"Optimal Leverage Strategies for Asian REITs "

2018 ◽  
Author(s):  
Tien Sing ◽  
Carolyn Chang ◽  
Kian Lim
Keyword(s):  
Optimal Leverage

scholarly journals Determinan Leverage Optimal Di Bursa Efek Indonesia

2020 ◽  
Vol 17 (1) ◽  
pp. 94-107
Author(s):  
Purwanto Widodo ◽  
Juardi Juardi
Keyword(s):  
Capital Structure ◽  
Dynamic Models ◽  
Stock Exchange ◽  
Dynamic Panel Data ◽  
Method Of Moment ◽  
Manufacturing Companies ◽  
Macroeconomic Factors ◽  
Trade Off ◽  
Optimal Leverage ◽  
Dynamic Capital Structure

Research on capital structure, recently characterized by the use of dynamic capital structure. The use of dynamic capital structure basically wants to know the existence of optimal leverage as hypothesized by Trade-Off Theory and Speed off Adjustment (SOA) to optimal leverage. This research tries to overcome this problem, by using dynamic panel data by using company characteristics and macroeconomic factors. The use of General Method of Moment (GMM) to overcome the problem of econometrics due to the use of dynamic models. Samples taken from manufacturing companies listing on the Indonesia Stock Exchange 2009-2015. The inference model and the determinant behavior of capital structure can be explained by Trade-Off Theory and Pecking Order Theory. The variable characteristics of the company and macro economy are significant and are marked according to the hypothesis. The findings of this study include: the influence of profitability, size, tangibility, growth opportunity and business risk. In addition, on average companies in Indonesia can increase their debt to utilize tax shields

Heterogeneity and Asymmetry in Speed of Leverage Adjustment: The Indian Experience

2016 ◽  
Vol 19 (03) ◽  
pp. 1650019 ◽  
Author(s):  
Surenderrao Komera ◽  
P. J. Jijo Lukose
Keyword(s):  
Capital Structure ◽  
Cash Flow ◽  
Emerging Market ◽  
Adjustment Costs ◽  
Group Affiliation ◽  
Cross Sectional ◽  
Speed Of Adjustment ◽  
Business Group Affiliation ◽  
Optimal Leverage ◽  
Target Leverage

In this paper, we examine firms' capital structure adjustment behavior and estimate their “speed of adjustment” toward optimal leverage ratios by employing a dynamic, partial adjustment model. We find that sample firms on an average offset half of the deviation from their target leverage ratios in less than one and half (1.41) years. Such evidence suggests optimal capital structure behavior among sample firms. Further, we report cross sectional heterogeneity and asymmetry in speed of adjustment estimates, resulting from varied leverage adjustment costs across the sample firms. We find higher speed of adjustment estimates among larger sample firms suggesting higher leverage adjustment costs for smaller firms. Business group affiliation does not seem to influence the costs of sample firms' leverage adjustment. Over-levered firms report higher speed of adjustment estimates, suggesting that sample firms do not consider debt financing as a “disciplining mechanism” for managers. Further, we find lower speed of adjustment estimates for sample firms with higher cash flow, implying that Indian markets do not actively accommodate firms' cash flow needs. Thus, our findings reveal complex asymmetric information problems and consequent varied leverage adjustment costs among emerging market firms.

Optimal Leverage in Day Trading

2018 ◽  
pp. jot.2018.1.062
Author(s):  
Christian Lundström
Keyword(s):  
Day Trading ◽  
Optimal Leverage

scholarly journals DESTABILIZING EFFECTS OF BANK OVERLEVERAGING ON REAL ACTIVITY—AN ANALYSIS BASED ON A THRESHOLD MCS-GVAR

2017 ◽  
Vol 22 (7) ◽  
pp. 1750-1768 ◽  
Author(s):  
Marco Gross ◽  
Jerome Henry ◽  
Willi Semmler
Keyword(s):  
Regime Switching ◽  
Large Scale ◽  
Banking System ◽  
Real Activity ◽  
Vector Autoregressive ◽  
Joint Dynamics ◽  
Global Vector ◽  
Optimal Leverage ◽  
The Relationship ◽  
Activity Dependent

We investigate the consequences of overleveraging and the potential for destabilizing effects arising from financial- and real-sector interactions. In a theoretical framework, we model overleveraging and demonstrate how a highly leveraged banking system can lead to unstable dynamics and downward spirals. Inspired by models developed by Brunnermeier, Sannikov and Stein, we empirically measure the deviation-from-optimal-leverage for a sample of large EU banks. This measure of overleveraging is used to condition the joint dynamics of credit flows and macroeconomic activity in a large-scale regime change model: a Threshold Mixed-Cross-Section Global Vector Autoregressive (T-MCS-GVAR). The regime-switching component of the model is meant to make the relationship between credit and real activity dependent on the extent to which the banking system is overleveraged. We find significant nonlinearities as a function of overleverage. The farther the observed leverage in the banking system from optimal leverage, the more detrimental is the effect of a deleveraging shock on credit supply and economic activity.

Optimal Leverage and Aggregate Investment

1999 ◽  
Vol 54 (4) ◽  
pp. 1291-1323 ◽  
Author(s):  
Bruno Biais ◽  
Catherine Casamatta
Keyword(s):  
Aggregate Investment ◽  
Optimal Leverage

Asset Variance Risk Premium and Capital Structure

2020 ◽  
pp. 1-45
Author(s):  
Babak Lotfaliei
Keyword(s):  
Risk Premium ◽  
High Exposure ◽  
Asset Return ◽  
Variance Risk Premium ◽  
Model Free ◽  
Return Variance ◽  
Increasing Risk ◽  
Variance Risk ◽  
Investment Grade ◽  
Optimal Leverage

Abstract This article investigates how the asset-return variance risk premium changes leverage. I find that the premium reduces leverage by increasing risk-neutral bankruptcy probability and costs in a model where asset returns have stochastic variance with the risk premium. Empirically, the model calibrations verify a significant reduction in optimal leverage, closer to observed leverage than the model without the premium. In model-free regressions, I document that leverage correlates negatively with the variance premium. The highest negative correlation is among investment-grade firms with low asset beta and historical variance but high variance premiums because their assets have high exposure to the market’s variance premium.

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