulan dan 3 bulan, hal ini ditunjukkan dari nilai AMSE call options dan put option dalam model Black Scholes yang selalu lebih kecil dari model GARCH untuk setiap periode jatuh tempo kontrak. Selain itu, potensi keuntungan yang maksimal dengan menerapkan strategi long straddle pada kontrak opsi indeks harga emas pada kisaran tahun 2008-2018 adalah sebesar 54,98 persen dengan potensi keuntungan rata-rata sekitar 25-30 persen.

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Gold Return Volatility Modeling Using Garch

Cited by 7 publications

References 2 publications

Modeling the Rwanda Exchange Rates by GARCH Models

Modeling the Rwanda Exchange Rates by GARCH Models

Analysis of Rewards Results of Banking Shares Book III Registered in Indonesia Stock Exchange

Hedging strategy in emerging market: Application long straddle option in gold price index

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