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All Journal Media Statistika Statistika Jurnal Gaussian MIX : Jurnal Ilmiah Manajemen Indonesian Journal of Applied Statistics (JRAMB) Jurnal Riset Akuntansi Mercu Buana

Di Asih I Maruddani

Department Of Statistics, Faculty Of Science And Mathematics, Diponegoro University

Author-ID : 3131520

Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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EXPECTED SHORTFALL DENGAN SIMULASI MONTE-CARLO UNTUK MENGUKUR RISIKO KERUGIAN PETANI JAGUNG Rita Rahmawati; Agus Rusgiyono; Abdul Hoyyi; Di Asih I Maruddani
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In risk management, risk measurement plays an important role in allocating capital as well as in controlling (and avoiding) worse risk. Estimating the risk value can be done by using a risk measure. The most popular method for evaluating risk is Value at Risk (VaR). But VaR does not fulfill the coherency as a measure of risk effectiveness. In this paper, we propose Expected Shortfall (ES) which has coherency nature. ES is defined as the conditional expectation of losses beyond VaR of the same confidence level over the same holding period. For measuring ES, we use Monte-Carlo Simulation Method. This method is applied for measuring risk that will be faced by corn’s farmers due to the changes in corn prices in Pemalang city. The results show that the ES value is 0.085472 at 95% confidence level and one-month holding period. This number means that a farmer will face 8.5472% of investment as maximum loss exceeding of VaR.

PERHITUNGAN VALUE AT RISK DENGAN PENDEKATAN THRESHOLD AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY-GENERALIZED EXTREME VALUE Mutik Dian Prabaning Tyas; Di Asih I Maruddani; Rita Rahmawati
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock is the most popular type of financial asset investment. Before buying a stock, an investor must estimate the risks which will be received. Value at Risk (VaR) is one of the methods that can be used to measure the level of risk. When investing in stock, if an investor wants to earn high returns, then he must be prepared to face higher risks. Most of stock return data have volatility clustering characteristic or there are cases of heteroscedasticity and the distribution of stock returns has heavy tail. One of the time series models that can be used to overcome the problem of heteroscedasticity is the ARCH/GARCH model, while the method for analyzing heavy tail data is Extreme Value Theory (EVT). In this study used an asymmetrical ARCH model with the Threshold ARCH (TARCH) and EVT methods with Generalized Extreme Value (GEV) to calculate VaR of the stock return from PT Bumi Serpong Damai Tbk for the period of September 2012 to October 2018. The best chosen model is AR([3])–TARCH(1). At the 95% confidence level, the maximum loss an investor will be received within the next day by using the TARCH-GEV calculation is 0.18%.

IMPLEMENTATION OF STOCHASTIC MODEL FOR RISK ASSESSMENT ON INDONESIAN STOCK EXCHANGE Di Asih I Maruddani; Trimono Trimono; Mas'ad Mas'ad
MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.2.151-162

Currently, financial assets become an alternative choice for investors in Indonesia to get maximum profits. The Indonesia Stock Exchange is the official capital market in Indonesia which is a place for trading financial assets. Stocks are listed as the most preferred financial asset by investors. In reality, stock investment is not a risk-free investment. The main risk that investors should face is the loss risk. This kind of risk can occur at any time. From that problem, this study aims to do risk assessment on the Indonesian stock market. The evaluation will be started with stock price index prediction using the Stochastic model (Geometric Brownian Motion Model and Jump Diffusion). Then, the result from that processes will be used to get loss risk prediction through the Adjusted Expected Shortfall model. By using the historical price of JKSE index from 01/08/21 to 31/08/22, Jump Diffusion is the best model to predict the JKSE index with MAPE value is 1.08%. Then, at the 95% confidence level and 1-day holding period, the expected loss risk using Adjusted Expected Shortfall model on 09/01/2022 is -0.02978.

PENGARUH SKEWNESS DAN KURTOSIS DALAM MODEL VALUASI OBLIGASI Abdurakhman Abdurakhman; Di Asih I Maruddani
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Gram-Charlier expansion, where skewness and kurtosis directly appear as parameters, has become popular in finance as a generalization of the normal density. Non-normal skewness and kurtosis of underlying asset of bond issuer company are significantly contributes to the phenomenon of volatility smile. Hermite polynomial is used to get an expansion of the probability distribution. In this paper, Gram-Charlier model is applied to BTPN Bond which is issued in 2017. The result showed that Gram-Charlier model is more consistent than Black-Scholes model when the skewness and kurtosis are taken into account.Keywords: Skewness, Kurtosis, Gram-Charlier, Hermite polynomial

AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA Puspita Kartikasari; Hasbi Yasin; Di Asih I Maruddani
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.44-55

Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long memory). The result of this determination is the best ARFIMA model obtained to predict additional of recovering cases of COVID-19 is (1,0,489.0) with an SMAPE value of 12,44%, while the case of death is (1.0.429.0) with SMAPE value of 13,52%. This shows that the ARFIMA model can accommodate well the long memory effect, resulting in a small bias. Also in estimating model parameters, it is also simpler. For cases of recovery and death, the number is increasing even though the case of death is still very high compared to cases of recovery.

Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Agus Rusgiyono Ahmad Badruttamam Alifia Hana Linda Rachmawati Atikah Mufidah Budi Warsito Dede Andrianto Dedi Baleo Pasaribu Delsy Nurutsaniyah Desi Nur Rahma Diah Safitri Dina Rahma Prihatiningsih Dwi Harti Pujiana Dwi Ispriyanti Eka Anisha Eny Sulistyowati Fatiya Nur Umma Fredy Yoseph Marianto Hanik Malikhatin Hasbi Yasin Hasbi Yasin Henny Setyowati Juria Ayu Handini Khoirul Anam Lina Tanasya Maria Odelia Mas'ad Mas'ad Meiliawati Aniska Mia Anastasia Sinulingga Mustafid Mustafid Mutik Dian Prabaning Tyas Nidaul Khoir Nur Fitriyah Nur Khofifah Purwati Ayuningtyas Puspita Kartikasari Puspita Kartikasari Putri Devitasari Reno Penggalih Surya Wardhani Reyuli Andespa Ria Epelina Situmorang Rita Rahmawati Rukun Santoso Salsabila Syifa Binsanno Setiani Setiani Sudarno Sudarno Sugito Sugito Suparti Suparti Tarno Tarno Tatik Widiharih Trimono Trimono Trimono Trimono Tutut Dewi Astuti Tutut Dewi Astuti Yustian Dwi Saputra

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