European asset swap spreads and the credit crisis

We examine time-varying behaviour and determinants of asset swap (ASW) spreads for 23 iBoxx European corporate bond indexes from January 2006 to January 2009. The results of a Markov switching model suggest that ASW spreads exhibit regime-dependent behaviour. The evidence is particularly strong for Financial and Corporates Subordinated indexes. Stock market volatility determines ASW spread changes in turbulent periods, whereas stock returns tend to affect spread changes in calm periods. While market liquidity affects spreads only in turbulent regimes the level of interest rates is an important determinant of spread changes in both regimes. Finally, we identify stock returns, lagged ASW spread levels, and lagged volatility of ASW spreads as major drivers of the regime shifts. The results are robust in the extended sample (January 2006 to October 2013) that includes a post-crisis period.


Introduction
An asset swap (ASW) is a synthetic position that combines a fixed rate bond with a fixed-tofloating interest rate swap. 1 The bondholder effectively transforms the payoff, where she pays the fixed rate and receives the floating rate consisting of LIBOR (or EURIBOR) plus the ASW spread. In case of a default the owner of the bond receives the recovery value and still has to honor the interest rate swap. The ASW spreads is a compensation for the default risk and corresponds to the difference between the floating part of an asset swap and the LIBOR (or EURIBOR) rate. Corporate bonds are always quoted with their ASW spreads and their pricing is based on the spreads. ASWs are very liquid and could be traded separately, even easier than underlying defautable bonds (Schonbucher, 2003). ASW spreads are, therefore, a bond specific measure of credit risk implied in bond prices and yields.
ASWs are closely associated with credit derivatives such as credit default swaps (CDS). 2 For example, asset-swapped fixed-rate bonds financed in the repo market are comparable to CDS contracts (Francis et al., 2003). ASW usually trade in a close range (see Norden andWeber, 2009, andZhu, 2004) and tend to be cointegrated with CDS (De Wit, 2006). 3 More recently, however, CDS trading dropped significantly whilst issuance of corporate bonds and the liquidity of the ASW market increased to record levels. 4 The popularity of corporate bonds is associated with higher returns in bond and ASW markets, and bearish equity markets. Furthermore, compared to CDS, the ASW market attracts more diverse investors and is easier to access due to their significantly smaller contract size (IFSL, 2009a;Dotz, 2007). 5 1 In the US, ASW are better known as Bond Total Return Swaps (TRS) or Bond Total Rate of Return Swaps (TROR). 2 CDS are essentially insurance contracts where buyers agree to pay a predefined periodic fee (i.e. CDS spread) while the sellers provide compensation in case of a default. 3 Theoretically, the difference between CDS and ASW spread (i.e. basis) is expected to be close to 0. In practice, however, the prices are different due to the impact of supply and demand and the fact that ASW spreads also reflect funding costs (see Chaudry, 2004). Other drivers of basis are related to CDS counterparty risk, 'soft' credit events, and inclusion of CDT options in CDS contracts (for more see Francis et al., 2003;and Blanco et al., 2005). 4 Notional amount outstanding in CDS market dropped from $62 trillion, at the end of 2007, to $38 trillion at the beginning of 2008 (IFSL, 2009a andISDA, 2009). Centralised clearing and voluntary termination of contracts were important contributors to the sharp drop in the liquidity of CDS market. At the same time issuance of investment grade bonds in European market has increased almost three fold, reaching E140bn mark at the beginning of 2009 (IFSL 2009b). 5 For example, the standard CDS notional amount is 2,000 times higher (for high-yield debt) then the standard corporate bond's face value of €1,000. Consequently, CDS market was dominated by large and highly leveraged market players (Dotz, 2007).
Recent empirical evidence suggest that the changes in liquidity play an important role in price discovery and accuracy of alternative credit risk measures. Mayordomo et al. (2011), for example, show that ASW is more accurate measure of credit risk than CDS during illiquid periods. ASW spreads are a also expected to be superior measure of default risk than corporate bond credit spreads (Mayordomo et al. 2011;De Wit, 2006;Francis et al. 2003). For example, corporate bond credit spreads are affected by different tax treatment and liquidity of corporate and treasury bonds, differences in corporate bonds' contractual agreements (e.g. embedded options) and maturities (Elton et al., 2001;Cao et al., 2010).
Whilst previous studies examine determinants of credit spreads inferred from CDS indexes (Byström, 2005;Alexander and Kaeck, 2008), single name CDS spreads (Yu, 2005;Benkert, 2004;Erricson et al., 2004;Cossin et al., 2002;Hull et al., 2004)), individual corporate bonds (Collin-Dufresne et al., 2001;Tsuji, 2005), and bond portfolios/indexes (Pedrosa and Roll, 1998;Brown, 2000), ours is the first paper to examine determinants of ASW spreads inferred from European iBoxx corporate bond indexes. Most related to our work is the study of Alexander and Kaeck (2008) who examine determinants of iTraxx Europe CDS indexes during June 2004 to June 2007. We extend their model for determinants of credit spreads by considering market liquidity. The consideration of market liquidity is motivated by recent evidence that price discovery process in credit markets tend to be affected by market illiquidity (Mayordomo et al., 2011). We also contribut to the literature by examining determinants of ASW spreads for 10 industry and 13 composite iBoxx indexes stratified by their credit rating and seniority, in different market regimes. The examination of credit spreads in different market regimes is particularly important given differences in importance of various factors (global, industry, and country specific) affecting default probabilities in different market regimes (Aretz and Pope, 2012). This examination is also important in the light of recent regulatory changes which focused on CDS markets (BIS, 2003;ECB, 2004) and payied very little attention to fast growing ASW market.
Our main findings are: (i) ASW spreads behave differently during periods of financial turmoil, with a residual volatility which is up to eight times higher compared to calm periods; (ii) structural determinants explain ASW spreads better for financial sector companies than for the remaining industry sectors; (iii) we find little evidence of regime switching in noncyclical industry sectors (e.g. Utility, Chemicals, Telecoms); (iv) the financial sector shows a high degree of autocorrelation in ASW spreads, which is mostly negative in calm but highly positive in turbulent market periods; (v) stock market volatility determines ASW spreads mainly in turbulent periods whereas stock returns are more important in periods of lower volatility; (vi) interest rates are an important determinant in both market regimes; (vii) the liquidity premium, defined as the difference between the swap and the government bond yield curve tends to be relevant only in turbulent regimes; (viii) raising stock market returns and interest rates tend to reduce the probability of entering the volatile regime; (ix) our Markov switching model exhibits better out of sample accuracy than the equivalent OLS model for determinants of ASW spreads.
The remainder of this paper is organized as follows: Section two motivates our hypotheses.
Section three describes data and methodology. In section four we present results of our Markov switching models together with an analysis of main drivers of the regime switching. This is followed by various robustness checks performed in section five. Finally section six sums up and concludes.

Literature and hypotheses
The pricing of credit risk has evolved in two main approaches. First, reduced form models treat default as an unpredictable event, where the time of default is specified as a stochastic jump process. 6 Second, structural models build on Merton (1974) and use market and company fundamentals. Since structural models offer an economically intuitive framework to the pricing of credit risk, a large body of empirical literature has grown testing theoretical determinants of credit spreads with market data. 7 For example, the risk-free interest rate is expected to be negatively related to default risk. Higher risk-free rates increase the risk-neutral drift and lower the probability of default (Merton, 1974). The lower probability of default narrows the credit spread and leads to a negative association of interest rates and credit spreads (Longstaff and Schwartz, 1995). Another argument supporting the inverse relationship between interest rates and credit spreads refers to the business cycle. For example, in periods of economic recessions interest rates tend to be lower and corporate defaults tend to occur more often. swap interest rates are not completely free of risk they are often regarded as a better benchmark for the risk-free rate than government yields (Houweling and Vorst, 2005). For example, they do not suffer from temporary pikes sometimes caused by characteristics of repo agreements involving government bonds. Furthermore, swaps have no short sale constraints, are less influenced by regulatory or taxation issues, and tend not to be affected by scarcity premiums in times of shrinking budget austerity. Finally, swap rates closely correspond to the funding costs of market participants (see Houweling andVorst, 2005, andHull et al., 2004). Overall, we expect a negative association between ASW spreads and swap interest rates.
Another key variable in the structural framework is the leverage ratio, defined as the ratio of a firm's debt to its firm value. When the ratio approaches unity a default is likely to be triggered. Hence, a lower firm value (and therefore a lower equity value) increases the probability of default. Similarly, an increase in firm value volatility increases the probability of default, and, therefore, increases the credit spread. Since the firm value and its volatility are typically not directly observable we proxy for these two variables by stock market returns and implied volatility of traded stock options. Similar proxies were used in the previous literature (Huang and Kong, 2003;Alexander and Kaeck, 2008;Aretz and Pope, 2012). Aretz and Pope (2012), for example, report that equity returns efficiently capture changes in default risk. The use of the implied volatility of traded stock options is justified by the positive association between the volatility of the firm and equity volatility. Similarly, the higher stock market returns imply higher firm values and a lower probability of default. Thus, we expect a negative association between ASW spreads and stock market returns, and a positive association between ASW spreads and the stock market volatility.
A further possible determinant of credit spreads is the difference between the swap interest rate and the interest rate on a par value government bond of the same maturity, known as the swap spread (Duffie and Singleton, 1999;Liu et al, 2006). Feldhütter and Lando (2008) decomposed the swap spread into a credit risk element, a convenience premium and idiosyncratic risk factors. They concluded that the major determinant of swap spreads was the convenience yield defined as investors' willingness to pay a premium for the liquidity of government bonds. The importance of the convenience yield is especially apparent in unsettled markets when investors' concerns about liquidity and changes in markets' perception of risk result in 'flight to quality' (Longstaff, 2004). In such an environment government bond yields usually fall more than those of other credit securities, which further leads to an increase in the swap spread.
Empirical evidence for the association of swap spreads and credit spreads is provided for several markets. For example, Brown et al. (2002) report a significant positive relationship between swap and credit spreads in the Australian market. Kobor et al. (2005) find a positive long-term relationship between swap spreads and credit spreads for US AA-rated bonds with maturities of two, five and ten years. Finally, Schlecker (2009) documents a cointegration relationship of credit spreads with swap spreads for the US as well as the European corporate bond markets. We, therefore, expect a positive association of ASW spreads, based on European iBoxx corporate bond indexes, and swap spreads. are highly leptokurtic for all sectors. The skewness of spreads is generally positive, with extreme values for Banks, Tier 1 Capital and AAA-rated corporate bonds. 9 These three sectors exhibit the highest level of (positive) skewness and excess-kurtosis. The evolution of ASW spreads of the iBoxx Corporate Bond indexes and its determinants during the sample period is illustrated in Figure 2. The stock market was increasing steadily until summer of 2007. In the following 18 months, however, the European markets lost more 8 Given that most liquid CDS spreads have 5-year maturity we can compare our results directly to the results reported in previous studies based on CDS spreads (e.g. Alexander and Kaeck, 2008). 9 It is worth mentioning that the Corporates AAA index contains only one non-financial bond (issued by health care company Johnson & Johnson). The remaining 35 bonds in this index represent debt raised by highly rated financial institutions. Tier 1 Capital consists of the most subordinated bonds issued by banks. than half of its value. The level of interest rates peaked in the summer of 2008. Since then the interest rates were declining until the end of our sample period. Volatility, swap spreads, as well as ASW spreads of the Corporate Composite bond index were relatively moderate until June 2007. Thereafter they all were increasing sharply with a notable jump in September 2008. Figure 2 about here ***

Markov switching model
The reported leptokurtic distribution of our sample ASW spreads together with time-varying properties of the parameters call for consideration of non-linearity and regime shifts. Markov models provide an intuitive way to model structural breaks and regime shifts in the data generating process. Such models can be linear in each regime, but due to the stochastic nature of the regime shifts nonlinear dynamics are incorporated. The models define different regimes allowing for dynamic shifts of economic variables at any given point in time conditional on an unobservable state variable, . 10 Another advantage of using a latent variable is the constantly updated estimate of the conditional state probability of being in a particular state at a certain point in time. In our specification the state parameter is assumed to follow a firstorder, two-state Markov chain where the transition probabilities are assumed to be constant.
The dependent variable, ΔASW , , is the change (rather than level) in the ASW spread of industry sector k on day . 12 , , is a matrix of regression coefficients as used in model of the k th sector, which are dependent on the state parameter . ΔASW , −1 is the one period lagged ASW spread change. the inclusion of lagged spread changes (ΔASW , −1 ) as control variable is motivated by both previous studies and properties of our sample. 13 Equity values (ΔStock return , ) are proxied by respective Dow Jones (DJ) Euro Stoxx indexes which are also provided by Markit (see Table 1). 14 VStoxx index (ΔVStoxx ) is as a proxy for the implied volatility, since it is the reference measure for the volatility in European markets. The use of implied rather than historical volatility is further justified by the results of previous empirical studies on credit spreads. 15 The change in the level of interest rates is estimated by Principal Component Analysis (PCA) using the European swap rates with maturities between one and ten years. 16 The consideration of the dynamics of the complete swap rate term structure, instead of using arbitrarily chosen maturities, is our further contribution to the literature. In the PCA context, swap rate maturities represent key liquidity points. The PCA uses historical shifts in the swap rates to compute the correlation matrix of the shifts. The matrix is then used to compute eigenvectors and eigenvalues. The computed eigenvalues are in fact weights, which tell us the relative importance of the level and slope shifts. The first eigenvector corresponds to a level and the second to a slope of the swap rate curve shift. The resulting first principal component of our analysis (ΔIR_Level ), therefore, reveals the changes in the level of the entire swap rate curve.
The swap spread (ΔSwap Spread ), as a proxy for bond market liquidity, is measured as the difference between the five year European swap interest rate and the yield of German gov-12 Collin-Dufresne et al. (2001) and Alexander and Kaeck (2008) also examine credit spread changes. Studies that do not examine time series variation in spreads and their determinants use credit spread levels as dependent variables in respective models (see Tsuji, 2005;Cremers et al. 2008;Zhang et al. 2009;Cao et al. 2010). Models for levels tend to provide higher explanatory power measured by R 2 . For example, Zhang et al. (2009) report R 2 s up to 73% in models for levels compared to R 2 s up to 5.4% in respective models for changes in CDS spreads. 13 For example, Byström (2006) and Alexander and Kaeck (2008) report a high degree of autocorrelation in daily changes of CDS iTraxx index spreads, for all industry sectors. Our unreported results suggest that 15 of the 23 sample ASW spreads exhibit a highly significant degree of autocorrelation with mixed signs. 14 The only exception is the equity value proxy for non-financials where the FTSE World Europe ex Financials stock index was used, as Markit does not provide relevant index. 15 Cao et al. (2010) find that stock option implied volatilities explain CDS spreads better than historical volatilities. Similarly, Cremers et al. (2008) show that implied volatilities improve on historical volatilities when explaining variations of corporate bond spreads. 16 Principal component analysis is originally developed by Spearman (1904). It is a non-parametric method that helps to reveal the underlying variance driving structure of a panel of data and extracts the most important uncorrelated sources of information. ernment bonds of the same maturity. 17 Finally, , , is a vector of disturbance terms, assumed to be normal with state-dependent variance , , 2 .

Determinants of ASW spreads in different market regimes
Results of the Markov switching regressions are provided in Table 2. The residual volatility (Std. Dev.) is higher during turbulent than during calm market periods for all sample sectors.
On average, the residual volatility is 5.4 times higher during the turbulent periods, ranging from five (e.g. Chemicals, Utilities, Telecommunications) to seven (Tier 1 Capital) times. The AAA-rated Corporates, as well as for finance related indexes. Overall, the results suggests that credit spreads tend to be more affected by stock market returns during calm periods while in turbulent periods stock market volatility becomes a more important determinant of credit spreads.

*** Insert
The interest rate level (ΔIR_Level ) affects ASW spreads negatively in both regimes. 20 Table   2 also reveals larger negative coefficients for interest rate levels (ΔIR_Level ) in turbulent compared to calm regimes. Thus, decreasing interest rates in turbulent periods tend to increase spreads more than in calm periods. This result contradicts findings for CDS spreads reported by Alexander and Kaeck (2008) who report negative and statistically significant relation between interest rates and credit spreads only during calm periods. In addition, they report lack of statistically significant relation between interest rates and credit spreads for finan- 18 Our results are in line with Alexander and Kaeck (2008), who report similar results for changes in CDS spread indexes. 19 It is worth noting that for the above mentioned indexes we report a positive association between volatility and credit spreads during turbulent periods. 20 ΔIR − Level affects ASW spreads negatively in 45 out of 46 cases. In 31 of the 45 cases the effect is statistically significant at the 5% level, or better.

Regime specific moments of ASW spread
Regime specific moments of ASW spread changes (ΔASW , ) are presented in Table 3. The first column of Table 3  basis points, the average skewness is 0.87, and the average excess kurtosis is 2.29 (for Corporate Composite index). Notable, the distribution of ASW spread changes of AAA-rated Corporates and Banks is highly leptokurtic with an excess kurtosis of 6.75 and 13.2, respectively, whereas the excess kurtosis for Retail sector is the lowest in the sample. Table 3 about here ***

*** Insert
Overall, our findings confirm that ASW spread changes deviate much more from normal distribution in the turbulent regime and that the recent credit crisis affected financial more than any other industry sector.

Regime probabilities and ASW spread volatility
We further examine consistency of estimated regime probabilities and the volatility of ASW spread changes (ΔASW , ) 2 . We expect a positive relation between volatility and estimated probabilities of entering into a turbulent period. Furthermore, we expect that the estimated probabilities relate to dates of major events during our sample period. We therefore plot the major events together with estimated probabilities and ASW spread changes (see Figure 3).
The selected events are: (1) first reports on a sharp drop in US house prices, (2) the Ameriquest crisis, (3) financial markets rallied to a five year high, (4) the credit markets crisis, (5) LIBOR rose to 6.79%; (6) the collapse of Bear Stearns, (7)

Determinants of regime changes
To statistically test variables that induce a regime shift, we estimate a logit model relating the estimated state probability of being in either of the regimes to structural variables. The dependent variable is, therefore, equal to one if the estimated probability from model (1) is higher than 0.5 (indicating a high volatility -turbulent regime) and equal to zero if the estimated probability value is equal to or lower than 0.5 (indicating a low volatility -calm regime). The explanatory variables are the same structural variables as in model (1), with an addition of the squared change of lagged ASW spreads (∆ASW t−1 2 ). Given that volatility of ASW spreads is expected to be high during turbulent regimes (i.e. when volatility of residuals is high) it is important to examine the causality between regime changes and the volatility of ASW spreads (proxied by ∆ASW t−1 2 ). The model, thus, has the following form: 23 Where [ = 1] denotes the filtered probability of being in the high volatile regime at time and 0 and 1 represent regression coefficients. Various models are estimated using only one lagged explanatory variable −1 at a time.
The ∆ASW t−1 2 column in Table 4 reveals that large changes in the volatility of credit spreads, irrespective of the direction, lead to a shift in market regimes. 24 The coefficients are statistically significant at the 5% or better in 18 (of 23) regressions. Results presented in the second column in Table 4 show that lagged changes of credit spreads (ΔASW −1 ) have a significant and positive influence on the regime probability (the coefficients are statistically significant at the 5% or better in 21 (of 23) regressions). As expected, stock market returns have a negative sign in all sectors (statistically significant in 8 cases), indicating that positive daily market returns reduce the probability of switching to the high volatility regime. In contrast, lagged changes in volatility (ΔVStoxx t-1 ) do not seem to have any influence on the switching behavior. The level of interest rates (ΔIR_Level), on the other hand, is negatively associated with credit spreads in all sectors (but statistically significant only in 3 cases). The coefficients for the lagged swap spreads are not statistically significant.

Robustness checks
In this section we conduct further analysis and examine the robustness of our findings. First, we test for the equality of coefficients in our Markov model in different market regimes. Second, we test-down our Markov model by excluding all explanatory variables which were not statistically significant. Third, we conduct in and out-of-sample tests for accuracy of our model's predictions.

Equality of coefficients in different market regimes
Engel and Hamilton (1990) suggest a classical log likelihood ratio test with the null hypothesis ( 0 ) of no switching in the coefficients ( =1 and =2 ) but allow for switching in the residual variance ( =1 and =2 ). 25 Thus we test the following hypothesis: The corresponding results are reported in Table 5. We further conduct a test for switching in each explanatory variable of model 1 (see Table 6).  Table 6 about here ***

Tested-down Markov model
The results for tested-down Markov models are presented in Table 7 Table 7 about here *** 28 Automobiles & Parts and Chemicals at the 10% significance level. Personal & Household Goods and Utility at the 5% significance level. 29 For Health Care interest rates are statistically significant only in turbulent period whilst for Retail only in calm period. 30 The different results could be related to residual interest rate and funding risk associated with ASW but not with CDS spreads.

In-sample accuracy test of the Markov switching model
We assess the contribution of our Markov model to the in-sample accuracy of estimation by comparing the results of the Markov model with the results of an OLS model that uses the same explanatory variables. First, we use the Markov and the OLS models to predict changes in ASW spreads. The predictions for the Markov model are based on the estimated parameters (reported in Table 2) for calm and turbulent regimes. The turbulent and calm regimes were defined using probabilities estimated by our Markov model. Observations with the estimated probabilities above 0.5 were included in the turbulent regime. The predictions for the OLS model are based on the estimated parameters for the entire sample period. The predictions for the two regimes are, therefore, based on the same OLS parameters. Second, we regress the actual changes of the sample ASW spreads against the predicted changes obtained by the respective models. We therefore have two regressions for each of the regimes. Intercepts close to 0 and the slope coefficients close to 1 are an indication of a better model accuracy.
The results for selected industry sectors are presented in Table 8. 31 In the turbulent regime, Oil and Gas and Telecommunication sectors have the highest R 2 and F statistics. The hypothesis that the coefficient slope equals to 1 cannot be rejected in OLS regressions for Oil and Gas and Markov regressions for Oil and Gas and Telecommunication sectors. The hypothesis that the intercept is equal to 0 cannot be rejected only in regressions for Oil and Gas sector.
The models, therefore, work particularly well for Oil and Gas sector.

*** Insert Table 8 about here ***
In the calm regime, the hypothesis that the slope coefficient equals to 1 has to be rejected for all sectors. Notably, the t-statistics for the slope coefficients in the calm period are much higher compared to the turbulent regime. The hypothesis that the intercept term equals to 0 has to be rejected only in Retail (OLS model) and Banking (OLS and Markov models) sectors. Overall, the results from Table 8 show a marginal improvement in predictive power when using the Markov switching model. 31 For brevity we present the results for five sectors. The results for other sectors are available upon request.

Out of sample accuracy test of the Markov switching model
The predictions for the out of sample test are based on our Markov model (equation 1) for the two regimes and an equivalent OLS model using a rolling window of 500 (past) daily observations. The first estimation window starts on January 6 th , 2006 and ends on December 18 th , 2007 (500 observation). The out-of-sample period contains 278 observations (trading days), from December 19 th , 2007 until January 29 th , 2009. We than use the predictions to test the null hypothesis that the mean difference between actual and predicted changes in ASW spreads are zero in different regimes. 32 The results are presented in Table 9. Table 9 about here *** In the calm regime, the difference between average (mean) actual and predicted ASW spread changes is not statistically significant across selected sectors and for both models. In the turbulent regime, the (absolute) mean difference between actual and predicted ASW spread changes is smaller for the Markov model compared to the OLS model in all sectors, depart from Oil & Gas. Thus, the Markov model estimates are (in most cases) closer to the actual ASW spread changes. When the OLS model is used the mean difference between actual and predicted ASW spread changes is statistically significant for Banking, Telecommunication, and the Composite sectors. In contrast, when the Markov model is used for predictions, the corresponding differences are not statistically significant in any of the sectors. Overall, the Markov model exhibits better out of sample accuracy compared to the equivalent OLS model for determinants of ASW spreads.

Conclusion
In this study we examine the time-series dynamic of credit risk based on ASW spread data for a set of 23 European iBoxx Corporate Bond indexes during the period from January, 1 st 2006 to January, 30 th 2009. Our results suggest a leptokurtic distribution for the sample ASW spreads characterized by huge excess kurtosis. To allow for dynamic shifts in the data generating process, we employ a two-state Markov model. The corresponding results reveal that the estimated coefficients differ considerably between the two regimes. For example, stock market returns are negative and in most cases significantly associated with ASW spreads in calm periods. This result also holds in turbulent periods but to a lesser extent. The stock market volatility has a positive effect on ASW spreads in turbulent periods, whereas the opposite is true in calm periods. As predicted, a higher swap spread, which can be considered as a quality premium required for non-government bonds demands larger ASW spreads. However, this only holds in turbulent regimes. In calm periods, the relationship is not statistically significant. Independent of the regime, the level of interest rates is clearly negatively related to credit risk. The lower interest rates, therefore, lead to an increase in ASW spreads.
Our findings suggest significant differences in the importance of stock market returns, volatility, and interest rates for explaining ASW spreads from various industries. This result is surprising since theory predicts that all credit spreads should be affected those variables (Collin-Dufresne 2001) and empirical evidence document considerable comovement of credit spreads derived from bond index portfolios (Pedrosa and Roll, 1998)   The number of constituents in the respective iBoxx index is given in the first column. Annualized Modified Duration and Time to Maturity (Mat.) are given in years. The mean and median daily change of ASW spreads is given in basis points. The standard deviation of daily changes is given in basis points and the annualized Standard Deviation is given in annualized basis points. The mean and median of ASW spreads are denoted in basis points. Finally the respective stock index for every ASW sector is reported in the last column. These are the corresponding DJ Euro Stoxx sector indexes (depart from the the group of non-financial firms where the FTSE World Europe ex Financials index is used) and the DJ Euro Stoxx 600 index (Stoxx 600). ** and * denote significance at the 1% and 5% level, respectively.  This table compares the regime specific moments (mean, skewness and kurtosis) of the asset swap spread changes (ΔASW t ). The value of the mean changes is reported in basis points. The second column presents the percentage of time sample indexes spent in the turbulent regime.  This Table presents the α 1 coefficients from the logit regressions (see equation 3) with t-statistics (in parentheses) and R 2 [in brackets]. We use a Huber-White consistent estimate of the covariance matrix to control for autocorrelation and heteroscedasticity. The theoretical determinants are: lagged squared ASW changes (∆ASW t−1 2 ), lagged ASW changes (∆ASW t-1 ), lagged daily stock index returns (Stock return t-1 ), lagged change in the VStoxx volatility index (∆VStoxx t-1 ), lagged change in the level of the swap curve (∆IR_Level t-1 ), and lagged changes in the difference of the swap and the German government yield curve (∆Swap Spread t-1 ).  The theoretical determinants are: lagged squared ASW changes (∆ASW t−1 2 ), lagged ASW changes (∆ASW t-1 ), lagged daily stock index returns (Stock return t-1 ), lagged change in the VStoxx volatility index (∆VStoxx t-1 ), lagged change in the level of the swap curve (∆IR_Level t-1 ), and lagged changes in the difference of the swap and the German government yield curve (∆Swap Spread t-1 ).   This table presents results of the regressions of the actual changes in asset swap spreads (ΔASW t ) against the predicted changes (predicted ΔASW t ). The predictions are based on our Markov model (equation 1) for the two regimes (turbulent and calm) and an equivalent OLS model (using the same explanatory variables) for the entire sample period. The turbulent and calm regimes were defined using probabilities estimated by our Markov model. Observations with the estimated probabilities above 0.5 were included in the turbulent regime. T-statistics for tests of the β equals to 1 and the constant term equals to 0, reported in brackets. N is the number of observations in the corresponding regime. ** and * denote significance at the 1% and 5% level, respectively.