MODELING INDONESIAN LQ45 STOCK MARKET INDEX VOLATILITY (APPLICATION OF GARCH AND BAYESIAN GARCH)

A comparative study has been conducted to examine the performance of the GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model and Bayesian GARCH applied to the daily LQ45 stock market index of the Indonesian Stock Exchange, where the innovations of both models are assumed to follow Student-t. The forecasting performance of both models are evaluated with standard statistical measurement. The empirical result shows that there a no different performance between these models. This is an indication that bayesian estimation only an alternative method in predicting LQ45 volatility, but not to increase the predictio

Response-surface dan Taguchi : Sebuah alternatif atau kompetisi dalam optimasi secara praktis

Response-surface telah lebih dahulu muncul sebagai alat analisis optimasi pada skala industri. Berbagai asumsi statistika maupun matematika yang melekat pada metode ini, menjadi sebuah keunggulan sekaligus kekurangan dalam aplikasi praktisnya. Keunggulan Response-surface sangat terlihat ketika model matematis memenuhi seluruh asumsi statistik yang melekat sehingga optimasinya menjadi tidak bias. Hasil sebaliknya terjadi ketika salah satu saja asumsi tersebut tidak terpenuhi. Taguchi, hadir beberapa dekade kemudian, dan memberikan tahapan optimasi yang sangat praktis. Dasar pembentukan desain Taguchi tetap mengacu pada desain eksperimen klasik. Namun, tidak adanya asumsi statistik yang mengikuti tahapan analisisnya membuat metode ini banyak dipilih oleh para praktisi. Taguchi tidak mampu memberikan arah optimasi sebagaimana Response-surface mengakomodasi adanya “steepest ascent/descent”. Bagaimanapun, kedua metode ini dapat saling melengkapi ataupun justru menjadi dua metode yang saling berkompetisi dalam proses optimasi mesin produksi. Paper ini akan memberikan gambaran tentang kedua metode, mulai dari dasar tahapan metodologi eksperimennya, proses analisis, hingga bentuk hasil akhir yang didapatkan dari keduanya. Response-surface dan Taguchi, akan menjadi sebuah akternatif bahkan saling melengkapi, ketika tahapan keduanya dikombinasikan dalam tataran prakti

PENERAPAN OPTIMASI MULTIRESPON MENGGUNAKAN HYBRID PRINCIPAL COMPONENT ANALYSIS - TAGUCHI PADA PROSES TURNING MATERIAL POLYACETAL

Polyacetal merupakan material thermoplastic yang sering digunakan untuk menggantikan beberapa aplikasi-aplikasi metal. Proses turning material ini dipengaruhi oleh parameter cutting speed, feed rate, dan nose radius, dengan multirespon yang diukur yakni kekasaran permukaan (Ra), dan laju pemakanan material (MRR). Optimasi parameter mesin turning diterapkan untuk mendapatkan optimalisasi secara simultan untuk kedua respon tersebut. Principal component analysis (PCA) adalah salah satu teknik reduksi multivariabel yang kemudian dikombinasikan dengan eksperimen taguchi (Hybrid PCA-Taguchi) untuk mendapatkan optimasi multirespon. Hybrid PCA-Taguchi mengakomodasi sifat multivariabel secara statistik yang ditangkap oleh PCA lalu mentransformasikan multirespon menjadi respon tunggal, sehingga Taguchi dapat mengoptimalkannya. Metode ini juga tetap dapat mengakomodasi karakteristik kualitas Taguchi, baik smaller-the-better, nominal-the-best maupun larger-the-better. Penerapan metode ini pada proses turning material polyacetal telah memberikan kombinasi parameter optimal untuk dua respon

Integrating steepest ascent for the taguchi experiment: A simulation study

Many previous researches conveyed the superiority of Steepest Ascent (SA) method to find the optimal area in Response Surface Methodology (RSM) by shifting the experiment factor level. By using this method, Design of Experiment (DoE) is enabled to shift the factor level gradually in the right track, so that the global optimum can be reached. However, the response variable that is commonly optimized by using RSM cannot fulfill the classical statistics assumption of surface regression model. Taguchi’s orthogonal array, as alternative of RSM, gives loose statistics assumptions in performing the analysis. However, Taguchi’s orthogonal array has not yet been supported to shift the factor level to an optimum direction. Adopting the procedures of RSM in finding the optimal level combination using SA, integrating SA method in the Taguchi experiment is proposed in this paper. This procedure is applied into a simulated response surface. Then, the performance of this procedure is evaluated based on its direction to reach the optimum solution. The simulation data representing the real case is generated for two factors. Then, the proposed procedure is applied. The result of this simulation study shows that the integrated SA method in the Taguchi experiment successfully found the factor level combination that yields optimum response even though it is not as close as possible as the RSM results

INCORPORATING SERVQUAL-QFD WITH TAGUCHI DESIGN FOR OPTIMIZING SERVICE QUALITY DESIGN

Deploying good service design in service companies has been updated issue in improving customer satisfaction, especially based on the level of service quality measured by Parasuraman’s SERVQUAL. Many researchers have been proposing methods in designing the service, and some of them are based on engineering viewpoint, especially by implementing the QFD method or even using robust Taguchi method. The QFD method would found the qualitative solution by generating the “how’s”, while Taguchi method gives more quantitative calculation in optimizing best solution. However, incorporating both QFD and Taguchi has been done in this paper and yields better design process. The purposes of this research is to evaluate the incorporated methods by implemented it to a case study, then analyze the result and see the robustness of those methods to customer perception of service quality. Started by measuring service attributes using SERVQUAL and find the improvement with QFD, the deployment of QFD solution then generated by defining Taguchi factors levels and calculating the Signal-to-noise ratio in its orthogonal array, and optimized Taguchi response then found. A case study was given for designing service in local bank. Afterward, the service design obtained from previous analysis was then evaluated and shows that it was still meet the customer satisfaction. Incorporating QFD and Taguchi has performed well and can be adopted and developed for another research for evaluating the robustness of result

Combined Structural Equation Modelling - Artificial Neural Networks Model for Predicting Customer Loyalty

Customer loyalty becomes considerations by service providers to maintain for reducing the churn rate. Many studies propose factors that are significantly influencing customer loyalty, and apply them for predicting it. Based on mathematical models, loyalty prediction methods are developed, and it involves new approaches including machine learning. This research aim is predicting customer loyalty using the combination of structural equation model (SEM) and artificial neural networks (ANN). The methodology starts by applying SEM for selecting statistically significant factors affect the loyalty. The linear SEM model ensures this relationship by fulfilling statistical hypothesis and fulfilled assumptions. Once selected factors are found, they are treated as inputs for ANN modelling. ANN is selected because of its ability in nonlinear modelling to enhance its prediction. ANN then learns the relationship between those inputs and the loyalty in real time as any additional observation recorded in. Based on trained ANN, prediction of customer loyalty based on input factors could be done. A case study was conducted at a Hotel by asking 130 customers. SEM inputs includes tangibles, facility, and staff attitudes, while loyalty scores become output. Combination of SEM-ANN has successfully predicted the customer loyalty and brought up chances for improvement strategies

Comparing Statistical Feature and Artificial Neural Networks for Control Chart Pattern Recognition: A Case Study

Control chart has been widely used for monitoring production process, especially in evaluating the quality performance of a product. An uncontrolled process is usually known by recognizing its chart pattern, and then performing some actions to overcome the problems. In high speed production process, real-time data is recorded and plotted almost automatically, and the control chart pattern needs to be recognized immediately for detecting any unusual process behavior. Neural networks for automatic control chart recognition have been studied in detecting its pattern. In the field of computer science, the performance of its automatic and fast recognition ability can be a substitution for a conventional method by human. Some researchers even have developed newer algorithm to increase the recognition process of this neural networks control chart. However, artificial approaches have some difficulties in implementation, especially due to its sophisticated programming algorithm. Another competing method, based on statistical feature also has been considered in recognition process. Control chart is related to applied statistical method, so it is not unreasonable if statistical properties are developed for its pattern recognition. Correlation coefficient, one of classic statistical features, can be applied in control chart recognition. It is a simpler approach than the artificial one. In this paper, the comparison between these two methods starts by evaluating the behavior of control chart time series point, and measured for its closeness to some training data that are generated by simulation and followed some unusual control chart pattern. For both methods, the performance is evaluated by comparing their ability in detecting the pattern of generated control chart points. As a sophisticated method, neural networks give better recognition ability. The statistical features method simply calculate the correlation coefficient, even with small differences in recognizing the generated pattern compared to neural networks, but provides easy interpretation to justify the unusual control chart pattern. Both methods are then applied in a case study and performances are then measured

Regime Switching GARCH : An Application to Dowjones Index Return

Since firstly proposed by Engle (1982) and Bollerslev (1986), ARCH-GARCH models have been used to describe volatility behaviors of time series, especially in stock market analysis. One of the weaknesses of ARCH-GARCH is its inability to model behavior transition between high volatilities and low volatilities. In this research, markov switching GARCH is investigated and applied to capture the presence of different volatility regimes, i.e. low volatilities regime and high volatility regime in Dowjones index return. However, there is no information to decide which observations belong to each of the regimes, and to account this difficulty, EM algorithm is applied for parameter estimation. The result shows that Dowjones index return includes two volatility regimes. The transition matrix of the model yields that low volatility regime is often happened than the high one

On the Markov Switching GARCH: a Brief Introductory

This paper describes briefly about GARCH with regime switching (SW-GARCH) following Markov Chain property. This approach accounts for jumps between volatility regimes which useful to detect some change of policies during the time horizon are running. To show the work of the employing this approach, an implementation through Unilever stock data has been tried. The results show that the data follow the change between two regimes with probability accordingly

Simulasi Pemodelan Segmented Autoregressive Untuk Peramalan Data Interrupted Time Series

Peramalan merupakan komponen penting yang menjadi bagian dari strategi CPFR (Collaborative Planning, Forecasting, and Replenishment) di dalam optimalisasi Supply Chain. Penelitian ini terfokus pada pengembangan alternatif metode peramalan sederhana untuk data interrupted time series. Model yang dikembangkan berupa segmented regression yang diintegrasikan dengan autoregressive. Model ini mengakomodasi perubahan di dalam data time series yang disebabkan oleh intervensi dari luar data tersebut yang membentuk sebuah bentuk lompatan data. Untuk memperlihatkan kemampuan model segmented autoregressive ini, disimulasikan beberapa bentuk interrupted time series lalu menerapkan segmented autoregressive pada data simulasi tersebut. Hasil yang diperoleh menunjukkan bahwa segmented autoregressive dapat menangkap pola interrupted time series dan tetap memenuhi asumsi pemodelan statistik