Impact of reserve requirement and Liquidity Coverage Ratio: A DSGE model for Indonesia

2021 ◽  
Author(s):  
Tevy Chawwa
Keyword(s):  
Dsge Model ◽  
Reserve Requirement ◽  
Coverage Ratio

scholarly journals Monetary Policy and Reserve Requirement in a DSGE Model with Financial Frictions

2015 ◽  
Vol 19 (4) ◽  
pp. 579 ◽  
Author(s):  
José Angelo Divino ◽  
Alexandre Kornelius
Keyword(s):  
Monetary Policy ◽  
Financial Frictions ◽  
Dsge Model ◽  
Reserve Requirement

Este artigo modifica o modelo DSGE de Gertler & Karadi (2011), que inclui fricção sobre o balanço dos intermediários financeiros, para introduzir a exigência de recolhimentos compulsórios pela Autoridade Monetária e um choque de confiança dos depositantes no sistema financeiro. Os impactos dessas mudanças sobre os canais de transmissão da política monetária são analisados. Os resultados indicam que a presença de compulsório amplifica a transmissão da política monetária pelo canal do crédito, aumentando a alavancagem dos bancos quando há uma queda nos juros e diminuindo caso contrário. A diminuição do crédito quando os juros aumentam pode ser contrabalanceada por uma política macroprudencial de ajuste do nível de compulsório baseada em uma regra que depende de desvios do crédito no estado estacionário. O compulsório não deve, porém, substituir a taxa de juros como o instrumento de política monetária mais adequada para estabilizar a inflação.

Panel Data Analysis of Determinants of Leverage in the Automobile Industry in India

GIS Business ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. 29-47
Author(s):  
Vibha Tripathi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Automobile Industry ◽  
Panel Data Analysis ◽  
Negative Relationship ◽  
Order Theory ◽  
Ordinary Least Squares ◽  
Pecking Order ◽  
Trade Off ◽  
Coverage Ratio

The study tries to investigate the key determinants of capital structure of leading automobile companies and the Automobile Industry in India. The study also tracks the theory implications, i.e. trade off vs. pecking order in these firms and the industry in general. An attempt is to see, if individually each sample company and the whole industry are influenced by the same determinants of capital structure. Pooled ordinary least squares and panel data econometric techniques such as fixed effect models are used to investigate the most significant determinants that affect the capital structure choice of 10 leading companies categorized as BSE Auto Top 100 and the Automobile Industry as a whole for a period of 14 years from 2000–2001 to 2013–2014. The study reveals some interesting facts and results. Multiple regression analysis reveals that while profitability and size are significant determinants in most of the leading companies; NDTS, Growth, and Debt service coverage ratio are not significant for these companies. While the Panel data results of the Automobile Industry as a whole reveals that profitability is the only significant determinant having negative relationship with debt equity ratio; and the other variables are insignificant. Also individual companies coefficient results shows implications of mix of pecking order and trade off theories while the panel data results of the whole Industry strongly supports the Pecking order theory.

Pengaruh Financial Leverage terhadap Financial Performance pada Sektor Industri Manufaktur yang Terdaftar di Bursa Efek Indonesia (BEI) Periode 2015

2018 ◽  
Vol 9 (2) ◽  
pp. 33-48
Author(s):  
Rivaldy Februansyah ◽  
Ika Yanuarti
Keyword(s):  
Financial Performance ◽  
Manufacturing Sector ◽  
Linear Regression Analysis ◽  
The Other ◽  
Debt Ratio ◽  
Financial Leverage ◽  
Coverage Ratio ◽  
Equity Ratio ◽  
Internal Fund

The manufacturing sector is one of the most dominant economic sectors in in achieving growth and development in Indonesia. It needs adequate fund to develop its business. The sources of fund are from internal and external. The firm usually optimized the usage of internal fund prior to external fund. The internal fund comes from equity while the external funds are from debt and stock. Debt is also known as financial leverage. There is a phenomenon that the usage of debt increased the firm’s financial performance, since interest on debt could lower the payment of tax (tax shield). On the other side, the higher the financial leverage the higher the risk of bankruptcy. This research aims to analyze whether financial leverage has an influence on financial performance in the manufacturing sector listed on the Indonesia Stock Exchange (IDX) period 2015. The method of analysis used in this research is multiple linear regression analysis. This research uses quantitative approach with a sample of 140 listed companies in the manufacturing industry. The firm’s financial performance could be measured by the financial ratios. Financial Leverage ratios are ratios that measure the ability of firm’s to meet its financial obligation and the level of usage debt as compared to equity. There are several financial leverage ratios that used in this research, such as Debt Ratio (DR), Debt to Equity Ratio (DER), Interest Coverage Ratio (ICR), and Long Term Debt Ratio (LTDR). Financial performance indicates the ability of firm to generate profit and measured by Profitability Ratio. Return on Asset (ROA) is one of the Profitability Ratio. The statistical result shows that Debt Ratio (DR) negatively affect Return on Asset (ROA) and Interest Coverage Ratio (ICR) positively affect Return on Asset (ROA). Meanwhile, Debt to Equity Ratio (DER) and Long Term Debt Ratio (LTDR) did not affect Return on Asset (ROA). On the other hand, result shows that Debt Ratio (DR), Debt to Equity Ratio (DER), Interest Coverage Ratio (ICR), and Long Term Debt Ratio (LTDR) affect Return on Asset (ROA) simultaneously. Keywords: Financial Leverage, Debt Ratio (DR), Debt to Equity Ratio (DER), Interest Coverage Ratio (ICR), Long Term Debt Ratio (LTDR), Financial Performance, Return on Assets (ROA)

An Open-Economy DSGE Model of the Euro Area

2006 ◽  
Author(s):  
Pascal Jacquinot ◽  
Ricardo Mestre ◽  
Martin Spitzer
Keyword(s):  
Euro Area ◽  
Open Economy ◽  
Dsge Model

Effect of Capital Structure Determinants on Profitability: With Special Reference to Iron and Steel Sector Units of India

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Neeraj Gogia
Keyword(s):  
Capital Structure ◽  
Sample Size ◽  
Financial Risk ◽  
Iron And Steel ◽  
Coverage Ratio ◽  
Independent Variables ◽  
Operating Profit ◽  
Return On Capital ◽  
Steel Sector ◽  
Equity Ratio

This paper is an attempt to study the capital structure of Indian Steel Industry and its major determinants. In this study, almost 50% of companies out of 22 sample size are bearing highly debt driven in their capital structure and it creates financial risk to the debt driven companies. Debt driven companies have obligation to pay interest irrespective of profit made or loss incurred by the firms. Hence we tried to find out which are the various factors significantly explaining the return on capital employed. For which we have considered four independent variables from early studies and employed correlation analysis, multiple regression analysis techniques and ANOVA in this study to test the dependency of the return on capital employed ratio on independent variables. The researcher found three variables such as debt equity ratio, operating profit ratio and interest coverage ratio respectively having significant impact on the return on capital employed to ratio of selected sample size of steel companies.

Multi-AUV Aided Cooperative 3D-localization for Underwater Sensor Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Meiyan Zhang ◽  
Wenyu Cai
Keyword(s):  
Sensor Networks ◽  
Mean Squared Error ◽  
Autonomous Underwater Vehicles ◽  
Estimation Method ◽  
Location Estimation ◽  
Underwater Sensor Networks ◽  
Coverage Ratio ◽  
Minimum Mean Squared Error ◽  
3D Localization ◽  
Localization Scheme

Background: Effective 3D-localization in mobile underwater sensor networks is still an active research topic. Due to the sparse characteristic of underwater sensor networks, AUVs (Autonomous Underwater Vehicles) with precise positioning abilities will benefit cooperative localization. It has important significance to study accurate localization methods. Methods: In this paper, a cooperative and distributed 3D-localization algorithm for sparse underwater sensor networks is proposed. The proposed algorithm combines with the advantages of both recursive location estimation of reference nodes and the outstanding self-positioning ability of mobile AUV. Moreover, our design utilizes MMSE (Minimum Mean Squared Error) based recursive location estimation method in 2D horizontal plane projected from 3D region and then revises positions of un-localized sensor nodes through multiple measurements of Time of Arrival (ToA) with mobile AUVs. Results: Simulation results verify that the proposed cooperative 3D-localization scheme can improve performance in terms of localization coverage ratio, average localization error and localization confidence level. Conclusion: The research can improve localization accuracy and coverage ratio for whole underwater sensor networks.

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