Pemanfaatan Software Open Source R dalam pemodelan ARIMA

R (R Development Core Team, 2009) merupakan salah satu software open source yang terpopuler dan telah menjadi “lingua franca” atau “bahasa standar” untuk keperluan komputasi statistika saat ini. Dalam tulisan ini, akan dikenalkan dan dibahas penggunaan R untuk komputasi model ARIMA, yang merupakan salah satu model standar yang dikenalkan dalam kuliah analisa runtun waktu. Pengenalan dilakukan dengan menggunakan data empiris dimana komputasi model ARIMA dilakukan dengan menggunakan R versi CLI (command line interface) dan versi GUI (Graphical User Interface) yang merupakan hasil pengembangan terbaru dalam Rosadi, Marhadi dan Rahmatullah (2009). Dalam metodologinya, dikenalkan teknik pemodelan standar dengan menggunakan metode Box-Jenkins, maupun teknik pemilihan model automatik menggunakan ukuran kriteria informasi, seperti yang dibahas di Hyndman dan Khandakar (2008). Kata-kata kunci: R Commander Plug-in, Open Source, automatic ARIM

Estimasi VaR Dengan Pendekatan Extreme Value

Setiap bentuk investasi memiliki risiko yang besar kecilnya tergantung pada banyak faktor, misalnya tingkat kepercayaan (α) dan juga waktu (T). Risiko pada setiap instrumen investasi tersebut dapat diukur dan dikelola sehingga para investor terhindar dari risiko kerugian yang besar. Value at Risk adalah salah satu alat untuk mengukur risiko investasi yang sangat populer. Dalam paper ini akan dikaji model pengukuran risiko Value at Risk dengan pendekatan model extreme value. Selanjutnya metode pendekatan ini akan dipergunakan untuk menganalisis data harga saham yang diperdagangkan di pasar modal Indonesia. Kata Kinci : Investasi, pengukuran risiko, Value at Risk, model Extreme Valu

Portfolio optimization based on self-organizing maps clustering and genetics algorithm

In this modern era, gaining additional income is necessary to fulfill daily needs since inflation is unavoidable. Investing in stocks can give passive income to help people deal with the increasing prices of necessities. However, selecting stocks and constructing a portfolio is the major problem in investing. This research will illustrate the stock selection method and the optimization method for optimizing the portfolio. Stock selection is carried out by clustering using Self-organizing Maps (SOM). Clustering will show the best stocks formed for a portfolio to be optimized. The best stocks that have the best performance are selected from each cluster for the portfolio. The best performance of the stock can be determined using the Sharpe Ratio. Optimization will be carried out using a Genetic Algorithm. The optimization is carried out using software R i386 3.6.1. The optimization results are then compared to the Markowitz Theory to show which method is better. The expected return on the portfolio generated using Genetic Algorithm and Markowitz Theory are 3.348458 and 3.347559975, respectively. While, the value of the Sharpe Ratio is 0.1393076 and 0.13929785, respectively. Based on the results, the best performance of the portfolio is the portfolio produced using Genetic Algorithm with the greater value of the Sharpe Ratio. Furthermore, the Genetics Algorithm optimization is more optimal than the Markowitz Theory

The Adequateness of Wavelet Based Model for Time Series

In general, time series is modeled as summation of known information i.e. historical information components, and unknown information i.e. random component. In wavelet based model, time series is represented as linear model of wavelet coecients. Wavelet based model captures the time series feature perfectly when the historical information components dominate the process. In other hand, it has low enforcement when the random component dominates the process. This paper proposes an eort to develop the adequateness of wavelet based model, when the random component dominated the process. By weighted summation, the data is carried to the new form which has higher dependencies. Consequently, wavelet based model will work better. Finally, it is hoped that the better prediction of wavelet based model will be carried to the original prediction in reverting process

Model Suku Bunga Multinomial

Makalah ini adalah merupakan pengembangan dari model suku bunga binomial seperti yang telah banyak dikenal. Dengan menggunakan asumsi multinomial diharapkan model binomial dapat diperluas menjadi model suku bunga multinomial. Jika diketahui suku bunga sampai saat ini, maka suku bunga pada periode berikutnya akan mempunyai k + 1 nilai yang mungkin. Sifat path-independence akan membuat bentuk multinomial relatif lebih sederhana. Dengan sifat ini jika diketahui sekarang waktu ke 0 dan suku bunga memiliki k + 1 nilai yang mungkin pada periode berikutnya, maka pada waktu ke t akan ditemukan hanya tk + 1 nilai yang mungkin. Kata kunci: model suku bunga, multinomial, term structure, contingent claim

Model Pengoptimuman Portofolio Mean-Variance dan Perkembangan Praktisnya

Many research about portfolio optimization in Indonesia still uses the ‘original’ mean-variance model as proposed by Markowitz more than 60 years ago. This article reviews the development and modification of the Markowitz’s mean-variance model, especially that dealing with real stock-market features, which could help the investor to create their own portfolio. There were several real-stock market features that implemented in the modification of mean-variance portfolios optimization models, such as the minimum transaction lots, the transaction cost, the cardinality constraint, the weight constraint, and the sectoral constraint. To implement these features, several heuristic methods were used to obtain the optimal portfolio weight, such as genetic algorithm, Tabu search, bee colony algorithm, particle swarm algorithm, and simulated annealing. These methods become alternative to the mathematical programming method

CAPM (Capital Asset Pricing Model) with Stable Distribution

In the classical finance theory, the CAPM models are developed using the Gaussian framework, that is, weassume the vector of returns can be modeled using the multivariate normal distribution. However, it is foundempirically that typically the financial data, especially the returns of assets, are leptokurtic (i.e., it is heavy tail andpeaked around the center). It has been shown in the literature that the stable distribution, where the normal is of aspecial case, becoming one of the popular model to model leptokurtic data. In this paper, we analyse the CAPMunder the assumption that the data follows the stable non-normal distribution with the index ofstability1 <α < 2 . We finally provide empirical application of the CAPM under the Gaussian and stable casesusing several returns data from Indonesian Stock Market

Rplugin.econometrics: Paket Graphical User Interface Open Source Untuk Analisis Runtun Waktu Menggunakan Perangkat Lunak R

R (R Development Core Team, 2009) is one of the open source software that is popular and has become "lingua franca" or standard language for the purposes of computing the current statistics. In this paper, will be introduced and discussed RcmdrPlugin.Econometrics package (Rosadi, Marhadi and Rahmatullah, 2009), which is a GUI version (Graphical User Interface) of R for the purposes of econometric analysis or time series. RcmdrPlugin.Econometrics package is an additional menu (plug-in) which provided for the R Commander, which is the most popular GUI of R. To illustrate the design philosophy of this package, provided also illustrate the USAge of the RcmdrPlugin.Econometrics package for the exponential smoothing

Soil Physical Characteristics and Saturated Hydraulic Conductivity in the Landform of Barito Delta, Kalimantan, Indonesia

We explore the soil physical characteristics in wetland of Barito Delta from primary data of soil sample and electrical resistivity measurement with the support from some secondary data. We also estimate saturated hydraulic conductivity (Ks) in Barito Delta from soil physical characteristics applying Saxton and Rawls (1986) and Weynants et al. (2009). Soil texture profile is determined from interpolation of soil fraction in each layer applying Bayesian statistics to analyze soil physical characteristics in the landforms of Delta. Clay is the dominant soil fraction in the soil of Barito Delta. Clay fraction percentage decrease along the depth of soil profile as it reaches fine sand particles deriving from ancient sedimentation from the past. It is an opposite with soil organic matter content that has contrast value from 1st to 2nd soil depth, but a few discrepancy from 3rd depth to downward direction. The content of clay in the soil depends on the sedimentation activity in the landform. Clay is dominant soil particle in the Delta; in case, it is in flat area and there is no intensive of sea water sedimentation such as in Basin of Peat Anticline and Natural Levee. In more than 2 m depth of soil, loamy sand and silty clay textures are mostly in the landform that is influenced by sea water activity, while by river water is clay loam. Ks values from Saxton and Rawls (1986) are close to Ks values from the measurement of previous studies. Ks values are generally small in Barito Delta that is mostly ranging from 1.10-7 to 2 m s-1. Ks values are larger following the depth of soil profile. The values of Ks are smaller in Basin of Peat Anticline and Natural Levee than in Tidal Flat and Beach Ridge. It is because both landforms have clay as dominant soil particles.</p