STATS20 University of Indianapolis Z Score and Standard Deviations Exam

Anonymous

Question Description

Please read the exam questions below.

In August 2017, Tesla Motors raised $1.8 billion in corporate bonds, priced at 5.3%, 8-year notes. The bonds were subordinated to more senior debt and received a B- rating from S&P and a B3 rating from Moody’s.

(1)Using the credit analytics discussed in our class, and traditional metrics, what does your group think should be Tesla’s bond rating before and just after the new bond issue?

(2)Would your answer change if the firm raised an additional $3 billion in bonds to meet production objectives?

(3)What is your estimate of the Bond Rating Equivalent (BRE) as of the most recent (Q1-2019) financials and latest (June 26, 2019) stock price?

(4)Given your answers #1 and #2 above, what are your expected cumulative PD (Probability of Default) and LGD (Loss Given Default) for Tesla for one-year and five(5) years?

(5)What are the bonds issued in 2017 now (June 26, 2019) selling at and what is the bond’s yield to maturity?

(6)Which of the two Z-Score models (Z or Z”) is most applicable to a firm like Tesla? Why? (7)What are the main differences between Z and Z”? List up to four differences.

Unformatted Attachment Preview

TRIUM Exam Question Professor Edward Altman June 2019 In August 2017, Tesla Motors raised $1.8 billion in corporate bonds, priced at 5.3%, 8-year notes. The bonds were subordinated to more senior debt and received a B- rating from S&P and a B3 rating from Moody’s. (1) Using the credit analytics discussed in our class, and traditional metrics, what does your group think should be Tesla’s bond rating before and just after the new bond issue? (2) Would your answer change if the firm raised an additional $3 billion in bonds to meet production objectives? (3) What is your estimate of the Bond Rating Equivalent (BRE) as of the most recent (Q1-2019) financials and latest (June 26, 2019) stock price? (4) Given your answers #1 and #2 above, what are your expected cumulative PD (Probability of Default) and LGD (Loss Given Default) for Tesla for one-year and five (5) years? (5) What are the bonds issued in 2017 now (June 26, 2019) selling at and what is the bond’s yield to maturity? (6) Which of the two Z-Score models (Z or Z”) is most applicable to a firm like Tesla? Why? (7) What are the main differences between Z and Z”? List up to four differences. Please work on this question with no less than two (2) members and no more than three (3). Do not confer with other groups with respect to any aspect of this question. Limit your answer to no more than three (3) pages, (double-spaced) plus Exhibits (no Appendices). Please make sure the typeface is clear and large enough so it is easily legible.        dZ/hD'>K>D ůĂƐƐŽĨϮϬϮϬ    ^ƚƌĂƚĞŐLJĂŶĚ&ŝŶĂŶĐĞĨŽƌ'ůŽďĂůdžĞĐƵƚŝǀĞƐ    EĞǁzŽƌŬ ^ƵŶĚĂLJϭϲʹ&ƌŝĚĂLJϮϴ:ƵŶĞϮϬϭϵ     ƌĞĚŝƚZŝƐŬŽƵƌƐĞWĂĐŬ WƌĞͲDŽĚƵůĞZĞƋƵŝƌĞĚZĞĂĚŝŶŐ         TRIUM Class of 2020: Module 4 − Pack 3: Credit Risk TRIUM Global Executive MBA THIS PRINT COURSEPACK AND ITS ELECTRONIC COUNTERPART (IF ANY) ARE INTENDED SOLELY FOR THE PERSONAL USE OF PURCHASER. ALL OTHER USE IS STRICTLY PROHIBITED. XanEdu™ publications may contain copyrighted materials of XanEdu, Inc. and/or its licensors. The original copyright holders retain sole ownership of their materials. Copyright permissions from third parties have been granted for materials for this publication only. Further reproduction and distribution of the materials contained herein is prohibited. WARNING: COPYRIGHT INFRINGEMENT IS AGAINST THE LAW AND WILL RESULT IN PROSECUTION TO THE FULLEST EXTENT OF THE LAW. THIS COURSE PACK CANNOT BE RESOLD, COPIED OR OTHERWISE REPRODUCED. XanEdu Publishing, Inc. does not exert editorial control over materials that are included in this course pack. The user hereby releases XanEdu Publishing, Inc. from any and all liability for any claims or damages, which result from any use or exposure to the materials of this course pack. Items are available in both online and in print, unless marked with icons. − Print only − Online only TRIUM Class of 2020: Module 4 − Pack 3: Credit Risk Table of Contents “Toward a Botton−Up Approach to Assessing Sovereign Default Risk: An Update” by Altman, Edward I; Rijken, Herbert 1 “The Fate of the Euro: It is Still Italia!” by Altman, Edward I 31 “Defaults and Returns in the High−Yield Bond and Distressed Debt Market: The Year 2018 in Review and Outlook” by Altman, Edward I; Kuehne, Brenda J 37 “The Investment Performance and Market Dynamics of Defaulted and Distressed Corporate Bonds and Bank Loans: 2018 Review and 2019 Outlook” by Altman, Edward I; Kuehne, Brenda J 116 “A 50−Year Retrospective on Credit Risk Models, the Altman Z−Score Family of Models and Their Applications to Financial Markets and Managerial Strategies” by Altman, Edward I 169 i Toward A Bottom-Up Approach to Assessing Sovereign Default Risk: An Update* Edward I. Altman, New York University Stern School of Business, Herbert Rijken, Vrije University ** Abstract We propose a totally new approach toward assessing sovereign risk by examining rigorously the health and aggregate default risk of a nation’s private corporate sector. Models such as our new Z-Metrics™ approach can be utilized to measure the median probability of default of the non-financial sector cumulatively for five years, both as an absolute measure of corporate risk vulnerability and a relative measure compared to other sovereigns and to the market’s assessment via the now liquid credit-default-swap market. Specifically, we measure the default probabilities of listed corporate entities in nine European countries, and the U.S.A., as of 2009 and 2010. These periods coincide with the significant rise in concern with sovereign default risk in the Euro country sphere. We conclude that our corporate health index of the private sector measured at periods prior to the explicit recognition by most credit professionals, not only gave an effective early warning indicator but provided a mostly appropriate hierarchy of relative sovereign risk. Policy officials should, we believe, nurture, not penalize, the tax revenue paying and jobs generating private sector when considering austerity measures of distressed sovereigns. ========================================================= Key Words: Sovereign Risk, Financial Crisis, Default Probability, Z-Metrics JEL classification: F34, F36 * This is an updated version of the article originally published in The Journal of Applied Corporate Finance, vol.23, No. 3, Winter, 2011. **The authors would like to thank Dan Balan and Matthew Watt of RiskMetrics Group, a subsidiary of MSCI, Inc., for computational assistance, and Brenda Kuehne of the NYU Salomon Center for her research assistance. 1 1 During the past four years, bank executives, government officials, and many others have been sharply criticized for failing to anticipate the global financial crisis. The speed and depth of the market declines shocked the public. And no one seemed more surprised than the credit rating agencies that assess the default risk of sovereign governments as well as corporate issuers operating within their borders. Although the developed world had suffered numerous recessions in the past 150 years, this most recent international crisis raised grave doubts about the ability of major banks and even sovereign governments to honor their obligations. Several large financial institutions in the U.S. and Europe required massive state assistance to remain solvent, and venerable banks like Lehman Brothers even went bankrupt. The cost to the U.S. and other sovereign governments of rescuing financial institutions believed to pose “systemic” risk was so great as to result in a dramatic increase in their own borrowings. The general public in the U.S. and Europe found these events particularly troubling because they had assumed that elected officials and regulators were well-informed about financial risks and capable of limiting serious threats to their investments, savings, and pensions. High-ranking officials, central bankers, financial regulators, ratings agencies, and senior bank executives all seemed to fail to sense the looming financial danger. This failure seemed even more puzzling because it occurred years after the widespread adoption of advanced risk management tools. Banks and portfolio managers had long been using quantitative risk management tools such as Value at Risk (“VaR”). And they should also have benefited from the additional information about credit risk made publicly available by the new market for credit default swaps (“CDS”). 2 2 But, as financial market observers have pointed out, VaR calculations are no more reliable than the assumptions underlying them. Although such assumptions tend to be informed by statistical histories, critical variables such as price volatilities and correlations are far from constant and thus difficult to capture in a model. The market prices of options—or of CDS contracts, which have options “embedded” within them—can provide useful market estimates of volatility and risk. And economists have found that CDS prices on certain kinds of debt securities increase substantially before financial crises become full-blown. But because there is so little time between the sharp increase in CDS prices and the subsequent crisis, policy makers and financial managers typically have little opportunity to change course.1 Most popular tools for assessing sovereign risk are effectively forms of “top-down” analysis. For example, in evaluating particular sovereigns, most academic and professional analysts use macroeconomic indicators such as GDP growth, national debt-to-GDP ratios, and trade and budget deficits as gauges of a country’s economic strength and well-being. But, as the recent Euro debt crisis has made clear, such “macro” approaches, while useful in some settings and circumstances, have clear limitations In this paper, we present a totally new method for assessing sovereign risk, a type of “bottom-up” approach that focuses on the financial condition and profitability of an economy’s private sector. The assumption underlying this approach is that the fundamental source of national wealth, and of the financial health of sovereigns, is the economic output and productivity of their companies. To the extent we are correct, such an approach could provide financial professionals and policy makers with a more effective means of anticipating financial 1 See, for example, Hekran Neziri’s “Can Credit Default Swaps predict Financial Crises?” in the Spring 2009 Journal of Applied Economic Sciences, Volume IV/Issue 1(7). Neziri found that CDS prices had real predictive power for equity markets, but that the lead time was generally on the order of one month. 3 3 trouble, thereby enabling them to understand the sources of problems before they become unmanageable. In the pages that follow, we introduce Z-Metrics™, as a practical and effective tool for estimating sovereign risk. Developed in collaboration with the Risk Metrics Group, now a subsidiary of MSCI, Inc., Z-Metrics is a logical extension of the Altman Z-Score technique that was introduced in 1968 and has since achieved considerable scholarly and commercial success. Of course, no method is infallible, or represents the best fit for all circumstances. But by focusing on the financial health of private enterprises in different countries, our system promises at the very least to provide a valuable complement to, or reality check on, standard “macro” approaches. But before we delve into the details of Z-Metrics, we start by briefly reviewing the record of financial crises to provide some historical perspective. Next we attempt to summarize the main findings of the extensive academic and practitioner literature on sovereign risk, particularly those studies designed to test the predictability of sovereign defaults and crises. With that as background, we then present our new Z-Metrics system for estimating the probability of default for individual (non-financial) companies and show how that system might have been used to anticipate many developments during the current EU debt crisis. In so doing, we make use of the most recent (2009 and 2010) publicly available corporate data for nine European countries, both to illustrate our model’s promise for assessing sovereign risk and to identify scope of reforms that troubled governments must consider not only to qualify for bailouts and subsidies from other countries and international bodies, but to stimulate growth in their economies. 4 4 More specifically, we examine the effectiveness of calculating the median company fiveyear probability of default of the sovereign’s non-financial corporate sector, both as an absolute measure of corporate risk vulnerability and a relative health index comparison among a number of European sovereigns, and including the U.S. as well. Our analysis shows that this health index, measured at periods prior to the explicit recognition of the crisis by market professionals, not only gave a distinct early warning of impending sovereign default in some cases, but also provided a sensible hierarchy of relative sovereign risk. We also show that, during the current European crisis, our measures not only compared favorably to standard sovereign risk measures, notably credit ratings, but performed well even when compared to the implied default rates built into market pricing indicators such as CDS spreads (while avoiding the well-known volatility of the latter). Our aim here is not to present a “beauty contest” of different methods for assessing sovereign risk in which one method emerges as the clear winner. What we are suggesting is that a novel, bottom-up approach that emphasizes the financial condition and profitability of a nation’s private sector can be effectively combined with standard analytical techniques and market pricing to better understand and predict sovereign health. And our analysis has one clear implication for policy makers: that the reforms now being contemplated should be designed, as far as possible, to preserve the efficiency and value of a nation’s private enterprises. Modern History Sovereign Crises When thinking about the most recent financial crisis, it is important to keep in mind how common sovereign debt crises have been during the last 150 years—and how frequently such debacles have afflicted developed economies as well as emerging market countries. Figure 1 5 5 shows a partial list of financial crises (identified by the first year of the crisis) that have occurred in “advanced” countries. Overall, Latin America seems to have had more recent bond and loan defaults than any other region of the world (as can be seen in Figure 2). But if we had included a number of now developed Asian countries among the “advanced” countries, the period 19971999 period would be much more prominent. FIGURE 1 Financial Crises, Advanced Countries 1870-2010 Crisis events (first year) Austria Brazil Canada Czechoslovakia China Denmark DEU GBR Greece Italy Japan Netherlands Norway Russia Spain Sweden USA 1893, 1898, 1873, 1870, 1921, 1877, 1880, 1890, 1870, 1887, 1942 1897, 1899, 1918, 1920, 1876, 1873, 1989 1902, 1906, 1910, 1939 1885, 1891, 1974, 1894, 1891, 1921, 1921, 1998 1924, 1897, 1884, 1914, 1931, 1939 1923, 1983 1931, 2008 1902, 1901, 1984, 1932, 1907, 1907, 1931, 1991, 2009 1931, 1921, 1931, 1987 2008 2007 1930, 1935, 1990 1939 1931, 1988 1931, 1978, 2008 1907, 1922, 1931, 1991 1893, 1907, 1929, 1984, 2008 Source: IMF Global Financial Stability Report (2010), Reinhart and Rogoff (2010), and various other sources, such as S&P’s economic reports. 6 6 Source: Compilation by Ingo Walter, NYU Stern School of Business The clear lesson from Figures 1 and 2 is that sovereign economic conditions appear to spiral out of control with almost predictable regularity and then require massive debt restructurings and/or bailouts accompanied by painful austerity programs. Recent examples include several Latin American countries in the 1980s, Southeast Asian nations in the late 1990s, Russia in 1998, and Argentina in 2000. In most of those cases, major problems originating in individual countries not only imposed hardships on their own people and markets, but had major financial consequences well beyond their borders. We are seeing such effects now as financial problems in Greece and other southern European countries not only affect their neighbors, but threaten the very existence of the European Union. Such financial crises have generally come as a surprise to most people, including even those specialists charged with rating the default risk of sovereigns and the enterprises operating 7 7 in these suddenly threatened nations. For example, it was not long ago that Greek debt was investment grade, and Spain was rated Aaa as recently as June 2010.2 And this pattern has been seen many times before. To cite just one more case, South Korea was viewed in 1996 as an “Asian Tiger” with a decade-long record of remarkable growth and an AA- rating. Within a year however, the country was downgraded to BB-, a “junk” rating, and the county’s government avoided default only through a $50 billion bailout by the IMF. And it was not just the rating agencies that were fooled; most of the economists at the brokerage houses also failed to see the problems looming in Korea. What Do We Know about Predicting Sovereign Defaults? There is a large and growing body of studies on the default probability of sovereigns, by practitioners as well as academics.3 A large number of studies, starting with Frank and Cline’s 1971 classic, have attempted to predict sovereign defaults or rescheduling using statistical classification and predicting methods like discriminant analysis as well as similar econometric techniques.4 And in a more recent development, some credit analysts have begun using the “contingent claim” approach5 to measure, analyze, and manage sovereign risk based on Robert Merton’s classic “structural” approach (1974). But because of its heavy reliance on market 2 On April 27, 2010, Standard & Poor’s Ratings Services lowered its long- and short-term credit ratings on the Hellenic Republic (Greece) to non-investment grade BB+; and on June 14, 2010, Moody’s downgraded Greece debt to Ba1 from A2 (4 notches), while Spain was still Aaa and Portugal was A1. Both of the latter were recently downgraded. S&P gave similar ratings. 3 One excellent primer on sovereign risk is Babbel’s (1996) study, which includes an excellent annotated bibliography by S. Bertozzi on external debt capacity that describes many of these studies. Babbel lists 69 potentially helpful explanatory factors for assessing sovereign risk, all dealing with either economic, financial, political, or social variables. Except for the political and social variables, all others are macroeconomic data and this has been the standard until the last few years. Other work worth citing include two practitioner reports—Chambers (1997) and Beers et al (2002)—and two academic studies—Smith and Walter (2003), and Frenkel, Karmann and Scholtens (2004). Full citations of all studies can be found in References section at the end of the article. 4 Including Grinols (1976), Sargen (1977), Feder and Just (1977), Feder, Just and Ross (1981), Cline (1983), Schmidt (1984), and Morgan (1986). 5 Gray, Merton and Bodie (2006, 2007) 8 8 indicators, this approach to predicting sovereign risk and credit spreads has the drawback of producing large—and potentially self-fulfilling—swings in assessed risk that are attributable solely to market volatility. A number of recent studies have sought to identify global or regional common risk factors that largely determine the level of sovereign risk in the world, or in a region such as Europe. Some studies have shown that changes in both the risk factor of individual sovereigns and in a common time-varying global factor affect the market’s repricing of sovereign risk.6 Other studies, however, suggest that sovereign credit spreads are more related to global aggregate market indexes, including U.S. stock and high-yield bond market indexes, and global capital flows than to their own local economic measures.7 Such evidence has been used to justify an approach to quantifying sovereign risk that uses the local stock market index as a proxy for the equity value of the country.8 Finally, several very recent papers focus on the importance of macro variables such as debt service relative to tax receipts and the volatility of trade deficits in explaining sovereign risk premiums and spreads.9 A number of studies have also attempted to evaluate the effectiveness of published credit ratings in predicting defaults and expected losses, wit ...
Purchase answer to see full attachment

Tutor Answer

DrFrankTUTOR
School: New York University

done

Surname 1
Running Head: ANALYZING BONDS PRICES AND PROBABILITY

ANALYZING BOND PRICES AND PROBAILITY
PROBABILITY OF DEFAULT (PD), CURRENT PRICES AND IMPLICATIONS

Student
Instructor
Institution
Course Name & Code
Submission Date

Surname 2
Running Head: ANALYZING BONDS PRICES AND PROBABILITY

ANALYZING BOND PRICES AND PROBAILITY
1) Tesla’s rating before and after issuance of $1.8 billion corporate bonds priced at 5.3% 8year notes in 2017 using the credit analytics and traditional approach
Using the traditional approach, the Tesla bonds before could be rated as B2 because they
portended high credit risk, associated with low coupon rates, and were highly speculative
to the market. It is known that the company took the action of putting corporate bonds to
leverage its performance which had fallen drastically. After the input of the $1.8billion
corporate bonds the Tesla rating upgraded to “Baa1” referring to upper medium grade
bonds that are somehow speculative and have moderate credit risk around 15% of default
(PD)
2) On addition of $3 billion Bonds to meet product...

flag Report DMCA
Review

Anonymous
I was on a very tight deadline but thanks to Studypool I was able to deliver my assignment on time.

Anonymous
The tutor was pretty knowledgeable, efficient and polite. Great service!

Anonymous
Heard about Studypool for a while and finally tried it. Glad I did caus this was really helpful.

Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4

Brown University





1271 Tutors

California Institute of Technology




2131 Tutors

Carnegie Mellon University




982 Tutors

Columbia University





1256 Tutors

Dartmouth University





2113 Tutors

Emory University





2279 Tutors

Harvard University





599 Tutors

Massachusetts Institute of Technology



2319 Tutors

New York University





1645 Tutors

Notre Dam University





1911 Tutors

Oklahoma University





2122 Tutors

Pennsylvania State University





932 Tutors

Princeton University





1211 Tutors

Stanford University





983 Tutors

University of California





1282 Tutors

Oxford University





123 Tutors

Yale University





2325 Tutors