Do credit rating agencies add value evidence from the sovereign rating business
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Do Credit Rating Agencies Add Value? Evidence from the sovereign rating business. Eduardo Cavallo, IADB Andrew Powell, IADB Roberto Rigobon, MIT. Motivation. Do credit agencies add informational value to an already well functioning financial market?

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Do Credit Rating Agencies Add Value? Evidence from the sovereign rating business

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Do Credit Rating Agencies Add Value? Evidence from the sovereign rating business

Eduardo Cavallo, IADB

Andrew Powell, IADB

Roberto Rigobon, MIT


Motivation

  • Do credit agencies add informational value to an already well functioning financial market?

    • Rating changes are usually anticipated. Hence, they should have been incorporated in interest rates and other financial variables.

    • In sovereign debt, does the rating adds information beyond the information already in the interest rate?

  • Very difficult to disentangle informational content of credit ratings


What do we do?

  • Evaluate informational content using methodology robust to several misspecification errors

  • Evaluate impact of rating changes on stock markets, future spreads, and exchange rates – after controlling for current interest rates and VIX


What we find?

  • Ratings provide information in additional to interest rates

  • Rating upgrades

    • Reduce future interest rate spreads

    • Increase stock markets

    • Appreciate exchange rates

  • Results are quite robustness


Agenda

  • Methodology

  • Data

  • Results

  • Conclusions


Methodology

  • Technically we are askingif the interest rate is a sufficient statistic for the credit rating.

  • We have to allow for misspecification.

  • To test this hypothesis we assume that there is an underlying fundamental for the economy, and interest rates and credit ratings are imperfect measures of it.

  • We evaluate the “sufficient statistic” property of the interest rate trying to explain other financial variables

    • Future spread

    • Stock market

    • Exchange rate


Methodology

X(t)

I(t)


Methodology

X(t)

R(t)


Methodology

X(t)

I(t)

R(t)


Methodology

S(t)

X(t)

I(t)

R(t)


Methodology

  • Idea

    • If the true model isthen we can estimateby OLS or using ratings as IV.

  • Test

    • Under the null hypothesis the OLS estimate and the IV estimate are identical.

    • Under the alternative hypothesis, the OLS and IV are different. The OLS is biased because of EIV, but IV is consistent.


Methodology

  • After we have found that the rating has informational content, we run a horse race between interest rates and ratings.

    • We estimate in a window surrounding credit rating changes. (+/- 10 days)

    • Fixed effect per event

    • Cumulative returns – to deal with endogeneity and anticipation.


Methodology

  • Typical event


Agenda

  • Methodology

  • Data

  • Results

  • Conclusions


Data

  • Source: Bloomberg

  • Daily information

  • 32 emerging market economies

  • January 1st 1998 and April 25th 2007

  • Macro variables: stock market, interest rate spread, dollar exchange rate, VIX

  • Ratings: Moody, S&P, Fitch – transformed to a numerical scale.

  • Unbalanced panel with ~80k observations


Data


Data

  • Concurrence of credit rating changes (21 days)

21

12

15

5


Agenda

  • Methodology

  • Data

  • Results

  • Conclusions


Results

  • Pooled all credit rating events.

  • Fixed effects for each event.

  • Analyze window of 21 days surrounding credit rating change.

  • Use cumulative returns.

  • We are not concerned with interpretation of coefficient. No attempt to disentangle channel of propagation.


Results

  • Table 4: OLS versus IV


Results

  • Table 5: summary


Lessons

  • Informational content

    • Around credit rating changes, ratings provide information beyond interest rates

      • EIV interpretation allows for a robust methodology

      • Robust to specification changes

      • Even though they are anticipated


Results

  • Macro variables and S&P


Results

  • Macro variables, Fitch and Moody


Results

  • S&P upgrades and downgrades


Results

  • Typical event


Lessons

  • Informational content

    • Around credit rating changes, ratings provide information beyond interest rates

      • EIV interpretation allows for a robust methodology

      • Robust to specification changes

      • Even though they are anticipated

  • Rating changes

    • Upgrades

      • Decrease future spreads (0.7% per notch)

      • Increase stock market (0.2% per notch)

      • Appreciate real exchange rate (0.2% per notch)

    • Downgrades

      • Decrease future spreads (0.6% per notch)

      • No impact on stock markets

      • No impact on exchange rates


Results

  • Does changes in asset class have larger impact?

    • We find that changing the asset class has no additional effect for the rating variable.

  • What about outlook changes?

    • Replicate the results for outlook.

    • Estimate degree of anticipation using the outlook change prior to the rating change.


Results

  • Using outlook in the specification


Results

  • Outlook: days between outlook and change.


Results

  • Degree of anticipation


Conclusions

  • Ratings provide information in additional to interest rates

    • Different agencies provide different information

  • Rating upgrades

    • Reduce future interest rate spreads

    • Increase stock markets

    • Appreciate exchange rates

    • All even after controlling for, fixed effects, interest rate and VIX.

  • Robustness

    • Anticipation affects the quantitative results but not the qualitative message

    • Outlooks provide same conclusions


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