scholarly journals Volatilitas Kurs dan Saham Mengikuti Model EGARCH(1,1) Berdistribusi Versi Skew Normal dan Student-t

2020 ◽  
pp. 257
Author(s):  
Didit Budi Nugroho ◽  
Anggita M Kusumawati ◽  
Leopoldus R Sasongko
Keyword(s):  
Random Walk ◽  
Akaike Information Criterion ◽  
Information Criterion ◽  
Linear Solver ◽  
Random Walk Metropolis ◽  
Non Linear ◽  
Reduced Gradient ◽  
Us Dollar ◽  
Generalized Reduced Gradient ◽  
Skew Normal

Studi ini membandingkan kinerja pencocokan model volatilitas GARCH(1,1) dan EGARCH(1,1) pada return kurs dan saham. Model mengasumsikan empat distribusi berbeda untuk error dari return: Normal, Skew-Normal (SN), Alpha-Skew Normal (ASN), dan Student-t. Data aset keuangan yang digunakan sebagai analisis perbandingan yaitu data kurs beli US Dollar (USD) dalam periode harian dari Januari 2010 sampai Desember 2017 dan data indeks saham FTSE100 dalam periode harian dari Januari 2000 sampai Desember 2013. Studi ini membandingkan metode Generalized Reduced Gradient (GRG) Non-Linier di Solver Excel dan metode Adaptive Random Walk Metropolis (ARWM) untuk mengestimasi model. Hasil menunjukkan bahwa metode GRG Non Linear Solver Excel memberikan estimasi yang serupa dengan metode ARWM dan tidak melanggar kendala model. Lebih lanjut, berdasarkan nilai Akaike Information Criterion (AIC), kedua data pengamatan menyediakan bukti bahwa model dengan distribusi Student-t adalah yang terbaik, diikuti oleh distribusi SN yang lebih baik daripada model dengan distribusi ASN dan Normal. Nilai AIC telah menyarankan model EGARCH(1,1) berdistribusi Student-t sebagai model pencocokan terbaik untuk kedua data pengamatan.

scholarly journals Volatility Fitting Performance of QGARCH(1,1) Model with Student-t, GED, and SGED Distributions

2020 ◽  
Vol 11 (2) ◽  
pp. 97-104
Author(s):  
Didit Budi Nugroho ◽  
Bintoro Ady Pamungkas ◽  
Hanna Arini Parhusip
Keyword(s):  
Stock Exchange ◽  
Linear Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Information Criterion ◽  
Estimation Methods ◽  
Data Sets ◽  
Non Linear ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

The research had two objectives. First, it compared the performance of the Generalized Autoregressive Conditional Heteroscedasticity (1,1) (GARCH) and Quadratic GARCH (1,1) (QGARCH)) models based on the fitting to real data sets. The model assumed that return error follows four different distributions: Normal (Gaussian), Student-t, General Error Distribution (GED), and Skew GED (SGED). Maximum likelihood estimation was usually employed in estimating the GARCH model, but it might not be easily applied to more complicated ones. Second, it provided two ways to evaluate the considered models. The models were estimated using the Generalized Reduced Gradient (GRG) Non-Linear method in Excel’s Solver and the Adaptive Random Walk Metropolis (ARWM) in the Scilab program. The real data in the empirical study were Financial Times Stock Exchange Milano Italia Borsa (FTSEMIB) and Stoxx Europe 600 indices over the daily period from January 2000 to December 2017 to test the conditional variance process and see whether the estimation methods could adapt to the complicated models. The analysis shows that GRG Non-Linear in Excel’s Solver and ARWM methods have close results. It indicates a good estimation ability. Based on the Akaike Information Criterion (AIC), the QGARCH(1,1) model provides a better fitting than the GARCH(1,1) model on each distribution specification. Overall, the QGARCH(1,1) with SGED distribution best fits both data.

scholarly journals Asymptotic analysis of the random walk Metropolis algorithm on ridged densities

2018 ◽  
Vol 28 (5) ◽  
pp. 2966-3001 ◽  
Author(s):  
Alexandros Beskos ◽  
Gareth Roberts ◽  
Alexandre Thiery ◽  
Natesh Pillai
Keyword(s):  
Random Walk ◽  
Asymptotic Analysis ◽  
Metropolis Algorithm ◽  
Random Walk Metropolis ◽  
Random Walk Metropolis Algorithm

Optimization of a Novel Combined Power/Refrigeration Thermodynamic Cycle

Solar Energy ◽  
2002 ◽  
Author(s):  
Shaoguang Lu ◽  
D. Yogi Goswami
Keyword(s):  
Waste Heat ◽  
Thermodynamic Cycle ◽  
Cycle Performance ◽  
Working Fluid ◽  
Second Law ◽  
Optimum Conditions ◽  
Ambient Temperatures ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient ◽  
Performance Conditions

A novel combined power/refrigeration thermodynamic cycle is optimized for thermal performance in this paper. The cycle uses ammonia-water binary mixture as a working fluid and can be driven by various heat sources, such as solar, geothermal and low temperature waste heat. It could produce power as well as refrigeration with power output as a primary goal. The optimization program, which is based on the Generalized Reduced Gradient (GRG) algorithm, can be used to optimize for different objective functions. Examples that maximize second law efficiency, work output and refrigeration output are presented, showing the cycle may be optimized for any desired performance parameter. In addition, cycle performance over a range of ambient temperatures was investigated. It was found that for a source temperature of 360K, which is in the range of flat plate solar collectors, both power and refrigeration outputs are achieved under optimum conditions. All performance parameters, including first and second law efficiencies, power and refrigeration output decrease as the ambient temperature goes up. On the other hand, for a source of 440K, optimum conditions do not provide any refrigeration. However, refrigeration can be obtained even for this temperature under non-optimum performance conditions.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Optimasi Pemanfaatan Air Embung Kasih untuk Domestik dan Irigasi Tetes

2019 ◽  
Vol 13 (1) ◽  
pp. 41
Author(s):  
Segel Ginting ◽  
Dadan Rahmandani ◽  
Abid Hendri Indarta
Keyword(s):  
Reduced Gradient ◽  
Generalized Reduced Gradient

Pemerintah membangun Embung Kasih untuk mengatasi terbatasnya sumber air di Desa Tuatuka, Provinsi NTT. Embung tersebut digunakan untuk memenuhi kebutuhan domestik dan irigasi. Pengoperasian embung perlu direncanakan karena volume tampungan terbatas. Optimasi penggunaan air embung diperlukan untuk menentukan jumlah penggunaan air dengan berbagai skenario kondisi hujan. Simulasi penggunaan air tahun 1974 s.d. 2015 dilakukan sebagai evaluasi untuk menilai keberhasilan operasi embung. Penelitian ini dilakukan dengan tujuan untuk menentukan jumlah pemakaian air untuk kebutuhan domestik dan atau irigasi secara optimal. Optimasi dilakukan dengan Metode Generalized Reduced Gradient (GRG) untuk fungsi tujuan memaksimalkan penggunaan air embung. Hasil optimasi diperoleh dengan beberapa skenario. Skenario pertama untuk hujan normal, pemanfaatan air untuk domestik sekitar 2.604 orang atau untuk mengairi lahan seluas 2,746 ha dengan irigasi tetes. Skenario kedua untuk kondisi hujan ekstrim basah, pemanfaatan air untuk domestik sekitar 3.601 orang atau untuk irigasi tetes sekitar 4,698 ha. Skenario ketiga untuk kondisi hujan ekstrim kering, pemanfaatan air untuk domestik sekitar 454 orang atau untuk irigasi tetes sekitar 0,45 ha. Berdasarkan evaluasi hasil optimasi dengan menggunakan simulasi data tahun 1974 s.d. 2015, maka ditetapkan jumlah penggunaan air embung untuk domestik sekitar 454 orang dan irigasi tetes seluas 1 Ha dengan tingkat keandalan operasi embung mencapai 78,57%.

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