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Tuesday, August 11, 2015

Analisis Perbandingan Efisiensi Bank Syariah di Indonesia Dengan Metode Data Envelopment Analysis (periode Tahun 2005)

Analisis Perbandingan Efisiensi Bank Syariah di Indonesia Dengan Metode Data Envelopment Analysis (periode Tahun 2005)

MUHARAM, Harjum (2007) Analisis Perbandingan Efisiensi Bank Syariah di Indonesia Dengan Metode Data Envelopment Analysis (periode Tahun 2005). Jurnal Ekonomi dan Bisnis Islam , II (3). pp. 80-166.

Abstract

Syariah banking industrial improvement in Indonesia cause increasing of competition level between bank, especiallY after economics crisis,so the appraisal of bank} efficiencybecomesmore important, because the efficiencyimages of compa'!J work. The approached method valuation that used to measure bank efficienryin this paper is Data Envelopment .Analysis (DEA), a technique linearprogramming that calculating output ratio to input eachDMU (Decision Making Unit). DMU calledefficient if the efficiency value is one (100 percent), if less than one it means DMU not efficient. DEA also be availablegive solutionfor other banks on sample that was not efficient to repair it self to be more efficient. Two input factors and three output factors lvere used in this stucfy. Constant Return to Scale (CRS) method with output oriented and intermediation approach is used in this paper.This research attempt to anaIYze relative ejJicienry of Indonesia syanab banking in year 2005 and compare it according to each groups (syariab publicbanking-[Jariah units, BUMN-Non BUMN syariab banking, detnsa-non deoisa private national syariab banking). The samples of 12 banks which have almost all share of national syariab banking in Indonesia were gathered from the total population of 114 bank of syariab banking industrial in Indonesia. The result indicates that in year 2005, three syariab banks alwqys get perfea score ejJicient 100 percent; there are BIN Syariah, Niaga Syariah and Pe17JJataSyariah. Nine syariah banks other are not aliuays ejJicient. Syariah Mandin Bank notyet ejJicient inyear 2005. Then, not all banks on perfec: ejJicient condition become example for inejJicient banks to repair it se!! to be more ejJicient. After DEA calculate ejJicienry score .from each banks, we compare this ejJicienry score according three groups (syariah public banking-[Jariah units, BUMN-Non BUMN syariah banking, detnsa-non detnsa private national syariab banking). FinallY, we get the result that there is no significantlY differences ejJicienry score in each groups. So, its means that ejJicienry of Indonesia syariab banking is good in year 2005
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Government Bond Yield Volatility and It's Determinants: The Case of Indonesia Government Bond



Harjum Muharam 


Diponegoro University - Economics and Business Faculty

December 12, 2013

The First International Conference on Finance and Banking Faculty of Economics and Business Diponegoro University-Indonesia Financial Management Association (IFMA), December 11-12, 2013, Bali, Indonesia 

Abstract:      
This research is conducted from gaps of research findings regarding factors influencing government bond yield. The aim of this research is to develop a model of government bond yield determinants and to test hypothesis about the effect of inflation, foreign reserves, local interest rate, stock market return, exchange rate, foreign interest rate, world oil prices, real sector performance, and conditional variances on government bond yield. Time series process and multifactor models are employed. The model combines two approaches called Multifactor EGARCH-M Model. The population is Indonesian government bond, denominating in IDR and has a fixed coupon rate. The sample selected is five years tenor bond. The findings are: (1) Indonesia’s government bond yield has volatility clustering as measured by GARCH process; (2) based on adjusted R2, logL, Akaike Information Criterion (AIC) and Schwarz Criterion (SC), Multifactor EGARCH-M Model is the best model among six models developed; (3) as a proxy of market risk and default risk, GARCH-M has the biggest effect on its yield followed by non gold reserve; (4) the other variables having influences on government bond yields are: local interest rate, stock market return, exchange rate, foreign interest of rate, and world oil price. Inflation and real sector performance have no effect on government bond yields.
Number of Pages in PDF File: 25
Keywords: yield, volatility, government bonds, multifactors model, conditional variance, GARCH


JEL Classification: G120

VAR Analysis on Mutual Relationship between Stock Price Index and Exchange Rate and the Role of World Oil Price and World Gold Price

Filus Raraga and Harjum Muharam
Faculty of Economics and Business Diponegoro University
ABSTRACT
This study aims to analyze the influence of world oil price and world gold price on mutual relations between exchange rate and stock price index. This study uses monthly data of exchange rate (IDR/US$) and JCI from January 2000 to January 2013. Co integration test was used in analyzing long-term relationships between variables. VAR model was used in determining whether world oil prices and world gold price affect the exchange rate and stock index, and analyze the interrelationships between exchange rate and stock price index. Impulse Response Analysis is used to determine the response of exchange rate and the stock price index on world oil price shocks and world gold price shocks. Analysis of Variance Decomposition is used to determine the role of world oil prices and world gold prices in explaining the movement of exchange rate and JCI. Co integration analysis results show that all the variables, ie, world oil prices, gold prices, exchange rates and JCI have long run co integration. The analysis showed that the world oil price has significant effect on the exchange rate  but has no effect on JCI;  the world gold price has no effect on exchange rate and JCI; exchange rate has significant effect on JCI and vice versa. Granger causality test showed that JCI and exchange rate have bidirectional relationship. Impulse Response Analysis results indicate that the world oil price shocks responded negatively by exchange rate; shocks in world gold prices responded negatively by JCI and exchange rate; exchange rate changes responded positively by JCI, and JCI changes responded positively by exchange rate.

Keyword: JCI, exchange rate, world oil price, world gold price, VAR Analysis.