Distance to default of Malaysian Corporate Sukuk Issuers: A Differential Study for Different Economic Sectors

Transcription

1. International Journal of Academic Research in Business and Social Sciences Vol . 1 0 , No. 5, May, 2020, E-ISSN: 2 2 2 2 -6990 © 2020 HRMARS Distance to default of Malaysian Corporate Sukuk Issuers: A Differential Study for Different Economic Sectors Awais Ur Rehman, Muhammad Abdullah bin Zaidel, Mohamad bin Jais, Arslan Haneef Malik To Link this Article: http://dx.doi.org/10.6007/IJARBSS/v10-i5/7227 DOI:10.6007/IJARBSS/v10-i5/7227 Received: 06 March 2020, Revised: 18 April 2020, Accepted: 25 April 2020 Published Online: 19 May 2020 In-Text Citation: (Rehman et al., 2020) To Cite this Article: Rehman, A. U., Zaidel, M. A. bin, Jais, M. bin, & Malik, A. H. (2020). Distance to default of Malaysian Corporate Sukuk Issuers: A Differential Study for Different Economic Sectors. International Journal of Academic Research in Business and Social Sciences, 10(5), 546–556. Copyright: © 2020 The Author(s) Published by Human Resource Management Academic Research Society (www.hrmars.com) This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode Vol. 10, No. 5, 2020, Pg. 546 - 556 http://hrmars.com/index.php/pages/detail/IJARBSS JOURNAL HOMEPAGE Full Terms & Conditions of access and use can be found at http://hrmars.com/index.php/pages/detail/publication-ethics 546
2. International Journal of Academic Research in Business and Social Sciences Vol . 1 0 , No. 5, May, 2020, E-ISSN: 2 2 2 2 -6990 © 2020 HRMARS Distance to default of Malaysian Corporate Sukuk Issuers: A Differential Study for Different Economic Sectors Awais Ur Rehman, Muhammad Abdullah bin Zaidel, Mohamad bin Jais, Arslan Haneef Malik Faculty of Economics & Business, University Malaysia Sarawak. Email: ever.awais@gmail.com Abstract: The Malaysia enjoys the monetary and non-monetary benefits for being the sukuk hub for globe. Malaysian market has already suffered much from defaults. To save them from future defaults the periodic update must be taken for the closeness of defaults. This study calculated distance to default (DD) for 231 observations for the time period of 2011-2017 in the post-crisis era. It was noted that the sukuk issuers are although diverse in their credit profiles, on the aggregate most of them are having strong level of assets to absorb their liability pay-offs. The DD values were at highest level for logistics, industrial goods, manufacturing, real estate and construction and lowest in energy sector. Directions for future research were also discussed in concluding part of this paper. Introduction It is reported by the esteemed institute of IMF that Islamic finance has emerged as a strong reality and the part of the financial markets, especially after it has shown a noteworthy resilience against the crisis. Although its market share is not a big chunk, yet it has successfully penetrated in the markets around the globe. The report adds that the rapid growth in Islamic finance was made possible due to the instruments of sukuk. Sukuk can be outreached across the geographic boundaries and has won the investors’ confidence, therefore the industry needs to make sukuk more prosperous. Sukuk has rightfully added economic efficiency and liquidity in the market. It is also import element of Islamic financial industry due to its size. 95% of assets in Islamic financial industry are held by Islamic banks and sukuk issuers (IMF, 2015). Its size is growing even more with an annual rate of 27.8% (Mohammed, 2015). The growth trajectory of sukuk is merely a single side of the story. Among the sukuk issuers all is not going in a perfect and promising way. Till the year 2015, the sukuk issuances kept their growth steady and positive, with the issuances in 2013 at record high. But after the 2015 the sukuk market has entered in the “consolidation phase”. The market under this phase is face different kind of challenges that are pressing the issuances negatively (IIFM, 2016, p. 3). One of these factors, the sukuk defaults in past have given a negative impact on the market. 547
3. International Journal of Academic Research in Business and Social Sciences Vol . 1 0 , No. 5, May, 2020, E-ISSN: 2 2 2 2 -6990 © 2020 HRMARS Only in the Malaysian market 26 cases of sukuk defaults have hit the market by 2000 to 2013, while the 2009 saw the highest cases of sukuk defaults. By the 2009 the market had to bear a series of default. The researchers of Islamic finance have termed this year as the year of default that had long lasting consequences on sukuk market (Shahida, Hafizuddin Syah, Daud, & Hafizi, 2014; Kamarudin, Kamaluddin, Manan, & Ghani, 2014). The financial crisis was hitting the market that triggered the defaults even worst. Researchers are worried that the aftermaths of financial crisis are not over yet, therefore the defaults can be a concern for the market even today (Majid, Shahimi, & Abdullah, 2010). When the defaults erupted the market was largely ignorant of it. The academia, the legal personnel and the rating agencies, were all not expecting those much defaults to be occurred. Only a few cases were known to the market players. But the defaults are not arbitrary in nature. The disturbance in the receivables can create a liquidity issue to honor the payables. This interconnectivity of the market makes the defaults contagious. It caused a sequence of sukuk defaults with 21 cases occurred in 20 months only. Khinfer (2010) recounts that it was lamenting that the researchers were ignorant of it. He describes that if market has not turned blind and would have forecasted the defaults, the precautionary measures could have brought up aforehand and it may have lessened the losses. It was estimated that only in the Malaysia, USD 2.243 billion was the amount of loss that had to be borne by industry due to this ignorance from sukuk clones to defaults (Kamarudin, Kamaluddin, Manan, & Ghani, 2014). The defaults are not easier to be tackled once they have erupted due to their contagious nature. Buckling up the belts before entering a rocky road is better in this case. Hence, it is necessary to timely and periodically update the market on defaults so that the precautions can be implemented timely and aforehand (Khalid, 2007; Saad, Haniff, & Ali, 2016). Hence this study aims to calculate the DD measures for sukuk issuers at the aggregate level and also for different economic sectors separately. Next section explains the discussion relating to DD afterwards the methodology section describes the discussion about data collection. After these the study presents its final results and draws a conclusion thereof. A number of future directions were also discussed for more studies on sukuk. Distance to Default as the Measure of Credit Risk Merton model is used extensively to measure the firms’ proximity to default by the researchers and the practitioners. This fame of Merton model is up in the trend since its inception about a half of century ago (Afika, Aradb, & Galil, 2016; Kim, Batten, & Ryu, 2020). This model uses the market data and the accounting data as well. It is based upon the theory of option pricing model. Stockholders have residual claim on the business assets after the business liabilities have been paid off. Therefore, the stockholders are considered as the buyers of put option. They have an option to buy the business asset in lieu for the payment of liabilities as a strike price. Model furthers its calculations based on some simple economic assumptions. The business asset has a variable value like a Brownian motion. It is not static and carries a variance. Secondly the firm has the liability of the firm can be imagined in the form of single of zero-coupon bond. This bond has a maturity term of T time period. Hence, the defaults can occur on at the elapse of time T not before it. Following to these assumptions the DD calculations can be shown by firstly quantifying the market value of the equity can be evaluated then by the following formula MV= AN (d1) – e-RfT MTL N (d2) Where the variables presented in the equation are 548
4. International Journal of Academic Research in Business and Social Sciences Vol. 1 0 , No. 5, May, 2020, E-ISSN: 2 2 2 2 -6990 © 2020 HRMARS MV: market value of equity A: firm value Rf: Risk free rate MTL: market value of total liabilities Pertinent to notes that all the values are taken from the market data except the variables of distribution. This variable can be calculated as d1 = (ln (A/MTL) + Rf + 0.5 σA2) / σA T While σA2: variance of the firm value T: Time to maturity The value of d2 can be quantified by the equation d2 = d1 - σA √T Moreover, the next equation is used to calculate σA σA = (A/MV) N (d1) / σE σE displays the equity-volatility. Many attempts to improve were made subsequently after the formulation of this model. Baharath and Shumway (2008) also made a similar strife to make the calculations easier and comprehendible. They replaced the sophist nonlinear quantifications with linear and easier ones. Their model was still arched over the option pricing theory, but it gave an easy in the calculations since all the data relating to their new variables are readily and publicly available. They uprooted all the unobservable quantities in the equations of DD. Their approach is termed as naïve model of DD. According to naïve model the volatilities can be calculated as σA. Naïve = MV/MTA σE + TL/MTA σD It is pertinent to note that the TL represents the total liabilities. These liabilities are taken at the book value rather than the market value. It gives an ease to trace this data from financial statements of the firms under discussion. Moreover, σD is the notation for debt volatility and can be processed as σD Naïve = 0.05 + 0.25 σE Equity volatility (σE) is the standard deviation of total equity. By convention 25% of the equity volatility is taken and it is increased with 0.05 for term-structure volatility. These conventions are given by Baharath & Shumway (2008) for the calculations of naïve values of DD. While after the calculations of volatilities, the DD can be calculated with following equation MTA ln( TL ) + T(E 1y − 0.5 σ2A Naive )