Research on CDS and Equity

8/8/2019 Research on CDS and Equity

1/43

The Relationship Between CDS SpreadsAnd Equities Market Volume and Volatility

With Respect to Credit Events

For Single-Name CDS

within CDX.NA.IG Index

Shane Hafer

Professor Antony DnesMay 1, 2008


2/43

Shane Hafer Page - 2 - 5/22/2008

Abstract:

The foremost objective of this study is to determine what, if any, is the

correlation between an individual firms credit default swap spread and their stock

price. The CDS market is currently unregulated and often trades on what has been

dubbed insider information. Therefore, movement in CDS spreads can often be seen

before an announcement becomes public information. Trading that occurs prior to a

credit rating announcement is a perfect example of imperfect information within the

markets. This paper analyzes the relationship between CDS spreads and corresponding

stock prices over a six-month period, looking specifically at abnormal movements in

intraday basis point spreads.

- 2 -


3/43


Index:

I. Introduction

II. Equity Market Volatility and Expected Risk Premium. Chen; Guo; Zhang.

III. The CDS Market:A Primer. Beck. Deutsche Bank Research

IV. The Relationship Between Credit Default Swap Spreads, Bond Yields, and

Credit Rating Announcements. Hull; Predescu; White.

V. Explaining Credit Default Swap Spreads with the Equity Volatility and

Jump Risks of Individual Firms. Zhang; Zhuo; Zhu.

VI. Econometrics of Event Studies. Kothari; Warner.

VII. Liquidity Constraints and Imperfect Information in Subprime Lending.

Adams; Einav; Levin.

VIII. Event Study Methods and Evidence on Their Performance . Armitage.

IX. Methodology

X. Hypothesis

XI. Conclusions

XII. References

XIII. Appendix

- 3 -


4/43


I. Introduction

Traded as an over-the-counter derivative, credit default swaps are essentially an

insurance policy protecting against the default of a specified corporate, sovereign, or

bank issued bond, or more generally a reference entity. A credit default swap consists

of a contractual agreement between counterparties in which they divide and deal with

the credit risk associated with a third-party. These third-parties are publicly traded

corporations that have issued debt in the form of bonds of various maturities. A CDS

buyer (protection buyer) will pay a isochronous fee to a CDS seller (protection seller)

in return for a specified compensation in the occasion that a credit event does occur or

the CDS reaches its termination date. Compensation can come in two forms; a cash

settlement or a physical settlement. Physical settlements entail the delivery of the

reference entity at par value; whereas a cash settlement is a payment consisting of the

difference in par value and actual value of the reference entity. Credit events include

upgrades and downgrades in credit ratings based on default-probability, or complete

default including bankruptcy.

Credit default swaps are traded in basis points in the street. Traders can use

swaps to hedge their cash or corporate bond trades or diversify their books. However,

the market for CDS has increased drastically as they are the most liquid of the credit

derivative products. Traders can find arbitrage opportunities within the CDS market by

speculating on a companys changing credit quality as rated by S&P, Moodys, or

Fitch. As the risk of default increases, credit rating will decrease, raising the price and

- 4 -


5/43


widening the basis point spread at which a CDS is trading. The opposite is also

intuitively true. The difference between the issued effective date and termination date

varies throughout the market, however 5-year CDS trade with the highest frequency.

When analyzing the maturity for reference entities (bonds), a closer maturity (1yr, 2yr,

3yr, etc.) denotes a lesser probability of default, and thus tighter CDS spreads.

Whereas, for a corporate bond that matures in 30 years, the probability that its issuer

will default within the next 30 years rather than, say 5 years, is inherently higher.

Therefore, the relationship between CDS spreads and default probability is positively

correlated.

As 5yr CDS are the most commonly traded, this research will focus specifically

on that product. The CDS indices, or CDX, are split up according to credit quality and

other factors. This paper will use the constituents of both the CDX.NA.IG and the

CDX.NA.HY indices; investment grade and high yield, respectively. These indices

represent opposite ends of the credit quality spectrum, and thus will give us a broad

basis in data with which interpolations can be formed; in total, 225 single-name

constituents. Many of these constituents are traded publicly in the form of equity or

stocks. The equities market is also affect by credit events in a converse relationship.

However, the CDS market has historically seen basis point changes in reaction to credit

events prior to equities market response.

The goal of this paper is to determine the degree of correlation between

movement in the credit market and equities market given a credit event; and with this

mind, if the credit derivative market acts as a predictor for shifts in the equities market.

- 5 -


6/43


More specifically, to see how the CDS and equities markets reacted to single-day jumps

in basis point spreads during the crash of the subprime market. The sample coverage

starts March 1, 2007 and ends August 31, 2007.

- 6 -


7/43


II. Equity Market Volatility and Expected Risk Premium

Equities pricing methods, including the traditional capital asset pricing model

(CAPM) are analyzed by Long Chen, Hui Guo, in accordance to the risk-return tradeoff

that exists in the equities market. A method for measuring expected return is created

using the theory that, because debt and equity are financial claims written on the same

corporation productions, they must share the same systematic risk that affects firm

fundamentals. Here, a direct link between the corporate bond and equities market is

formed by linking the ex ante (beforehand) risk premium to the yield spread in the cash

market. There is thus a transitive connection between the equities and credit market. In

general there is a tendency to believe that with greater risk, comes higher returns. A

2005 paper by Pastor, Sinha, and Swaminathan agrees with this concept.

The goal of their paper is to create an ex ante risk premium that can predict

realized stock market returns. Its relevance to the credit market lies in the formula in

which they derived to equate expected returns. The yield spread that is used to price

corporate bonds uses a similar forward-looking theory and can be used to assist in

creating an estimated ex ante risk premium after compensating for default risk and for

tax effects.

- 7 -


8/43


III. The CDS Market: A Primer

Beck discusses the basic principles behind the CDS market and default

probabilities. The relationship between the buyer and seller of protection is analyzed as

well as methods of settlement. The CDS market has increased in volume of contracts

over the past two decades, beginning in the 1990s, when they began to be traded with

more frequency as their own product. With the removal of the regulatory uncertainty

created by Basle II, the market experienced more growth. North America controls

much of the market, which is being created by the debt issued by corporations, banks,

and sovereigns. Sovereigns are traded in higher volume in Europe and thus will not be

included in my research. Movement in CDS generally occurs before bond spread

movements. The volume and liquidity within the marketplace is inclined to increase in

light of an approaching credit event; whereas, the bond market will slow as people are

unwilling to take risks. Arbitrage opportunities are more prevalent due to the way in

which spreads are recorded; thus imperfect information among traders is highly

probable.

- 8 -


9/43


IV. The Relationship Between Credit Default Swap Spreads , Bond Yields, and

Credit Rating Announcements

Data for this study was provided by GFI, a large inter-dealer brokerage. When

a trade is brokered by GFI, the corresponding bid/offer spread is recorded. Since 1998,

the number of recorded spreads has increased from 4,759 per day to over 125,000 in

2002. That number has only risen since then. Five year contracts now account for

eighty-five percent of the daily volume. In order to create an estimated value for the

actual traded basis point spread, an average of the maximum bid and the minimum offer

was used for this study. This concept will be integral for my research.

The goal of this study was to provide information on whether or not spreads

anticipate or follow credit events. The question of whether single name CDS with high

five year spreads were more likely to be downgraded and vice versa was posed. The

study found evidence to support anticipated movement within the CDS market for

credit downgrades and negative outlooks. However, upgrades and positive outlook

reports seemed to create no ex ante movement. Therefore, downgrades and negative

ratings reports will be the focus of my research.

- 9 -


10/43


V. Explaining Credit Default Swap Spreads with the Equity Volatility and

Jump Risks of Individual Firms

Zhang, Zhou, and Zhu use an innovative approach to determine CDS premiums

by analyzing the volatility and jump risk of individual firms from high-frequency

equity (return) prices. Since both volatility and jump risk can only act as predictors

for certain proportions of CDS spreads, the variables are looked at together while

controlling for alternative external factors. Old methods have not been as efficient due

to the fact that predicted credit spreads and far below observed values. Both historical

and realized data (long and short term, respectively) was collected for equity returns.

Many old papers had used corporate bond spreads and yields in order to calculate the

CDS premium; however, here CDS spreads were used because (1) CDS spreads

provide relatively pure pricing of the default risk of the underlying company and (2) in

the short run CDS spreads tend to respond more quickly to changes in credit

conditions.

Data on CDS spreads for this research was provided by the Markit Group; the

same organization that has provided the data for my own study. Their sample covers

the calendar years of 2001, 2002, and 2003, and analyzes 307 single-name entities.

After controlling for outside variables including ratings dummies, individual balance

sheet information, and macroeconomic conditions; equity volatility and jump are found

to be most closely correlated and significant in predicting default risk premiums. This

- 10 -


11/43


correlation is strongest for high-yield entities and firms under financial stress, or firms

with the highest probability of default.

- 11 -


12/43


VI. Econometrics of Event Studies

Kothari and Warners article is a general overview of event study methods. For

specific non-random samples such as the single-name CDS spreads and equity prices

that my research will analyze, properties of event study methods can vary by calendar

time period and can depend on event sample firm specific characteristics such as

volatility. The specific event in my research is a credit event, defined by a single

day movement in BPS of more than half of a standard deviation away from the mean.

The number of papers on event studies continues to grow, but this paper, being more

recent (2002) than ArmitagesEvent Study Methods and Evidence on Their

Performance (1995) makes it more relevant for analyzing my data. Both short-horizon

and long-horizon methods are interpreted, with some new knowledge added to each.

Older event studies from the five leading economic journals were consulted dating back

to 1974. Journals cited are: the Journal of Business, Journal of Finance, Journal of

Financial Economics, Journal of Financial and Quantitative Analysis, and the Review

of Financial Studies. My research will focus on short-horizon methods, since the

sample dates encompass less than one year. In general, it can be said that the best

statistical format of event studies has not changed over time. Changes in data used for

short-horizon methods pertain to the use of intraday returns data or spreads and will

have an effect on my own research. Although long-horizon methods still face

limitations, short-horizon test are the cleanest evidence we have on efficiency (Fama,

1991, p.1602) and will be used to analyze my data.

- 12 -


13/43


VII. Liquidity Constraints and Imperfect Information in Subprime Lending

Liquidity constraints, or rather borrowing opportunities, have not been studied

with respect to consumer behavior, specifically in the credit market, until this paper.

Here, data for subprime lenders and borrowers was collected from a large automotive

company because of the fact that they originate loans. Borrowers who would normally

not qualify for a bank loan are those who comprise the subprime market. Moral hazard

and adverse selection are both analyzed and found to be positively correlated to

increased loan size and probability of default.

The evidence produced by this research depicts: (1) the underlying forces of

informational models of lending, namely moral hazard and adverse selection; (2) the

supply-side responses these models predict, specifically loan caps, variable interest

rates, and risk-based pricing; and (3) the predicted consequences , specifically liquidity

effects in purchasing behavior. In general, it seems that default risk and loan size are

positively related, and those who are at the greatest risk of default tend to seek out the

largest loans. It must be noted that this article was published in April of 2007, prior to

the major fall of the subprime lending market, yet still may provide insight into the

causes prior to the collapse.

- 13 -


14/43


VII. Event Study Methods and Evidence on Their Performance

Seth Armitages paper concerning event studies refers to abnormal returns from

equities using the standard market model. Testing the significance of the predicted

returns helps us understand how well they can be used to explain correlation. He uses a

constructed t-statistic to perform these tests. Assuming investors in a market are risk

averse, higher risk should result in a higher variation in returns, as stated previously.

Caveats such as omitted-variable bias and autocorrelation can skew estimated

coefficients and must be corrected for. The market modelreads as follows:

ARit= Rit- (i+ i Rmt).

Where ARit is the abnormal return, Rit is the actual return, i and i are estimated

coefficients and Rmt is the market rate of return. The CAPM model, which is most

commonly seen, is used as follows:

E(Rit) = Rft+ Pi [E(Rmt) Rft]

where E(Rit) is the expected or normal return on share i for time t, Rftis some measure

of the risk-free rate of interest, E(Rmt) is some measure of the expected return on the

appropriate stock market and i is the covariance ofRitwithRmtover some estimation

period (cov [Rit, Rmt]) divided by the variance ofRmtover that period (s2[Rmt]). Using

the market model suggests that specified events occurred at or on the same date, which

in the case of my research is accurate. However, the CAPM method will be useful in

determining any inconsistencies in estimated returns. Armitages paper puts a strong

emphasis on choosing the appropriate method for a specified event study. Estimation

- 14 -


15/43


periods are also discussed, defining any period over 100 days as sufficient for

estimation of coefficients. Thin trading is also recognized as a problem that could

cause bias; however, since I am analyzing a period of high volume trading, this should

not be an issue.

- 15 -


16/43


VIII. Methodology

The data provided by the Markit Group included credit default swap spreads for

the time interval of March 1, 2007 to August 31, 2007. Daily spreads for the 225

constituents of the 'Investment Grade' and 'High Yield' credit indices were provided,

along with their credit ratings and reference entities, comprising a total of 37,012 data

points. In order to analyze these spreads, they had to first be converted into basis

points, the value in which they are traded. Each inter-day spread simply had to be

multiplied by 10,000. From here, the data had to be narrowed down into specific

sections, which would be later individually regressed against their equities counterparts.

First, I chose to break the 225 constituents apart into their separate and

respective indices, investment grade and high yield. The decision to use both indices

was made because they represent opposite ends of the credit rating spectrum;

investment grade being a derivative of highly rated corporate bonds (as deemed by

Moody's, Standard and Poor's, and Fitch) and high yield being a derivative of anything

less than investment grade, or 'junk bonds'. However, high yield CDS rarely trade on a

rated reference entity (junk bond), and therefore there use in this study was not

applicable. Next, the CDS were separated into their corresponding sectors: Basic

Materials, Financials, Utilities, Health Care, Telecommunications, Technology,

Consumer Services, Oil and Gas, Consumer Goods, Industrials, and Government.

In order to examine the effects of a credit event on the corresponding firms

stock price I began to look for basis point jumps in the intraday CDS spreads. Specific

- 16 -


17/43


information concerning credit rating announcements could not be made available by

Moodys or S&P, thus, I began to look for movements in price that were out of the

ordinary. To do this, I found the standard deviation of each single-name firms CDS

spreads for the given period. Next, the variation in standard deviation for each intraday

spread was created and evaluated. I decided to focus on intraday jumps of 0.5 standard

deviations or more, which I defined as out of the ordinary movement. With this in

mind, I selected twenty single-name firms that displayed the greatest frequency of out

of the ordinary basis point jumps. Then, I attained historical stock prices for the

twenty specific firms during the corresponding six-month period. With this

information, I was able to regress each firms CDS spreads against their stock prices,

controlling for the Dow Jones Industrial Average, and stock volume. Including a

variable for interest rates was considered, however, the Fed Funds rate was not changed

during the specified period.

To analyze each BPS jump more thoroughly, I wanted to look for correlations

that existed within the five days prior and past the day of our CDS movement. Each

individual firm was reviewed, BPS jumps specified, and regressed against the stock

price, Dow Jones Industrial Average, and stock volume for the corresponding dates.

Referencing Armitages paper on event studies, we see that he states that a period over

100 days is sufficient for estimating coefficients. Here, we are only looking at an

eleven day interval, which means that our model will not be as fit for estimation our

coefficients, and we should see low R-squared values. However, to see if there is any

change in correlation related to our BPS jump days, this is a necessary process.

- 17 -


18/43


X. Hypothesis

The foremost objective of this study is to determine what, if any, is the

correlation between an individual firms credit default swap spreads and their stock

price. The CDS market is currently unregulated and often trades on what has been

dubbed insider information. Therefore, movement in CDS spreads can often be seen

before an announcement becomes public information. A credit rating announcement is

a perfect example of this. We can use the abnormal jumps discussed before to

recognize some sort of announcement or just a large movement in CDS prices. If these

jumps are occurring prior to the publication of information about a particular firm,

these spread jumps could be used to predict a change in corresponding future stock

prices.

A positive correlation within the regression would tell us that as the CDS spread

increases, so does stock price. This is not what this study expects to see for a few

simple reasons. If the CDS spread is getting wider, the probability that that individual

firm will default on its bonds is greater. Therefore, if the probability of default is

greater, the company would be in worse economic shape, and thus we should see stock

prices falling. On the contrary, with our specific variables controlled for, we should see

that CDS spreads and stock prices are negatively correlated. Such that, as the CDS

spread widens, the corresponding stock price should fall. A tightening spread is not

mentioned because it would depict a positive rating announcement, which can be

anticipated and thus the spread and stock price should move in unison.

- 18 -


19/43


IX. Conclusions

After analyzing each chosen firm, we are left with forty separate regressions.

Estimated coefficients for basis point spread, stock volume, and the Dow Jones

Industrial Average regressed against stock price are listed in the attached appendix.

Graphs of each firms basis point spread versus their stock price are also attached.

By analyzing these estimated coefficients as well as their significance levels and

each models R-squared values we can come to certain conclusions about the data.

When we inspect the regressions containing data for the entire six-month period

we find that seventy percent of the firms that have been examined show negative

correlations between their individual CDS spreads and corresponding stock prices.

Sixty percent of the selected firms show negative correlations in the same category

with estimated coefficients that are significant at a ninety-nine percent confidence

interval. R-squared values for all of these models are within an acceptable range,

meaning that the models themselves are fit to estimate these coefficients. Looking

at the Dow Jones Industrial Average we can see that seventy-five percent of the

firms show positive correlations between their stock prices and the DJI, with sixty-

five percent significant at the ninety-nine percent confidence interval. This merely

tells us that the majority of firms intraday movements coincide with the market as

a whole. In general, we see that the movement of the equities market, as a whole, is

more closely related to stock price that movement in the CDS spreads.

- 19 -


20/43


When we examine the regressed data for the five days prior and post abnormal

BPS jumps, we see similar results with respect to the signs of the estimated

coefficients. However, these results are not significant for further extrapolation.

This is as expected, as the data only covers an eleven-day period, which is not a

large enough interval to form specific conclusions about the general movement and

correlation between credit and equities markets.

The estimated coefficients in general depict the fact that a negative relationship

between CDS spreads and stock prices does generally exist. However, we cannot

tell whether movement in one market will trigger a movement in the other.

Hypothetically, a trader could be able to recognize movement in one market and

trade accordingly in the other. However, the duration of these movements will

always be unknown, thus making it impossible to speculate with absolute certainty.

We must also remember the fact the past movements in any financial market will

not be depicted in exact likeness in future markets. Therefore, the relationships

provided by this research will not always be accurate and applicable to subsequent

markets.

- 20 -


21/43


- 21 -


22/43


XII. References

Adams, William; Einav, Lirian; Levin, Jonathan.2007.Liquidity Constraints and Imperfect Information in Subprime Lending.

National Bureau of Economic Research: NBER Working Paper Series. Working

Paper 13067. April 2007.

Armitage, Seth.

1995. Event Study Methods and Evidence on Their Performance . Journal of

Economic Surveys. Vol. 9, Issue 1, Pp. 25-52.

Beck, Roland.

CDS Market: A Primer. Deutsche Bank Research: Risk Analysis Group.http://www.dbresearch.com/PROD/DBR_INTERNET_EN-

PROD/PROD0000000000183612.pdf.

Chen, Long; Guo, Hui, Zhang, Lu.2006. Equity Market Volatility and Expected Risk Premium. Federal Reserve Bank of

St. Louis: Research Division: Working Papers Series.

http://research.stlouisfed.org/wp/2006/2006-007.pdf. January, 2006.

Hull, John; Predescu, Mirela; White, Alan.

2004. The Relationship Between Credit Default Swap Spreads, Bond Yields, and

Credit Rating Announcements. Joseph L. Rotman School of Management.

University of Toronto. January, 2004.

Kothari, S.P.; Warner, Jerolod B.

2004. Econometrics of Event Studies. Handbook of Corporate Finance: EmpiricalCorporate Finance. Ed. B. Espen Eckbo. October, 2004.

Zhang, Benjamin Yibin; Zhou, Hao; Zhu Haibin.

2006. Explaining Credit Default Swap Spreads with the Equity Volatility and Jump

Risks of Individual Firms.

http://www.utahwfc.org/2007papers/credit%20default.pdf. December, 2006

- 22 -
http://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000183612.pdfhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000183612.pdfhttp://research.stlouisfed.org/wp/2006/2006-007.pdfhttp://www.utahwfc.org/2007papers/credit%20default.pdfhttp://www.utahwfc.org/2007papers/credit%20default.pdfhttp://research.stlouisfed.org/wp/2006/2006-007.pdfhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000183612.pdfhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000183612.pdf


23/43


ALCOA Inc:

BPS/Stock Price @ Close:

6 Month Period:

_cons -44.12494 5.45929 -8.08 0.000 -54.93215 -33.31773dji .0063126 .0004632 13.63 0.000 .0053956 .0072297

volume 7.46e-09 1.53e-08 0.49 0.627 -2.28e-08 3.78e-08bps -.0372941 .0247209 -1.51 0.134 -.0862316 .0116434

stockclose Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 1590.24498 125 12.7219598 Root MSE = 1.8576 Adj R-squared = 0.7288 Residual 421.000088 122 3.4508204 R-squared = 0.7353 Model 1169.24489 3 389.748296 Prob > F = 0.0000

F( 3, 122) = 112.94Source SS df MS Number of obs = 12 6

reg stockclose bps volume dji

Credit Jump +/- 5 Days:

_cons -16.83162 38.65551 -0.44 0.676 -108.2374 74.57412DJI .003741 .0029585 1.26 0.247 -.0032548 .0107367Vol 1.62e-08 1.09e-08 1.49 0.181 -9.57e-09 4.19e-08BPS .1057624 .02325 4.55 0.003 .0507848 .1607399

Stock Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 32.5242624 10 3.25242624 Root MSE = .62935Adj R-squared = 0.8782

Residual 2.77257822 7 .396082603 R-squared = 0.9148 Model 29.7516842 3 9.91722806 Prob > F = 0.0004 F( 3, 7) = 25.04

Source SS df MS Number of obs = 11reg Stock BPS Vol DJI

- 23 -


24/43


AT&T Inc:


6 Month Period:

_cons 15.27632 1.814364 8.42 0.000 11.68547 18.86717dji .0018686 .0001422 13.14 0.000 .0015872 .00215

volume -5.67e-09 1.05e-08 -0.54 0.590 -2.65e-08 1.51e-08bps -.0125338 .0123361 -1.02 0.312 -.0369485 .0118809

close Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 189.867141 128 1.48333704 Root MSE = .78062 Adj R-squared = 0.5892 Residual 76.171291 125 .609370328 R-squared = 0.5988 Model 113.69585 3 37.8986168 Prob > F = 0.0000


reg close bps volume dji

Credit Jump +/- 5 days:

_cons 13.7358 29.16768 0.47 0.652 -55.2348 82.7064DJI .0019686 .0021651 0.91 0.393 -.0031512 .0070883Vol -6.50e-09 2.78e-08 -0.23 0.822 -7.22e-08 5.92e-08BPS -.0025982 .033204 -0.08 0.940 -.0811133 .0759168


Total 2.64195623 10 .264195623 Root MSE = .56886Adj R-squared = -0.2249

Residual 2.26522269 7 .323603241 R-squared = 0.1426 Model .376733541 3 .125577847 Prob > F = 0.7654 F( 3, 7) = 0.39


- 24 -


25/43


CARNIVAL CRUISE LINES Corp:


6 Month Period:

_cons 22.63656 2.272978 9.96 0.000 18.13805 27.13506dji .0022463 .0001787 12.57 0.000 .0018926 .0025999

volume -1.42e-08 4.77e-08 -0.30 0.766 -1.09e-07 8.02e-08bps -.1959451 .0133291 -14.70 0.000 -.2223249 -.1695652


Total 415.114773 128 3.24308417 Root MSE = .99556 Adj R-squared = 0.6944 Residual 123.893487 125 .991147895 R-squared = 0.7015 Model 291.221286 3 97.0737621 Prob > F = 0.0000






Total 26.3615552 10 2.63615552 Root MSE = 1.123Adj R-squared = 0.5216

Residual 8.82743693 7 1.26106242 R-squared = 0.6651 Model 17.5341183 3 5.84470609 Prob > F = 0.0435 F( 3, 7) = 4.63


- 25 -


26/43


COUNTRYWIDE HOME LOANS Inc:


6 Month Period:

_cons 29.19909 6.296061 4.64 0.000 16.73742 41.66076dji .0008046 .0004785 1.68 0.095 -.0001426 .0017518

volume -4.07e-09 1.51e-08 -0.27 0.788 -3.40e-08 2.59e-08bps -.0488916 .004125 -11.85 0.000 -.0570561 -.0407271


Total 4337.77091 127 34.1556765 Root MSE = 2.7567 Adj R-squared = 0.7775 Residual 942.316653 124 7.59932784 R-squared = 0.7828 Model 3395.45426 3 1131.81809 Prob > F = 0.0000


. reg close bps volume dji


_cons 101.0165 20.32692 4.97 0.002 52.95098 149.082DJI -.0051843 .0015154 -3.42 0.011 -.0087676 -.0016011Vol -5.37e-08 2.99e-08 -1.79 0.116 -1.24e-07 1.71e-08BPS -.007808 .0056686 -1.38 0.211 -.0212121 .0055962



Residual 3.26496883 7 .466424119 R-squared = 0.8472 Model 18.1031073 3 6.03436909 Prob > F = 0.0030

F( 3, 7) = 12.94Source SS df MS Number of obs = 11

reg Stock BPS Vol DJI

- 26 -


27/43


CIT GROUP Inc:


6 Month Period:

_cons 43.74977 9.009287 4.86 0.000 25.91927 61.58027dji .0012552 .0006863 1.83 0.070 -.0001031 .0026135

volume -1.68e-06 2.05e-07 -8.18 0.000 -2.09e-06 -1.27e-06bps -.0464517 .0047751 -9.73 0.000 -.0559022 -.0370012



Residual 2004.61702 125 16.0369362 R-squared = 0.7869 Model 7403.37871 3 2467.7929 Prob > F = 0.0000




_cons -49.4876 45.97478 -1.08 0.317 -158.2007 59.22549DJI .0065542 .0034278 1.91 0.097 -.0015514 .0146597Vol -2.87e-07 2.53e-07 -1.13 0.294 -8.84e-07 3.11e-07BPS .0009357 .0045115 0.21 0.842 -.0097324 .0116038






- 27 -


28/43


CAP ONE BANK:


6 Month Period:

_cons 71.85993 4.170254 17.23 0.000 63.60648 80.11338dji .0007424 .0003145 2.36 0.020 .00012 .0013647

volume -1.05e-07 6.04e-08 -1.73 0.086 -2.24e-07 1.50e-08bps -.173192 .0080339 -21.56 0.000 -.1890921 -.157292







_cons -39.25931 38.88331 -1.01 0.346 -131.2037 52.6851DJI .0082111 .0027769 2.96 0.021 .0016448 .0147775Vol 3.42e-08 2.45e-07 0.14 0.893 -5.46e-07 6.14e-07BPS -.0174378 .0398973 -0.44 0.675 -.1117801 .0769044






- 28 -


29/43


E I Du PONT De NEMOURS & Co:


6 Month Period:

_cons 33.41933 2.152864 15.52 0.000 29.15821 37.68045dji .0018841 .0001737 10.84 0.000 .0015402 .002228

volume -1.53e-07 4.51e-08 -3.40 0.001 -2.43e-07 -6.41e-08bps -.4080154 .0304348 -13.41 0.000 -.4682544 -.3477764










Residual 1.12641852 7 .160916932 R-squared = 0.0960 Model .119637222 3 .039879074 Prob > F = 0.8605



- 29 -


30/43


QUEST DIAGNOSTICS Inc:


6 Month Period:

_cons 26.52676 5.462232 4.86 0.000 15.71632 37.33719dji .0017567 .0004515 3.89 0.000 .0008632 .0026502

volume 5.35e-07 2.32e-07 2.30 0.023 7.45e-08 9.95e-07bps .0190983 .0264182 0.72 0.471 -.0331865 .0713832







_cons 34.94796 18.95838 1.84 0.108 -9.881484 79.7774DJI .0009694 .0014359 0.68 0.521 -.002426 .0043648Vol 1.00e-07 1.02e-07 0.98 0.361 -1.42e-07 3.42e-07BPS .0751618 .0136729 5.50 0.001 .0428305 .1074931






- 30 -


31/43


DOW CHEMICAL Co:


6 Month Period:

_cons 23.75441 5.143079 4.62 0.000 13.57562 33.9332dji .0014789 .0003522 4.20 0.000 .0007819 .002176

volume 1.52e-07 4.61e-08 3.31 0.001 6.11e-08 2.43e-07bps .0165229 .0226871 0.73 0.468 -.0283778 .0614236







_cons 63.41138 30.20091 2.10 0.074 -8.002412 134.8252DJI -.0015939 .0025025 -0.64 0.544 -.0075113 .0043235Vol 4.49e-08 4.15e-08 1.08 0.315 -5.33e-08 1.43e-07BPS .0382829 .0411772 0.93 0.383 -.0590858 .1356515


Total 3.6323636 10 .36323636 Root MSE = .55085Adj R-squared = 0.1646

Residual 2.1240538 7 .303436257 R-squared = 0.4152 Model 1.5083098 3 .502769933 Prob > F = 0.2617


. reg Stock BPS Vol DJI

- 31 -


32/43


INTERNATIONAL PAPER Co:


6 Month Period:

_cons 14.25565 3.35084 4.25 0.000 7.623926 20.88738dji .0021602 .000236 9.15 0.000 .0016931 .0026273

volume -5.71e-07 1.14e-07 -5.01 0.000 -7.97e-07 -3.45e-07bps -.0848805 .0195635 -4.34 0.000 -.1235992 -.0461619







_cons -.4261582 16.83006 -0.03 0.981 -40.22293 39.37061DJI .0033492 .0010899 3.07 0.018 .000772 .0059264Vol -5.49e-07 7.24e-08 -7.58 0.000 -7.20e-07 -3.77e-07BPS -.083119 .0438659 -1.89 0.100 -.1868453 .0206074



Residual .514455898 7 .0734937 R-squared = 0.9868 Model 38.3509561 3 12.783652 Prob > F = 0.0000



- 32 -


33/43


JC PENNEY Co Inc:


6 Month Period:

_cons 116.4229 9.316787 12.50 0.000 97.98386 134.862dji -.0020301 .0007354 -2.76 0.007 -.0034855 -.0005747

volume -1.10e-06 3.04e-07 -3.61 0.000 -1.70e-06 -4.95e-07bps -.1854468 .0238931 -7.76 0.000 -.2327343 -.1381593







_cons -22.69739 81.04675 -0.28 0.788 -214.3425 168.9477DJI .0072171 .0052992 1.36 0.215 -.0053135 .0197477Vol -8.77e-09 7.04e-07 -0.01 0.990 -1.67e-06 1.66e-06BPS -.046098 .0855453 -0.54 0.607 -.2483804 .1561844






- 33 -


34/43


THE KROGER Co:


6 Month Period:

_cons 12.4357 3.699305 3.36 0.001 5.114315 19.75708dji .0011042 .0002682 4.12 0.000 .0005734 .0016351

volume -6.97e-08 6.05e-08 -1.15 0.252 -1.89e-07 5.00e-08bps .0383019 .0239438 1.60 0.112 -.0090858 .0856896










Residual .598957381 7 .08556534 R-squared = 0.5966 Model .885861354 3 .295287118 Prob > F = 0.0803



- 34 -


35/43


MARSH & McLENNAN Co Inc:


6 Month Period:

_cons 42.13521 6.008848 7.01 0.000 30.24295 54.02747dji -.0014391 .000537 -2.68 0.008 -.0025018 -.0003763

volume -4.82e-07 8.29e-08 -5.81 0.000 -6.46e-07 -3.18e-07bps .1397242 .0227777 6.13 0.000 .0946442 .1848042







_cons -34.02661 7.97018 -4.27 0.004 -52.87309 -15.18013DJI .0049898 .0006581 7.58 0.000 .0034336 .006546Vol 1.12e-08 5.77e-08 0.19 0.851 -1.25e-07 1.48e-07BPS .022297 .0126235 1.77 0.121 -.0075528 .0521469






- 35 -


36/43


MOTOROLA Inc:


6 Month Period:

_cons 23.05115 1.444472 15.96 0.000 20.19236 25.90994dji -.0002699 .0001029 -2.62 0.010 -.0004736 -.0000662

volume 5.17e-09 3.04e-09 1.70 0.092 -8.51e-10 1.12e-08bps -.0451069 .0067156 -6.72 0.000 -.0583979 -.0318159







_cons 28.17332 20.42442 1.38 0.210 -20.12275 76.46939DJI -.0008373 .001837 -0.46 0.662 -.0051812 .0035066Vol -3.96e-09 2.94e-09 -1.35 0.221 -1.09e-08 3.00e-09BPS .0111445 .0557029 0.20 0.847 -.120572 .1428609






- 36 -


37/43


RADIAN GROUP Inc:


6 Month Period:

_cons 70.26996 11.93501 5.89 0.000 46.6491 93.89082dji -.0006733 .0009077 -0.74 0.460 -.0024697 .0011232

volume -8.42e-07 1.75e-07 -4.81 0.000 -1.19e-06 -4.96e-07bps -.0592581 .0026548 -22.32 0.000 -.0645123 -.054004







_cons 99.80089 68.29364 1.46 0.187 -61.68789 261.2897DJI -.005236 .0049411 -1.06 0.324 -.0169198 .0064479Vol 2.27e-08 1.17e-07 0.19 0.852 -2.53e-07 2.99e-07BPS -.0160953 .005864 -2.74 0.029 -.0299614 -.0022293






- 37 -


38/43


SPRINT NEXTEL Corp:


6 Month Period:

_cons -1.847439 2.162773 -0.85 0.395 -6.127834 2.432956dji .0016159 .0001798 8.99 0.000 .00126 .0019717

volume -3.04e-09 1.10e-08 -0.28 0.782 -2.47e-08 1.87e-08bps .0195953 .0107189 1.83 0.070 -.0016187 .0408092













- 38 -


39/43


TOLL BROTHERS Inc:


6 Month Period:

_cons 46.80916 2.718766 17.22 0.000 41.42838 52.18993dji -.0011265 .0002063 -5.46 0.000 -.0015348 -.0007182

volume -5.03e-08 7.05e-08 -0.71 0.477 -1.90e-07 8.92e-08bps -.0357712 .0021359 -16.75 0.000 -.0399983 -.031544







_cons 5.719382 29.30595 0.20 0.851 -63.57819 75.01695DJI .0016073 .0020743 0.77 0.464 -.0032978 .0065123Vol 3.61e-07 1.15e-07 3.14 0.016 8.87e-08 6.33e-07BPS -.0244467 .0110138 -2.22 0.062 -.0504902 .0015967






- 39 -


40/43


UNIVERSAL HEALTH SERVICES Inc:


6 Month Period:

_cons 46.87307 6.680559 7.02 0.000 33.65142 60.09473dji .0019875 .000517 3.84 0.000 .0009643 .0030107

volume -3.12e-07 1.05e-06 -0.30 0.766 -2.38e-06 1.76e-06bps -.179819 .0251627 -7.15 0.000 -.229619 -.130019







_cons -19.54883 27.58562 -0.71 0.501 -84.77847 45.6808DJI .0059628 .0023093 2.58 0.036 .0005021 .0114234Vol 4.97e-07 1.46e-06 0.34 0.743 -2.95e-06 3.94e-06BPS .0626159 .0366148 1.71 0.131 -.0239644 .1491962






- 40 -


41/43


VERIZON COMMUNICATIONS Inc:


6 Month Period:

_cons -13.14573 2.28056 -5.76 0.000 -17.65924 -8.632217dji .0039726 .0001796 22.12 0.000 .0036171 .0043281

volume 7.19e-09 2.29e-08 0.31 0.754 -3.81e-08 5.25e-08bps .0625984 .0166616 3.76 0.000 .0296229 .0955738







_cons 65.39858 31.54493 2.07 0.077 -9.193322 139.9905DJI -.0014349 .0021064 -0.68 0.518 -.0064157 .0035459Vol -8.21e-08 8.25e-08 -1.00 0.353 -2.77e-07 1.13e-07BPS -.060038 .0746393 -0.80 0.448 -.236532 .116456






- 41 -


42/43


WEYERHAEUSER Co:


6 Month Period:

_cons 46.22188 8.784446 5.26 0.000 28.83638 63.60739dji .0038395 .0006734 5.70 0.000 .0025069 .0051722

volume 6.05e-07 1.37e-07 4.42 0.000 3.34e-07 8.76e-07bps -.3907219 .0250832 -15.58 0.000 -.4403647 -.3410791







_cons -96.39902 161.0041 -0.60 0.568 -477.1133 284.3153DJI .0132798 .0107416 1.24 0.256 -.0121201 .0386797Vol 1.00e-07 4.31e-07 0.23 0.823 -9.20e-07 1.12e-06BPS -.109467 .2112026 -0.52 0.620 -.6088817 .3899477






- 42 -


43/43


Research on CDS and Equity

Documents

Transcript of Research on CDS and Equity