• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.307-318
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• 2021

The objective of this study is to analyze the performance of Islamic banks with the Maqashid Shariah approach. The analysis technique used is the Simple Additive Weighting Method (SAW) to solve multi-attribute decision problems. The sampling technique used was purposive sampling while the data came from the annual report of each bank. The results showed that the BTPN Shariah (BTPNS) and Bank Muamalat Indonesia (BMI) are ranked first and second respectively on the Maqashid Shariah Index (MSI) with values of 0.265429 and 0.237110 respectively. Panin Dubai Shariah Bank (PDSB) ranked third with an MSI value of 0.180733, followed by BCA Shariah which ranked fourth with an MSI value of 0.151299. BRI Shariah ranked fifth with an MSI value of 0.128606, followed by BNI Shariah which ranked sixth with an MSI value of 0.124661. Bank Mega Shariah ranked last with an MSI value of 0.087068. Furthermore, there is a relationship (correlation) between ROE, ROA, and OEOI and MSI since each data has a value of 0.000, 0.000, 0.050, and 0.001 respectively, which is smaller than the significance value of 0.05. On the other hand, NPF, TPF, and Asset Growth Rates do not correlate with the MSI since each data has a value of 0.051, 0.252, and 0.215 respectively which is greater than the significance value of 0.05.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.