Capital Market Research Forum 1/2556 · Capital Market Research Forum 1/2556 22...

Capital Market Research Forum 1/2556

22 กุมภาพันธ์ 2556

“ปัจจัยที่มผีลกระทบต่อการเปลี่ยนแปลงส่วนต่างเครดติส าหรับตราสารหน้ีหุ้นกู้เอกชนในประเทศไทย ที่มีอันดับความน่าเชื่อถือ

ที่สามารถลงทุนได้ ระหว่างมิถนุายน 2549 ถึง กุมภาพันธ์ 2555 การใช้งานของแบบจ าลองการเปล่ียนแปลงตามภาวะ”

โดย คุณธีรพจน์ ก้องธรนินทร์คณะบริหารธุรกิจ มหาวิทยาลัยอัสสัมชัญ

Research Objectives and Benefits for Thai Capital Market

• This study explores the relationship between interest rate, macroeconomic and liquidity risk factors and credit spread with an application of Markov Switching model to identify two credit cycle regimes. The approach is used enhance the explanatory power of the model in explaining credit spread puzzle.

Objectives

• Individual can use systematic risk factors to predict the credit spread change during low and high regimes

• Issuers can set an appropriate IPO price of corporate bonds.• Regulator can set policy appropriately to avoid persistent

effect of credit cycle.

Benefits for Thai Capital Market

• Credit spread change is insensitive in the low regime, and the sensitivities of interest rate, macroeconomic, and liquidity factors are according to the theories in the high regime. Low credit rating is insensitive to liquidity factors.

Findings

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• Most empirical studies on credit spread change precluding idiosyncratic andsystematic risk factors are failed to explain the credit spread puzzle. In practice,credit spread change varies in response to the credit cycle which is different from thebusiness cycle. This study proposes an empirical model with two-state switchingregime model of low and high variance regimes which are extracted by Markovswitching model to explain the variation of credit spread change in Thailand. Theresults suggest that the model can explain the variation of credit spread change moreefficient than the single-regime model. The sensitivities of interest rate,macroeconomic, and liquidity factors are consistent with associated theories and thecredit spread change are more sensitive to these factors in high regime across creditrating and time-to-maturity groups. The low rating group is not sensitive to theliquidity risk.

THE DETERMINANTS OF CREDIT SPREAD CHANGES OF INVESTMENT GRADE CORPORATE

BONDS IN THAILAND BETWEEN JUNE 2006 AND FEBRUARY 2012: AN APPLICATION OF REGIME

SWITCHING MODEL

3

Executive Summary

Generalities of the StudyCorporate Bonds



SWITCHING MODEL

4

defaultable

High YTM

time

LEN

DER

(IN

VES

TOR

S)B

OR

RO

WER

(FI

RM

S)

PR

INC

IPA

L

C1

0 1 2 TTM

C2

Ct

Cn

t

Credit Spread

Spot Rate

Time-to-Maturity

Credit spread and bond price are negatively correlated. Therefore the raise of credit

spread, reduce the bond price.

Default eventSource: Bank for International Settlement

Source: U.S. from Bloomberg; Japan, China, and Thailand retrivedJune12th,2012 from Asian Bond Online, Retrieved June 12th ,2012 from http://asianbondsonline.adb.org

US Japan

China Thailand

Statement of Problem• Credit Spread Puzzle is unsolved -> common systematic

risk still cannot be found. • Credit Cycle -> Persistent Effect of Credit spread, using

macroeconomic variable to model credit cycle is not accurate enough.

• Applied Markov Switching Regime on the credit spread can enhance explanatory power in U.S. economy.

Globally

• Increasing size and data transparency of outstanding bond in Thai Market

• Lack of Research on aggregate level and dynamic change of spread -> bond valuation

• Credit Spread is dynamic in Thailand, and it shows different regimes, high and low credit spread means and volatilities.

Locally

5THE DETERMINANTS OF CREDIT SPREAD CHANGES OF INVESTMENT GRADE CORPORATE


SWITCHING MODEL

Research Questions

• Is the credit spread change affected by the change of interest rate level? • Is the credit spread change affected by the change of slope of term-structure? • Is the credit spread change affected by the volatility of interest rate?

Interest rate

• Is the credit spread change affected by the return of equity market? • Is the credit spread change affected by the change of historical volatility of equity market

return?

Macroeconomic

• Is the credit spread change affected by the change of missing price ratio?• Is the credit spread change affected by the change of turnover ratio?

Liquidity

Controlled credit ratings and time-to-maturity

In general, what are the determinants of credit spread change in Thailand?

What are the determinants of credit spread change in Thailand in low and high regimes of credit cycle?



SWITCHING MODEL

6

Dependent Variable

Credit Spread

Yield spread (a difference) between yield of corporate bonds and government bonds. There are three types of credit spreads, i.e. Nominal, Option-adjusted, and Static.

Credit Default Risk

Risk that firm cannot pay back

cash flow as promised

Default Risk

Default probability,

Recovery Rate

Credit Spread Risk

Risk from the credit spread change

Interest Risk

Firm Value Correlation

Expectation of Future

Interest Rate

Macroeconomic Risk

Business Cycle affecting Firm

Earning and Value

Liquidity Risk

Cash Quickness or Transaction

Cost

Seasonal Effect

Embedded Option Risk

Convertible, callable,

income bonds

Other Risks

e.g. Maturity, Tax, reinvestment,

prepayment, inflation, exchange rate

7THE DETERMINANTS OF CREDIT SPREAD CHANGES OF INVESTMENT GRADE CORPORATE


SWITCHING MODEL

Theories

Default Risk

Z-Score (Altman, 1968)

Credit Risk Models (Option Pricing

Theory)

Structural Model (Merton, 1974)

Reduced Form Model (Jarrow and

Turnbull, 1995)

Interest Rate Risk

Option Pricing Theory

Term Structure Theory

Pure Expectation Theory

Liquidation Theory

Preferred-Habitat Theory

Market-Segmentation

Theory

Macroeconomic Risk

Business Cycle Theory (Chen,

2010)

Efficient Market Theory (Fama

1970)

Liquidity Risk Liquidity Theory

Embedded-Option Risk

Hedging Interest Rate Risk Theory

Financial indicator to default Z score, which related to default event

Leverage ratio identifies the default probability using firm data as in Option Pricing Model (Black and Scholes, 1973)

Assumed default probability and recovery rate are used in determin-ing bond value using Binomial Tree

Increase in interest rate increases the risk-neutral process of firm value (Longstaff and Schwartz, 1995)

Future interest rate can be expected by forward rate, the term structure is upward or downward.

At the same maturity, asset and liability constraints create imbalance of supply and demand, can be all shapes

The curve comes from shifting between long and short from supply and demand, can be all shapes

The longer maturity, the higher the risk, therefore yield is higher. Only upward slope

The recovery rate decreases during recession due to the lower earning. Credit spread rises

Information flow to the lower transaction cost market, i.e. stock market, then flows to the bond market. Therefore there is a correlation between two markets.

Less trading securities required risk premium to compensate the hedging activities (Lo, Mamaysky and Wang, 2004)

Callable bond yield is higher than non-callable bond yield due to the repayment feature during fall of interest rate.

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Theories

Independent Variables

Default Risk Factors

Leverage Ratio

Firm Value Volatility• Historical• Implied• GARCH

Recovery Rate

Interest Rate Risk

Level

Slope

Volatility

Macroeconomic

Inflation-Series• CPI, PPI, PCE, GDP

Output-Series• GDP, MPI, PCE

Financial Market-Series• Equity market return, volatility, VIX, FF factors,

Liquidity

Traditional• Age

Price and Volume Impact• Price and Yield volatility• Amihud Illiquidity• Range• Bid-Ask Spread• Trading Frequency• Missing Price

Market Participant• Yield Dispersion• Latent Liquidity

Market Liquidity• Credit Default Swap Spread• Macroeconomic Factor Related• Number of Contributors

9

Conceptual Framework



SWITCHING MODEL

10

In general, what are the determinants of credit spread change in Thailand?

What are the determinants of credit spread change in Thailand in low and high regimes of credit cycle?

Credit Spread Change

Interest Rate Factor

•Interest Rate Level

•Slope of Term Structure

•Historical volatility of Interest Rate

Macroeconomic Factors

•Equity Market Return

•Historical Equity Market Return Volatility Liquidity Factors

•Missing Price

•Turnover Ratio

Simulation

Calibration technique: GMM,

MLF, KMV

Structural Model

(Merton, 1973)

Modified:

Black and Cox (1976), Leland and Toft (1996),

Collin-Dufresne and Goldstein (2001), Duffie and Lando

(2001), Zhou (2001), Jarrow and Protter

(2004), Giesecke(2006), Chen et al.

(2008), Chen and Kou (2009)

Reduce Form Model

(Jarrow & Turnbull,

1995)

Modified:

Lessig and Stock (1998), Elton et

al. (2001), Longstaff et al.

(2005)

Multiple Linear Regression

Single Regime

Collinearity

Correlation Matrix

Corr(Xn, Xm) > 0.8 or

< -0.8

Drop/do nothing

Serial Correlation

ADF test

First Difference

Functional Form

Nonlinear Term to have a linear

relationship

interaction terms

Multiple Regime

Markov Regime

Switching model

Unobservable states

Threshold Autoregre

ssive model

Observable state from

macroeconomic

indicators

Conceptual Framework Single and Multiple-Regime Models

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This part is the explanatory variables with interaction term (dummy variable from Regime Switching Model)

When the interaction term = 0, the credit spread in the low regime can be explained as in this equation.

When the interaction term = 1, the credit spread in the high regime can be explained as in this equation.

This is a simple multiple regression of interest rate factors, macroeconomic factors, and liquidity factor, when holding credit rating and time-to-maturity constant (as in port.)

Single Regime

MultipleRegime

LowRegime

HighRegime

To find the unknown parameters, including two means and two variance and a transition matrix, the maximum likelihood using Hamilton’s filter is applied. •The Bayesian priors, alpha, beta, and nu, which is calculated within the iterative algorithms (Hamilton, 1990) to find the parameters. During the iteration, the smooth probabilities can be obtained to weight the CS(j,t)•The smooth probability from the function indicates the state of CS(j,t), whenever it is more than 0.5, the credit spread is in high regime, and vice versa•The process is calculated from the MS_Regresspackage in MATLAB.

Conceptual FrameworkInteraction terms – Markov Switching Model

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For each port. j, the CS(j,t) can be explained by two means and two variances, from the low and high regimes, St

A Transition Matrix, the probability to switch between high and low regime is assumed constant, e.g. p12 is the probability of a switch from state 2 to state 1 between time t and t+1

Sample Design & Data Sources Credit Spread

13

Corporate Bond data from ThaiBMA

(-) embedded option and floating rate bonds

(-) time-to-maturity less than one year

(-) no credit rating or credit rating less than

BBB

Individual Static Credit Spread of bond i on month t with credit rating and time-to-

maturi

Filtering port. Forming Constructing Credit Spread Curve

Port (j)

TTM

<3Y

3-5Y

>5Y

Credit Ratings

AAA

1

2

3

AA

4

5

6

A

7

8

9

BBB

10

11

12

CS(i,t) and TTM of each port.

OLS lognormal function

= a0, a1

CS(j,t) of each port. j and month t

Static Spread is calculated from the cash flow and price of corporate bond i at time t from this equation.

The credit rating port. represents group of credit spread at time t, e.g. AAA is a group of corporate bond with credit rating AAA, AAA(-), and AA is AA(+), AA, AA(-)

Sample Design & Data SourcesIndependent Variables

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Interest Rate

Interest Rate Level

Yield of two-year

Treasury yield

Zero coupon yield curve

from ThaiBMA

Slope of Interest Rate

Spread of 10-year and

2-year Treasury

bond

Zero coupon yield curve

from ThaiBMA

Interest Rate

Volatility

One-year historical

interest rate volatility

Daily spot rate of ten-year

Treasury bond yield from ThaiBMA

Macroeconomic

Equity Market Return

Monthly SET index

return

Bloomberg database

Historical Volatility of

Equity Market Return

180 days historical

volatility of SET index

Bloomberg database

Liquidity

Missing Price

Days in the month minus the number

of trading days with in the month

over the number of days in the month

Last trading day from

ThaiBMA’smark-to-market

corporate data

Turnover Ratio

Total trading volume in the month over outstanding

bonds

ThaiBMAWebsite

Interaction Term

Smoothed probability

Markov Switching Algorithm

MS_REGRESS

Preliminary ResultsSelecting Credit Spread and Portfolio Setting

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1-3 yr63%

3-5 yr29%

5-7 yr8%

>7 yr0%

Total Issues by TTM

AAA17%

AA17%

A52%

BBB14%

Total Issues by Credit Rating

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Preliminary ResultsConstructing Credit Spread Curve and Aggregate Credit Spread

Linear regression to construct Credit Spread Curve

Parameters from Credit Spread Curveare used to extrapolate the aggregateCredit spread

Parameters from Markov Switching Model

Preliminary ResultsDetecting Credit Cycle and Constructing of Interaction Terms



SWITCHING MODEL

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Smooth Probability Interaction Terms



SWITCHING MODEL

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Descriptive Statistics

Correlation Matrix

Unit Root Test

Preliminary ResultsUnit Root Test, Correlation Matrix, and Descriptive Statistics

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Empirical ResultsSingle Regime Models

Interest Rate change is negatively related with credit spread change in most of portfolio, except fo low rating group.

Slope of Term Structure only has positive effect on credit spread change in AAA group. Sign not as expected. Volatility of Interest rate has no effect.

Equity Market Return has strong negative relationship with credit spread, while its volatility does not have a significant relationship.

Market Liquidity has negative effect on credit spread of AA and A rating groups, while individual liquidity has a positive relationship with credit spread in AAA group. Credit spread change in BBB has no relationship with liquidity.



SWITCHING MODEL

Empirical ResultsMultiple Regime Models (low regime)



SWITCHING MODEL

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Interest Rate change isnegatively related with creditspread change in mosttrading groups, i.e. AA andA. Other interest ratevariables do not have effecton credit spread change.

Macroeconomic factorsdo not have effect oncredit spread in lowregime

Individual Liquidity factors hasa positive effect on creditspread change in most tradinggroup, while market liquidityhas a positive effect on creditspread change in AAA ratinggroup.

In low regime, nofactor can explainchange of creditspread of BBB

Empirical Results Multiple Regime Models (marginal effect)

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Interest Rate changeis negatively relatedwith credit spreadchange in most ofportfolio, except folow rating group.

Slope of Term Structure only haspositive effect on credit spread changein most portfolio, except for mostrating group A. Interest Rate volatilitydoes not have relationship with creditspread change.

Equity Market Return hasstrong negative relationshipwith credit spread. The equitymarket volatility has a positiveeffect on the credit spread inmost trading group.

Market liquidity can explaincredit spread in most tradinggroup, A., while other liquidityfactor cannot explain creditspread change.

Adjusted R squares of multiple regime models are higherthan single regime models. AICs of multiple regime modelsare all lower then single regime models.

Empirical ResultsSummary Signs of Independent Variables

• There are evidences in sign switches in low and high regimes.

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Summary of Findings and Discussion of ResultsSingle-Regime Model



SWITCHING MODEL

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• Explanatory power <45%, the low credit rating has the least explanatory power with systematic factors.

• Interest Rate Factors

– Level, Negative relationship

– Slope, only AAA has positive relationship, Volatility, no relationship.

• Macroeconomic Factors

– Two-month-lag Equity return, Positive relationship

– Equity return volatility, no relationship, contradicted to previous study

• Liquidity Factors

– Market liquidity, Negative relationship, only high trading portfolio

– Portfolio liquidity, Positive relationship, only AAA

Summary of Findings and Discussion of ResultsMultiple-Regime Model



SWITCHING MODEL

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• Explanatory power 61 to 83% with less AIC - more effective than Single-Regime Model

• Low Regime

– BBB group cannot be explained by systematic risk factors.

– Change in interest rate level can explained credit spread of diversified portfolio.

– Equity market return and volatility cannot explained credit spread in low regime. Bond and Equity market are not correlated during low regime.

– Local liquidity factors can explain credit spread for diversified portfolio, while market liquidity factor can explain AAA portfolio.

Summary of Findings and Discussion of ResultsMultiple-Regime Model



SWITCHING MODEL

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• Marginal effect– Not only the level, but also the slope of term-structure can explained credit

spread. The sign of the slope is positive, which is contradicted to the theory. However, in the high-regime, the credit spread fluctuates greatly due to the economic disturbance. Increase in future interest rate becomes a bad signal of economic.

– Equity market return is negatively related with the credit spread as in single-regime model. The volatility of equity market return is not statistically significant.

– Market liquidity is important factor for most trading portfolio.

Implications and Recommendations



SWITCHING MODEL

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• Academically, multiple-regime model can help explaining credit spread puzzle.

• Individual investors should aware that the systematic risk factors affect change of credit spread differently in low and high regime.

• Bond issuers can set IPO price using current market rate and trend of systematic risk factors.

• Regulators should aware of changing the interest rate level and expectation of the interest rate, due to the fact that in high-regime the bond market is sensitive to the interest rate factors.

• Liquidity can reduce the transaction cost of the bond and high liquidity can reduce credit spread. High liquidity can increase NAV of portfolio.

Future Studies



SWITCHING MODEL

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• If more information is available, it is interesting to test the individual risk factors with credit spread change.

• More complex switching models, such as SETAR, PSTAR, or LSTAR

• Choices of macroeconomic indicators that are suitable for credit spread according to Wu and Zhang (2008)

• Other liquidity proxies, Acharya, Amihud & Bharath (2010)

• Extended the study to other kinds of corporate bonds, i.e. floating rate bonds, embedded option bonds, or commercial papers.

THANK YOU VERY MUCH FOR YOUR ATTENTION

QUESTIONS AND ANSWERS

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