Differences and Importance of IPPS, OPPS, MPFS and DMEPOS

Differences and Importance of IPPS, OPPS, MPFS and DMEPOS The inpatient prospective payment system (IPPS) is a structure of payment that comprises the instances of diagnosis-related groups (DRGs) as acute care hospital inpatients. It is founded on resources that are employed to take care of recipients of Medicare in those groups. Each one DRG has a weight of payment allocated to it, founded on the standard cost of treating patients in that DRG. IPPS participates a significant function in deciding all costs of hospital as well as the costs of all tools for treating the patient all through a precise stay of inpatient (CMS. Gov, 2012). The outpatient prospective payment system (OPPS) on the other side is controlled for service groups of diverse outpatient as classifications of ambulatory payment (APCs). Outpatient services in every APC are alike in expressions of clinical aspects and entailed resources. The APC payment rate In addition, for every group is wage adjusted to rationalize differences of geographic and functional in the group to all services. Hospitals In this get a fixed sum for all services of outpatient founded on classifications of ambulatory payment. Medicare apart from this, employs it to repay physicians and additional health care providers for the items and services that are not division of prospective payment systems (Herbert, 2012). A Medicare physician fee schedule (MPFS) establishes the rates of payment for therapy and physician services that are founded on conversion factors, relative value units, and cost indices of geographic practice. Durable medical equipment, prosthetics, orthotics and supplies (DMEPOS) is recounted to reimbursement rates for these specific things to suppliers that make certain admission of a high-class of these things to the patients. It includes more than a small number of regulations of payment managing the delivery of DMEPOS things for beneficiaries of Medicare. It renders the process of competitive and authorization bidding, supplier enrollment, that have an force on suppliers payment made by the hospitals. It advances the capability of physicians to offer these things to their patients in an suitable manner. It make sure efficient supply of the required resources like health techniques, equipments, and technologies to the deprived at the right cost. There is most important divergence of recipients, provider groups, and their services offered for medical beneficiaries in these models, (CMS. Gov, 2012). OPPS and IPPS are executed for the similar provider i.e. health organizations and hospitals, nevertheless different in their recipients, who are out patients and inpatients correspondingly. DMEPOS and MPFS don’t comprise prospective payment systems and focus on supplier and physicians groups correspondingly. All these methods are structured to restrain on raise in health care services cost to the patients. It aids for the beneficiaries of medical to get quality and effective health care services at low down cost (Green Rowell, 2012). Hospitals With this are also confined to get a precise amount for their services, which they offer to the patients. Payment Expectations Both inpatient and outpatient prospective payment system methods of reimbursement are employed by Medicare to reimburse hospitals for outpatient and inpatient services, in addition to rehabilitation hospitals, skilled nursing facilities, and home health services. It is anticipated from both providers that they ought to provide outpatient and inpatient services to the patients efficiently. It is as well anticipated that these hospitals for all time emphasize improving effectiveness and efficiency of care, while generating a results-oriented, patient-focused, market-driven environment (Zweifel, Breyer Kifmann, 2009). It is supposed to be noted down In this context, that in the instance that someone is not capable to recompense for hospitahealth services it is anticipated from the hospital that it offer the free of cost health services. It in addition have to serve a least amount number of beneficiaries of Medicare. Non-physician and physicians practitioners Under the MPFS, are remunerated that offer fundamental health services to beneficiaries of Medicare. For this group Payment expectation is to advance the quality of care for patients while eradicating barriers to thriving participation of physician. They ought to follow Medicare laws with this, consecutively to accomplish the medical beneficiaries expectation. It is essential for them In addition, to offer facilities of Medicare to the patients at decided prices with no any conflicts. It is as well presumed to non-physician and physicians practitioners that they construct of the majority of their knowledge and skills consecutively to offer patients health treatment (CMS. Gov, 2012). All hospital and physicians practitioners acquire a fixed sum for every patient and are accountable for making accessible all services for that patient above a assigned period. DMEPOS is employed for paying back suppliers of prosthetics, durable medical equipment, orthotics and supplies to the patients. Value based purchasing of health care services are Payment expectations for this provider that can offer additional transparency on quality and cost to make certain Medicare beneficiaries optimal care. Providers In addition, have to be additional spotlight to supply to CMS performance data, which is probable to have an effect on potential reimbursements to provider. There are financial penalties for those providers In condition of any infringement of CMS’ standards,who don’t meet up these standards (Mayes Berenson, 2006). It is as well anticipated from suppliers that they offer efficient supplies to the hospitals in considers ensuring the eminence of the patients health. Implication of a Case Mix Involving IPPS, OPPS and DMEPOS for A Small Hospital Implication of a case mix In a small hospital, concerning OPPS IPPS,and DMEPOS is to develop the hospital care quality and center on designing effectual improvement facilities of quality. Hospitals are a most important constituent of the delivery system of health care, which are required to implement and develop an important outcome on quality, costs and admission to care. Small hospitals can attain their payments in a appropriate way in the course of executing these methods. They might be capable to get diverse equipments and required resources at rational price all the way through suppliers (Chalfin Rizzo, 2011). It can facilitate them to offer healthcare services based on quality to the patients at a lower cost. They can obtain an appropriate amount for offering healthcare services to the outpatients and inpatients. It facilitates them to classify their services according the health regulations in an effectual way. It as well offers them equivalent opportunity to get growth since of security for payment of their services as indicated by fixed standards and sets. Hospitals of Small specialty and centers in concern of this, are obtaining the latest technology and equipment consecutively to draw high-end customers from commercial hospitals. DMEPOS can aid them to obtain these services with easiness at low down cost. These hospitals Apart from this, are proficient to administer their cash flow competently regarding their inventories and services. A fixed and proper amount of payment to the small health care providers employees can stimulate them to offer quality services to the beneficiaries of medical effectively (CMS. Gov, 2012). Small hospitals can acquire bonus payments for offering health professional shortage care. Consequently, a small hospital can associate these payment methods suitably in its operations. There possibly will be likelihood of risk to get lesser amount on the other hand, for their services since of the nature of illness of patients, high treatment cost involvement, or additional situational factors. It is since the fee is charged for the anticipated expenditure of caring for the patient. If the on the whole cost of care is additional than anticipated, the profit the hospital and doctor receive can be decreased. It can force growth of hospital in unconstructive manner. It relies on the equipped efficiency of the hospital that they can acquire additional profits by offering care at a lower-than-anticipated cost. Furthermore, there possibly will be a likelihood of less increase in standard payments for services of small hospital in novel reforms of these models of payment (Wachter, Goldman Hollander, 2005).

Economic Performance And Current Situation Overview Economics Essay

The Hong Kong ‘s economic system is presently in an upward tendency. It is said to be the universe ‘s freest economic system, the 2nd largest beginning of foreign direct investing ( FDI ) in Asia, the universe ‘s 9th largest foreign exchange militias keeping, the universe ‘s 2nd highest per capita retention of foreign exchange militias and the universe ‘s more services-oriented economic system whereby the service sector accounts for more than 90 % of GDP. Hong Kong has successfully overcome the strict challenges from the planetary recession of 2009. It has been able to raise its existent Gross Domestic Product ( GDP ) from -2.7 % in 2009 to +6.8 % in 2010, therefore overcoming the economic recession of 2009. Hong Kong ‘s economic growing was achieved because of the robust rise in entire ware exports and service exports every bit good as the autumn in unemployment rate. However, the rising prices rate which is measured by the composite Consumer Pric e Index besides rise from 0.9 % in 2009 to 2.9 % in 2010, connoting that on norm the monetary values of all goods and services rose by 2.9 cents in every US $ over the twelvemonth. It is of import to observe that rising prices rate in Hong Kong, which refers to a general and sustained rise in the degree of monetary values of goods and services was chiefly due to imported rising prices, that is, addition in the monetary values of imported goods from overseas besides boosts up the monetary values of goods and services locally. The figure below shows the economic tendency of Hong Kong since 2000. From the chart, it can be clearly seen that Hong Kong ‘s existent GDP has fell by 3.7 % ( 10.5 % in 2000 – 6.8 % in 2010 ) over the last 10 old ages. At the same clip, it can besides be observed that Hong Kong experienced a major diminution from 10.5 % in 2000 to 0.1 % in 2001 because of the deterioration of the external environment prompted by the downswing in the US economic system, and with the state of affairs aggravated by the tragic event in the United States on 11th September 2001 when two air hoses crashed into the Twin Towers of World Trade Center in New York City. Furthermore, with the recent economic growing in 2010, citizens of Hong Kong are better off as the existent GDP per capita, that is, income per caput besides increases over the last three old ages from 2008 to 2010. Besides, it is every bit of import to advert that Hong Kong has four economic pillars: Trading and Logistics ( 24.1 % of GDP in footings of value-added in 2009 ) , Tourism ( 3.3 % ) , Financial Services ( 15.2 % ) , and Professional Services and other manufacturer services ( 13.1 % ) . On the other manus, there are six industries in which Hong Kong has clear advantages for farther development and which histories for 8 % of GDP in footings of value-added in 2009. The six industries comprises of Cultural and Creative Industry, Medical Services, Education Services, Innovation and Technology, Testing and Certification Services, and Environmental Services. Now, allow us analyze in deepness the different elements structuring the economic system of Hong Kong. The Domestic Sector The Domestic Sector contributed a batch on the economic growing of Hong Kong with the retail gross revenues holding a important recoil, reflecting a return of consumer assurance and strong influxs of tourers. The value of retail gross revenues increased by 18.3 % in 2010 compared to a 0.6 % rise in 2009. Furthermore, the touristry sector besides experienced a sustain growing with visitant reachings making 36.3 million, a 21.8 % addition in 2010 compared to 2009 with visitant reachings stand foring 29.6 million, a 0.3 % addition. The chart below shows how retail gross revenues and tourers reachings have fared strongly through to the twelvemonth terminal on a year-on-year footing since 2005 to 2010. Beginning: hypertext transfer protocol: //www.hketosydney.gov.hk/cust/HK_Feb_2011.pdf The External Sector Hong Kong ‘s external sector besides continued to do strongly in 2010 thanks to the vigorous economic public presentation of Asiatic economic systems, peculiarly the Mainland. The Mainland and other Asiatic markets, which accounts for around 70 % of Hong Kong ‘s entire exports of goods, remained the cardinal growing driver, registering a important rise of around 20 % in existent footings in 2010 compared to a autumn of 6.6 % in existent footings in 2009. It is of import to observe that the Asiatic markets particularly the Mainland continued to surpass the US and EU markets, mirroring the divergent form of planetary economic recovery across parts due to the addition in the petroleum oil monetary values in January 2011 to US $ 93 per barrel and besides due to the political agitation in Egypt. The diagram below illustrates how the Asiatic markets have surpassed the US and EU markets over the last five old ages. Two-Speed Growth Continued Beginning: hypertext transfer protocol: //www.hketosydney.gov.hk/cust/HK_Feb_2011.pdf Now allow us see the two different types of trade: seeable and unseeable. Visible Trade Visible Trade refers to the imports and exports of goods, such as oil, machines, nutrient, chemicals and so on. The major states to which Hong Kong exports its goods are Mainland of China, United States, European Union, Japan, Republic of Korea, Taiwan and Singapore. Though after the planetary recession in 2009, Hong Kong ‘s exports to these states have improved well like exports to Mainland in existent footings rose from -6.6 % in 2009 to 20 % in 2010 ; yet the European Union still lagged behind with merely a 6.6 % addition in existent footings in seeable exports in 2010 compared to the other major markets who have seen a double-digit addition in exports in the current twelvemonth. Similarly, the imports of goods in existent footings rose from -9.4 % in 2009 to 18.6 % in 2010. The maintained imports for most of the merchandises like consumer goods, groceries, natural stuffs and capital goods of Hong Kong climbed significantly in 2010 compared to 2009 whilst fuels retained impo rts fell by 9.3 % ( 23.2 % in 2009 – 13.9 % in 2010 ) . This was chiefly due to the recent rise in the petroleum oil monetary values. The figure below shows how seeable imports and exports have fared since 2005. It can be seen that both imports and exports of goods have increased since 2005 and have been able to excel the planetary recession in 2009. Invisible Trade Invisible Trade refers to the exchange of services, that is, imports and exports of services like fiscal and concern services, travel services, trade-related services and transit services. Exports of services sustained a strong growing throughout 2010, jumping by 15.0 % in existent footings for the twelvemonth as a whole, following the 0.3 % growing in 2009. Among the major service constituents of Hong Kong, the exports of travel services showed the strongest public presentation thanks to the ample inflow of visitants from te regional every bit good as long-haul markets. Likewise, exports of trade-related services besides grew aggressively in 2010 benefiting from the improved trading environment in Asia. On the other manus, imports of services experienced a rise up to 10.9 % in existent footings in 2010, in contrast to the 4.9 % contraction in 2009. The imports of services grew solidly in line with improvong the economic conditions. Hong Kong Trade Balance Although Hong Kong is sing important addition in its exports and imports of both good and services, the seeable trade shortage in 2010 has widened compared to 2009. But this seeable trade shortage was overcomed by the high unseeable trade excess ; therefore assisting the economic system of Hong Kong to give a trade balance excess of $ 104.6 billion equivalent to 2.8 % of entire value of imports of goods and services in 2010 compared to merchandise excess of $ 121.3 billion stand foring 4 % of the entire value of imports of goods and services in 2009. The figure below depicts Hong Kong trade public presentation over the last five old ages. It can clearly be seen that Hong Kong has been sing seeable trade shortage since 2005 its imports exceeded its exports. However, due to its high invible trade excess over the last five old ages, Hong Kong continues to hold a favorable trade balance which underlines the state ‘s strong external fight. The Financial Sector The heavy market concerns over lifting financial shortages and public debts in a few European economic systems have made the stock market more volatile recently. As a consequence, the Hong Kong dollar topographic point exchange rate moved withing a narrow scope of 7.749 to 7.805 per US dollar in 2010. Despite a brief weakening around the center of the twelvemonth amid possible capital escapes induced by heightened concerns about the European debt crisis, the Hong Kong spot exchange rate showed renewed strength thenceforth on the dorsum of strong demand associated with the vivacious Initial Public Offerings ( IPOs ) activities. Furthermore, under the Linked Exchange Rate system, motions in the Hong Kong dollar exchange rates against other currencies closely follow those of the US dollar. In 2010, the US dollar strengthened against the Euro and the British Pound amid concerns about the European debt job but weakened further against most other currencies, particularly the Australian dol lar and the Nipponese Yen. Consequently, in December 2010 the trade-weighted Hong Kong dollar Nominal and Real Effective Exchange Rate Indexs declined by 2.2 % and 2.1 % severally from 2009. Hence, Hong Kong dollar weakened against most major currencies as shown in the figure below for the twelvemonth 2010. Hong Kong dollar weakened slightly recently in 2010/11 In add-on, Hong Kong is a extremely attractive market for foreign direct investing. Harmonizing to the UNCTAD World Investment Report 2010, Hong Kong was the universe ‘s 4th largest FDI receiver, pulling US $ 48 billion in 2009. This marks the first clip that Hong Kong has gained 4th topographic point in the planetary rankings and represents a leap from its 9th place in 2008. For the 12th back-to-back twelvemonth, Hong Kong continues to be the 2nd largest FDI receiver in Asia, after the Chinese mainland. On the other manus, Hong Kong was the 2nd largest beginning of FDI in Asia, draging Japan, with FDI escapes amounting to US $ 52 billion in 2009. Further, imparting to all major economic sectors grew at a alert gait as Hong Kong has a low involvement rate. In other footings, involvement rates on both sweeping and retail foreparts continued to remain at historically low degrees in 2010. The Labour Sector Labour market conditions improved further on a wide forepart as a consequence of the strong choice up of economic activities and substancial occupation creative activity. Entire employment rose to an all-time high by end-2010, forcing the seasonally adjusted unemployment rate down farther to 4.0 % in the 4th one-fourth of 2010. Underemployment rate likewise dropped to 1.8 % . Labour income continued besides to lift. It is of import to observe that the new occupations created were non merely adequate for absorbing the bing umemployed individuals but besides the new entrants joing the labour force. The line chart below shows the tendency in unemployment rates since 2006. Unemployment rate declined for most in 2010, led by the important upturn in labour demand Beginning: hypertext transfer protocol: //www.hkeconomy.gov.hk/en/pdf/er_10q4.pdf Monetary values Inflation force per unit areas in Hong Kong went up bit by bit over the class of 2010 chiefly due to higher imported rising prices. Monetary values of nutrient and other trade goods rose strongly in the international markets in 2010 along with the continued planetary economic recovery and the really accommodating pecuniary environment worldwide. For 2010 as a whole, the Composite Consumer Price Index rose by 2.4 % following the 0.5 % addition in 2009 when the economic system was in deflation for several months in the twelvemonth. As mentioned antecedently, rising prices was besides caused by imported rising prices. In other footings, import monetary values augmented notably in 2010, due to the strong recoils in planetary trade good monetary values, higher rising prices in supply beginnings and to a lesser extent the somewhat weaker Hong Kong dollar alongside the US dollar ; accordingly, these increase the imported rising prices in Hong Kong. Inflation in Hong Kong Beginning: hypertext transfer protocol: //www.hketosydney.gov.hk/cust/HK_Feb_2011.pdf Key: CCPI stands for Composite Consumer Price Index ( * ) The underlying CCPI has netted out the effects of all relevant one-off steps introduced since 2007, including the release and Government ‘s payment of public lodging leases, rates concession, suspension of Employee Retraining Levy, and subsidies for family electricity charges. However, it is critical to indicate out that Hong Kong was non the alone state to be sing lifting rising prices on 2010. Many Asiatic economic systems with vivacious growing in activities besides saw higher inflationary force per unit areas throughout the twelvemonth.

The Correlation in Credit Risk in the market - Free Essay Example

Sample details Pages: 12 Words: 3729 Downloads: 9 Date added: 2017/06/26 Category Business Essay Type Research paper Did you like this example? Correlation in credit risk is a well-known phenomenon. Understanding the causes of correlated credit losses is crucial for many purposes, such as managing a portfolio, setting capital requirements for banks, and pricing structured credit products that are heavily exposed to correlations in credit risk; for example, collateralized debt obligations (CDO). This issue has become particularly important because of the rapid growth of structured credit products in the financial markets in recent years. Don’t waste time! Our writers will create an original "The Correlation in Credit Risk in the market" essay for you Create order But despite much research on the subject, we do not understand many aspects of correlation in credit risk; this paper attempts to move the literature forward. First, we explore the economic importance of contagion in credit risk correlation. This is an open empirical question. Many credit models are based on the doubly stochastic assumption that, conditional on observable risk factors, defaults are independent of each other. This assumption is widely accepted and implemented in banking to determine capital requirements.Evidence exists that contagion has a notable impact on the correlation in credit risk of firms subject to significant credit events. On the basis of these findings, some researchers have tried to include contagion in credit models. However, the economic importance of contagion in a firms credit risk correlation is not clear from the literature. If the role of contagion is statistically significant but not economically significant, modeling contagion may not be of first-order importance. But even though some researchers and practitioners reject the doubly stochastic assumption, they find that the proportion of correlation in credit risk that cannot be explained by observable risk factors is small (1 to 5 percent), which suggests that unobservable risk factors may be of minor importance in credit risk models. In this paper, we attempt to clarify this issue. We also explore the credit risk correlation pattern over time and across firms with varying credit quality. The academic literature cannot agree on these patterns either. These questions are important because credit risk has been and still is the biggest risk facing banks. And with securitization and the new products that have been developed in the financial market, credit risk has been spread out beyond the banking sector to various market segments. Ambiguity regarding these issues poses serious challenges for investors, practitioners, and regulators. In this paper, we approach cred it risk in two ways. First, unlike earlier studies, we use data from the credit default swap (CDS) market. Most researchers examine the correlation in a firms credit risk using either estimated default intensity based on actual default observations or implied default probability derived from the Merton (1974) model. The former approach may not be reliable, because some default events are strategic decisions and, therefore, may not correspond to economic default.1 Also, some financially distressed companies may be able to negotiate debt restructuring to avoid default or may be acquired with bankruptcy looming on the horizon, and these informal resolutions of financial distress are difficult to identify.2, 3 The problem of reliable numbers is a serious challenge-default is a low-frequency event, and any misclassification may have a major impact on the precision of parameter estimates. Thus, the estimated default intensity might be contaminated, and this weakness could be behind some r ather surprising findings in the literature. On the other hand, default probability estimated from the Merton model could be confounded by the oversimplified assumptions behind the model. In contrast, the CDS market enables the direct measurement of credit risk by many market participants. CDS is insurance against a default by a particular company or sovereign entity (known as the reference entity). The buyer of the CDS contract makes periodic payments to the seller for the right to sell a bond issued by the reference entity for its face value if the issuer defaults. So the price of CDS contracts (or the CDS spread) is a direct measure of the credit risk of the reference entity. Because CDS spreads can be based on a wide array of credit risk models, it is also a comprehensive measure of credit risk. The second way we approach credit risk in this paper is by investigating the observable factors and their contributions to the correlation in risk. Although previous studies have i ncorporated some macroeconomic factors into modeling credit risk, the impact of these variables is not consistent across studies, and some results are counterintuitive. We study the impact on credit risk of various macroeconomic variables as well as firm- and market-level variables, and we model the industry effect on the credit risk of individual firms. Although many researchers have suggested that the industry effect partially accounts for the correlation in credit risk, the literature has yet to provide conclusive evidence. On the basis of monthly changes in CDS spreads from January 2001 through December 2006, we find that changes in CDS spreads are positively correlated, with an average correlation of 21 percent. Observable variables at the firm level can reduce the correlation by 8 percent, resulting in a correlation of 13 percent among the regression residuals. Market-level and macroeconomic variables are significantly associated with changes in CDS spreads, with the expect ed signs of the regression coefficients. These variables, together with firm-level variables, can reduce the correlation by two-thirds to 7 percent. We also confirm the existence of the industry effect and find that firms in less cyclical industries have lower correlations in credit risk. Although industry variables are significantly related to CDS spread changes in the right directions, the industry effect can be responsible for less than 1 percent of the correlation in CDS spread changes after we control for firm-level, market-level, and macroeconomic variables. When all observable variables are combined, they can account for about 14 percent of the correlations, leaving 7 percent unaccounted for. The main observable variables that contribute to the correlations are firm-level variables and credit spreads, which can be affected by both contagion and systematic risks. Excluding these variables, the mean correlation among the residuals is 12 percent. These findings suggest that c ontagion could contribute from 33 percent to 57 percent of the correlation in credit risks. We also investigate the potential nonlinearity in the relationship between credit risk and observable variables, and find that accounting for nonlinearity does not qualitatively change our findings. Thus, the evidence suggests that contagion does play an economically important role in the credit risk correlation. In addition, we find that the correlation in credit risk is countercyclical; that is, it is higher during economic downturns and lower during booms. Also, it is higher among firms with low credit ratings than among those with high credit ratings. These findings are consistent with some theoretical predictions but not with the findings based on measures from the Merton model. We believe that the results derived from CDS spreads are more reliable because of the oversimplified assumptions behind Mertons model and the evidence in the literature that the Merton default probability m easure does not forecast default probability well. Since the study period was short, it included one full business cycle; thus, the results have general implications. The study period did not include the recent market turmoil; however, if contagion is a major phenomenon during severe economic downturns, failing to include the recent period of turmoil is biased only against the finding that contagion plays an important role. The evidence, therefore, suggests that modeling the unobservable risk factors should be of first-order importance for future research in credit modeling. This paper is organized as follows. In section II, there is a review of the current literature. In section III, description of the sample is given. Discussion of observable risk factors and their contributions to the correlation in credit risk is given in section IV. Section V presents results on the correlation in credit risk over time and by rating groups. In the last section, a brief conclusion is given . II. Literature Review Modelling Correlation in Credit Risk The two branches of credit risk measurement are (1) the structural approach and (2) the reduced-form approach. Structural models originate from the Merton (1974) model and assume that a company will default if the value of its assets is below a certain level; for example, the amount of its outstanding debt. The key to structural modelling is to capture the stochastic asset diffusion process, and default correlation between two companies is introduced by assuming that the stochastic processes followed by the assets of the two companies are correlated. Correlation in the stochastic asset diffusion processes of two firms can be caused by both observable risk factors and unobservable risk factors, such as contagion. The advantage of structural models is the flexibility in modeling correlation in credit risk; the disadvantage is the difficulty in implementing them empirically. The general theoretical predictions from this school are that credit ri sk correlation is higher for firms with a low credit rating than for those with a high credit rating, and that the correlation increases during economic downturns The reduced-form models assume that a firms default time is driven by a default intensity that varies according to changes in macroeconomic conditions In other words, when the default intensity for company A is high, the default intensity for company B tends to be high as well, which induces a default correlation between the two companies. The reduced-form models usually assume that observable risk factors are the main drivers of firm credit risk and that, after controlling for observable factors and default intensity, defaults should be independent. This is the doubly stochastic assumption. Because of its mathematical tractability, most researchers and practitioners gravitate toward this approach; thus, the doubly stochastic assumption is behind many commonly used reduced-form models to predict default, such as the dur ation models and the survival time copula models. The doubly stochastic assumption is also the key assumption behind the proprietary models. For instance, Moodys KMV Risk Advisor considers systematic factors using a three-level approach: (1) a composite market risk factor, (2) an industry and country risk factor, and (3) regional factors and sector indicators. The factor loading for an individual firm for each of the factors is estimated using asset variances obtained from the option theoretical model, and the factor loadings are then used to calculate co-variances for each pair of firms. In Credit Metrics, the credit transition matrix is conditioned on a credit cycle index, which shifts down when economic conditions deteriorate. The credit cycle index is obtained by regressing default rates for speculative grade bonds on the credit spread, 10-year Treasury yield, inflation rate, and growth in gross domestic product (GDP). In contrast, Credit Risk Plus incorporates cyclical facto rs by allowing the mean default rate to vary over the business cycle. Credit Risk Plus models find that correlation in credit risk is higher among firms with low credit ratings. In summary, the doubly stochastic assumption plays a critical role in the vast majority of credit models used in research and practice. The findings say that variations in the observable factors cannot fully explain the correlation in credit risk and that the doubly stochastic assumption is violated; however, the proportion of the correlation that cannot be explained by observable factors is rather small. The conclusion may be contaminated in two ways. First, the evidence could result from the misspecification associated with the model to predict default intensity. A different model could lead to two possibilities: (1) observable factors may be sufficient to account for the correlated default risk, or (2) the proportion not explained by observable factors could be much larger. It is not clear from the literature how the correlation in credit risk varies over business cycles and across firms with different credit quality, as studies on these subjects have yielded conflicting results. This lack of clarity poses a major challenge for investors, portfolio managers, bankers, and bank regulators. Macroeconomic Impact in Credit Risk Modelling Some studies incorporate macroeconomic conditions into credit risk models; however, researchers have used different macroeconomic variables, and some variables that are important in one paper are found to be unimportant in another. Also, some empirical results are quite counterintuitive. Some researchers find intuitive relations between credit risk and macroeconomic variables. For example, Collin-Dufresne, Goldstein, and Martin (2001) examine determinants of changes in credit spreads using changes in 10-year Treasury rates, changes in the slope of the yield curve, changes in market volatility, and monthly SP 500 returns. They find that all these variables are significantly related to changes in credit spreads, with the direction implied by structural models. Carling and colleagues (2007) investigate how macroeconomic conditions affect business defaults using a corporate portfolio from a leading Swiss retail bank. They find that the output gap, the yield curve, and consumers expectations of future economic development can help explain a firms default risk. In summary, the impact of macroeconomic variables is not consistently documented in the literature, and some results are counterintuitive. These findings add to the puzzle of whether observable risk factors can explain the correlation in credit risk. We believe that the inconsistent and sometimes counterintuitive findings may be contaminated by the noise in the default data, as default events are rare and can contain misclassifications that lead to estimation errors. CDS data are more suitable for this purpose. III. Data Description and Sample Statistics The Sample The primary data in this study are the monthly CDS data from January 2001 through December 2006. We use the five-year CDS, as this instrument is the most liquid in the CDS market. We use monthly data to match the monthly macroeconomic variables because price movements in monthly data are less contaminated than daily or weekly data by temporary imbalances between supply and demand. The CDS spread measures total credit risk, which includes both default probability (DP) and losses given default (LGD). It is widely documented that DP and LGD are positively correlated thus, the CDS spread is a comprehensive measure of total credit risk. The sample includes 523 firms (25,113 firm-month observations)-376 investment-grade firms and 147 speculative-grade firms, based on the average rating for each firm during the sample period. Our sample period (2001-2006) includes one full business cycle consisting of varying economic conditions: an economic downturn in the early period, a recovery in 2003, and a normal period afterward. Variables at the Firm, Industry, and Market Levels We use three firm-level variables to explain the changes in CDS spreads: monthly stock returns, monthly stock volatility change, and firm leverage change.According to the structural model, a firms default risk is higher when either volatility or leverage is high. Also, stock returns indicate the markets assessment of a firms future performance. Lower returns imply a dimmer outlook, which should correlate with a higher credit risk, so stock returns should be negatively associated with changes in CDS spreads. We use the following market-level variables: changes in implied market volatility (VIX), changes in market leverage, and changes in market returns (measured by NYSE-AMEX-NASDAQ value-weighted returns). An increase in either market volatility or market leverage, or a decrease in market returns, suggests a worsening economic outlook, which should be associated with an incr ease in credit risk. We define industry variables similarly-changes in industry volatility, changes in industry leverage, and changes in industry aggregate returns-and the same logic should hold at the industry level if there is an industry effect. Macroeconomic Variables We use real GDP growth rate and changes in capacity utilization rate to describe the business cycle. If credit risks are higher during an economic recession, we would see changes in CDS spreads negatively related to both real GDP growth rate and changes in capacity utilization rate. We also include inflation among our list of macroeconomic variables. Since previous studies have shown a negative relationship between real activity and inflation, we expected a positive relationship between inflation and credit risk. We use the following interest rate variables: changes in three-month T-bill rates, changes in term spreads (difference between the yields of 10-year T-bonds and three-month T-bills), and changes i n credit spreads between BBB and AAA bonds and between AAA bonds and 10-year T-bonds. The relationship between the three-month T-bill rate and credit risk should be negative for two reasons. First, the Feds monetary policy is pro-cyclical. Second, a higher interest rate can increase the risk-neutral drift of the process of firm value, thus reducing credit risks Collin-Dufresne and colleagues (2001) and Duffee (1998) both documented a negative relationship between interest rate and credit risk. Credit risk should also be negatively related to the term spread (Estrella and Hardouvelis 1991, Estrella and Mishkin 1996, and Fama and French 1989) and positively related to both measures of credit spread (Chen 1991, Fama and French 1989, Friedman and Kuttner 1992, and Stock and Watson 1989). Data Description Table 1 provides summary statistics of the sample. For all firms, the mean CDS spread is 126.27 basis points (bps). The median and standard deviation suggest that the distribution of CDS spreads is quite skewed and volatile. The mean change in CDS spreads is small (-0.07 percent), but the range is wide (-17.78 to 23.43 percent). Both the high and low in CDS spread changes are found among the speculative-grade firms; these firms also have higher mean changes in CDS spreads. As expected, all three measures (CDS spreads, equity volatility, and firm leverage) are lower among investment-grade firms and higher among speculative-grade firms. Panel B of table 1 shows that the average CDS spread was highest in 2002; it declined sharply in 2003 and 2004, then leveled off.11 The average monthly return on the NYSE-AMEX-NASDAQ index was 0.47 percent during the sample period, and the average annualized volatility was 19.08 percent. Over the entire sample period, the mean market leverage was 0.23. The average return across the industry portfolios was 0.57 percent, and the mean annualized industry volatility was 25.27 percent. Table 1. Descriptive Statistics Table 1 shows the summary statistics of the variables used in the study. Panel A presents the descriptive statistics for the firm-level variables: five-year CDS spreads (in basis points), CDS spread percentage changes, equity returns, equity volatility, and leverage. The monthly equity volatility is computed as the annualized standard deviation based on daily returns. The firm leverage is computed as the ratio of book debt value to the sum of market capitalization and book debt value. The data are from January 2001 through December 2006. Investment-grade refers to firms with ratings at BAA or above; speculative-grade refers to firms with ratings below BAA. Panel B presents the descriptive statistics of CDS spreads by year. Panel C presents the summary statistics of the market and industry variables. VIX is the implied volatility of the SP 500 index options obtained from the Chicago Board Options Exchange. The market return is the NYSE-AMEX-NASDAQ value-weighted index returns. Other market (industry) variables are the value-weighted average from all firms in the market (industry). We use the Fama-French 12-industry classification. Panel A. Firm Characteristics Variables Mean Median Minimum Maximum All firms CDS (bps) 126.27 63.10 8.65 1,632.36 CDS change (%) -0.07 -0.46 -17.78 23.43 Equity return (%) 1.23 1.13 -4.26 4.86 Equity volatility 0.31 0.28 0.13 0.78 Leverage 0.32 0.29 0.00 0.94 Investment-grade CDS (bps) 60.22 47.10 8.65 444.89 CDS change (%) -0.42 -0.60 -5.06 7.93 Equity return (%) 1.18 1.13 -0.80 4.39 Equity volatility 0.27 0.25 0.16 0.64 Leverage 0.28 0.24 0.00 0.94 Speculative-grade CDS (bps) 295.23 223.24 53.81 1,632.36 CDS change (%) 8.26 5.78 -17.78 23.43 Equity return (%) 1.34 1.34 -4.26 4.86 Equity volatility 0.41 0.39 0.13 0.78 Leverage 0.44 0.43 0.06 0.92 Table 1. Descriptive Statistics (contd.) Panel B. Summary Statistics of CDS Spreads (bps) Year Mean Median Minimum Maximum 2001 151.67 83.33 17.83 3,249.57 2002 212.29 99.70 15.22 3,232.04 2003 150.72 69.62 9.84 2,508.39 2004 109.33 49.27 8.72 1,843.10 2005 107.17 44.90 5.21 2,181.16 2006 94.39 41.40 3.98 2,396.08 Panel C. Market- and Industry-Level Variables Variables Mean Median Minimum Maximum Market aggregate return (%) 0.47 1.11 -10.01 8.41 VIX (%) 19.08 16.69 10.91 39.69 Market leverage 0.23 0.23 0.19 0.27 Industry return (%) 0.57 1.57 -12.64 10.23 Industry volatility (%) 25.27 20.21 11.91 80.57 Industry leverage 0.23 0.17 0.07 0.48 IV. Observable Risk Factors and Correlation in Credit Risk Because most of our analyses involve panel data, our estimates are based on robust standard errors. We estimated these errors by assuming independence across firms, but we accounted for possible autocorrelation within the same firm. We use the contemporaneous variables on the right-hand-side variables . Market and Macroeconomic Effect Table 2 shows the effect of firm-level variables on changes in CDS spreads. We calculate the pairwise correlations (of the raw CDS spread changes or residuals from the regressions) and report the means in the last row of the table. The first column of table 2 shows that, without controlling for any observable covariates, the average correlation in changes in CDS spreads in the entire sample is 21 percent. The correlation ranges from a minimum of -30 percent to a maximum of 72 percent, and the interquartile spans a range of 30 percent. Table 2. Effect of Firm Characteristics on the Correlation in Changes in CDS Spreads Independent Variables Model 1 Model 2 Model 3 Model 4 Model 5 Equity returns -0.567*** -0.473*** [0.023] [0.025] Change in firm leverage 1.662*** 0.318*** [0.114] [0.084] Chance in equity volatility 0.199*** 0.148*** [0.015] [0.012] Constant 0.003*** -0.002*** -0.003*** 0.003*** [0.001] [0.001] [0.001] [0.001] Observations 25,113 25,113 25,113 25,113 25,113 R2 9% 5% 3% 11% Correlation/residual correlation 0.21 0.17 0.14 0.16 0.13 Industry Effect Table 5 shows the average pairwise correlation in CDS spread changes among firms in each of the 11 Fama-French industries.12 The table shows much variation in correlation in credit risk among firms in the same industry. Over the study period, the energy sector has the highest correlation among all industries, whereas the health care sector has the lowest correlation. Only four of the 11 industries h ave a higher average correlation than the overall average of 21 percent. The ranking of correlation by industry changed over the six-year study period. The financial industry had the highest correlation in 2001 and 2002, suggesting that an economic downturn affects financial firms more than others. The energy industry had the highest correlation from 2004 to 2006, likely driven by volatile price movements in oil. The health care, medical equipment, and drug industries had the lowest correlations in three of the six years, and consumer nondurable goods had the lowest correlation in two years. These findings suggest that less cyclical industries have lower correlations in credit risk. Table 5. Correlation in CDS Spread Changes Across Industries Year Ind1 Ind2 Ind3 Ind4 Ind5 Ind6 Ind7 Ind8 Ind9 Ind10 Ind11 2001 0.12 0.44 0.44 0.63 0.24 0.36 0.51 0.28 0.41 0.65 2002 0.13 0.43 0.26 0.26 0.14 0.41 0.43 0.38 0.24 0.17 0.45 2003 0.20 0.33 0.15 0.24 0.05 0.13 0.25 0.36 0.17 0.03 0.29 2004 0.24 0.26 0.21 0.35 0.17 0.21 0.26 0.32 0.23 0.14 0.30 2005 0.22 0.28 0.23 0.55 0.18 0.22 0.22 0.35 0.20 0.23 0.31 2006 0.06 0.07 0.09 0.33 0.17 0.11 0.12 0.26 0.22 0.06 0.13 2001-2006 0.16 0.28 0.18 0.35 0.18 0.17 0.16 0.29 0.19 0.11 0.22 V. Conclusions In this paper, we examine the correlation in credit risk using CDS data. We find that observable variables at the firm, industry, and market levels, as well as macroeconomic variables, cannot fully explain the correlation in credit risk, leaving at least one-third of the correlation in credit risk unaccounted for during the study period (2001-2006). These findings suggest that contagion may be a common phenomenon in an economy and that the doubly stochastic assumption may not hold in general. Because of the large proportion of correlation that cannot be explained by observable risk factors, future research in credit modeling should focus on incorporating unobservable risk factors into models. We also find that credit risk correlation is higher during economic downturns and higher among firms with low credit ratings than among those with high credit ratings. These findings are consistent with the theoretical predictions but inconsistent with some empirical findings based on the M erton default probability measure. We contend that our results are more reliable because of the oversimplified assumptions behind Mertons model and the evidence in the literature that the Merton default probability measure cannot accurately forecast default probabilities.

Jet Blue Case Analsis - 4874 Words

Jet Blue Business Analysis Introduction JetBlue Airways Corporation has established itself as a low-fare passenger airline with a differentiated product and a high-quality customer service. They focus on serving underserved markets and large metropolitan areas that have high average fares. They offer both short-haul and long-haul routes that are point-to-point rather than the hub and spoke route system that has been adopted by most major U.S. airlines. JetBlue was incorporated in Delaware in August 1998 and started operations in February 2000. On April 11, 2002 they announced their initial public offering of its common stock. Their base of operations is at New York John F. Kennedy International Airport (JFK). On February 14, 2003,†¦show more content†¦Differentiation The differentiated aspect of JetBlue s business strategy includes extra-wide leather seats with more legroom. It offers 24-channel live satellite T.V. free at every seat. Pre-assigned seating, superior customer service, and a customer loyalty program. Financials JetBlue JetBlue is a financially growing company. Its revenues have increased from $104.6 million in 2000 to $635 million at the end of 2002. Net Income has also increased from a negative $21.3 million in 2000 to nearly $55 million at the end of 2002. Net Income was already at $245 million as of June 2003. From March of 2003 to June 2003, the share price had risen from $27.71 to $41.98 per share. JetBlue is well on its way to becoming an industry leader. Industry Comparisons In its three years of operation, JetBlue has made great strides with their financial position. Revenues for JetBlue were an impressive $635 million. AirTran Holdings, Inc. had $825 million in revenue, while Southwest had $5,521 million. Southwest has clearly more revenue than JetBlue, but they have been around since 1971 and serve 58 cities to JetBlue s 22 cities. Net Income comparisons show that JetBlue earned $55 million, Southwest had $241 million, and AirTran earned $68 million. The Balance Sheet is a statement of financial position. JetBlue has 29% of its total wealth in assets and 43% in