european sovereign bond etfs – tracking errors and sovereign debt crisis

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EUROPEAN SOVEREIGN BOND ETFs TRACKING ERRORS AND SOVEREIGN DEBT CRISIS Branko Uroevi , Faculty of Economics, University of Belgrade and National Bank of Serbia First Moscow Finance Conference November 2011. Outline. Background, motivation, contributions Literature and hypotheses - PowerPoint PPT Presentation

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  • EUROPEAN SOVEREIGN BOND ETFs TRACKING ERRORS AND SOVEREIGN DEBT CRISIS

    Branko Uroevi, Faculty of Economics, University of Belgrade and National Bank of Serbia

    First Moscow Finance ConferenceNovember 2011

  • Outline

    Background, motivation, contributionsLiterature and hypothesesData and methodologyResultsConclusions

  • MotivationMarket for sovereign bonds of the Euro zone in the forefront of interest of investors, politicians, Fundamental shift in perception: from virtually riskless to much more akin to corporates (some of them with a junk bond status)Diverging performance across countriesEFTs: new, liquid and relatively transparent way to get exposure to that marketWhile many asset classes are severely impacted by the crisis, ETFs steadily grow in importance

  • Literature and motivationAmenc and Golz (2009) EU Survey - ETFs play an increasingly important role in providing exposure to various asset classesRompotis (2008; German equity index ETFs); Milionas and Rompotis (2006; Swiss equity index ETFs); Gallagher and Segara (2005; Australian equity index ETFs); Blitz et al. (2010; EU equity index ETFs);Drenovak and Urosevic (2010), Houweling (2011) analyse corporate (European and US) and sovereign (US) bond ETFs. Performance measured using correlation-based tracking error.Alexander (1999); Alexander and Dimitriu (2004):Two time series can be cointegrated even if static correlations between them are lowImportant to examine tracking performance based on both correlation and cointegration, especially for passive investment strategies.

  • Literature and motivationNorden and Weber (2009)CDS market reflects information earlier than bond marketBond spreads adjust to CDS spreads.The dynamics of spread volatility are driven by those of spread levels (Nomura, 2011). They may influence tracking performance after the crisis commenced.

    Composition of ETFs may also play an important role now (and did not play much of a role before)

  • ContributionsStudy tracking performance of the most important families of ETF funds that track indexes of euro zone sovereign bonds using a novel data setCompare performance using 4 types of tracking errors: active return (TE1), two short-term correlation based measures (TE2 and TE3) and a cointegration-based tracking error measure (TE4)Synthetic versus physical replicationPreliminary study of determinants of tracking errors including how crisis influenced them

  • HypothesisH1: Euro zone sovereign ETFs underperform their underlying bond indexes, regardless of the replication method.H2: ETFs tracking errors are positively associated with the volatility of target indices.H3: ETFs tracking errors are positively associated with the maturity of underlying indexes.

  • HypothesesH4: Tracking performance of EU sovereign debt ETFs deteriorated during the post Lehman period.H5: CDS spreads for index constituents are an important determinant of tracking performance of EU sovereign debt ETFs in a post-Lehman periodH6: Synthetic ETFs perform better measured by TE1 and TE4, and worse measured by correlation based measures (TE2 and TE3), compared to full replication physically-based ETFs.

  • Sample and data

    Sample: 31 Euro sovereign bond indices ETFs, January 2007-December 2010 (captures more than 90% of the market):iShares (track Barclays term, Markit iBoxx Liquid Capped, eb.rexx German Government). Physical replication. db x-trackers (track Markit iBoxx Sov). Synthetic replicationLyxor (track EuroMTS). Synthetic replicationAll indices tracked by sample ETFs are total return indices (all interest payments are reinvested); all maturities consideredDaily data on: Net Asset Values (NAV), Weights, CDS, and Bid-ask. Data on NAV from Frankfurt (for consistency)Sources: Bloomberg, index providers, Morningstar

  • Aggregate country exposure of government bond indices tracked by sample ETFs, stratified by ETF providers

    GIFrSpBNdGrPAIrFiniSharesBarclays Term 1-332.642.111.114.2Barclays Term 3-532.223.730.813.3Barclays Term 5-748.025.012.714.2Barclays Term 7-1056.67.929.95.6Barclays Term 10-159.341.430.37.012.0Barclays Term 15-3029.434.821.79.74.4iBoxx Liq Sov Cap 1.5-2.520.320.020.28.04.49.513.93.8iBoxx Liq Sov Cap 2.5-5.520.320.020.520.013.06.1iBoxx Liq Sov Cap 5.5-10.520.520.320.319.913.15.9iBoxx Liq Sov Cap 10.5+20.619.820.119.314.55.7iBoxx Liq Sov Cap 1.5-10.520.420.220.319.811.97.4eb.rexx 1.5-2.5100.0eb.rexx 2.5-5.5100.0eb.rexx 5.5-10.5100.0eb.rexx 10.5+100.0eb.rexx DE100.0Db x-trackersShort iBoxx Sov21.623.720.99.45.95.43.92.23.72.01.1iBoxx Sov21.623.720.99.45.95.43.92.23.72.01.1iBoxx Sov 1325.124.419.811.15.75.53.81.81.21.00.7iBoxx Sov 3524.417.219.811.07.25.44.22.34.32.0iBoxx Sov 5721.120.122.46.88.66.03.92.55.71.71.2iBoxx Sov 71019.325.321.28.34.35.84.52.54.13.11.7iBoxx Sov 10154.326.023.47.25.86.65.84.87.27.61.3iBoxx Sov 15+23.930.121.39.44.63.72.40.93.40.5iBoxx Sov 25+22.020.731.011.64.94.42.33.0LyxorEuroMTS 1-3Y23.723.421.811.56.24.34.60.91.31.840.7EuroMTS 3-5Y24.118.021.29.07.66.03.72.64.71.341.8EuroMTS 5-7Y20.816.923.66.18.96.25.52.86.21.911.2EuroMTS 7-10Y18.624.219.69.14.15.65.33.33.94.362.1EuroMTS 10-15Y2.131.023.25.26.88.55.34.68.45.0EuroMTS 15Y+22.329.021.311.05.33.72.30.93.60.8

  • Summary statistic (average values)

    AUM (000)TER (bps)Dur (yrs)Vol (%)CorrM-star indexiShares -Barclays 189,594 20 6.77 4.42 0.99 4.25 iShares -iBoxx 114,943 16 5.80 4.63 0.97 3.40 iShares- eb.rexx 689,901 15 5.70 4.55 0.98 4.80 db x-trackers 321,861 15 6.79 5.56 0.98 3.40 Lyxor 543,808 16.5 6.77 5.12 0.51 3.83

  • Methodology Standard methods

  • Methodology - OLS (TE3 = standard error of the regression)

  • Methodology - Cointegration framework (TE4 = autocorrelaton of the residual from the cointegrating regression)

    Here, NAVp and NAVb are daily log NAV values for ETFs and underlying indices, z is the cointegrating vector NAVpt - NAVbt, is the first difference operator, and the lags, lengths and coefficients are determined by OLS regression.

  • Tracking error and replication methodsPrevious studies did not examine tracking errors in the context of different replication methods adopted by ETFs. Replication methods should be taken into consideration when selecting an appropriate measure of tracking errors. A full physical replication of indexes with strict inclusion criteria would, for example, lead to a very high correlation of ETF and tracking index returns and, therefore, low TE2 and TE3. This can come at the expense of long term co-movement (TE4, for example).

  • Ranking Based on TE1Consistent with our hypothesis 1, sample ETFs underperformed their respective indexes during the sample periodThe average underperformance for the total sample is similar to the underperformance reported in previous studies on bond ETFs (Drenovak and Urosevic, 2010; Houweling, 2011). The average annual TE1, however, varies significantly from 0.53 (LyxorMTS+15) bps to 27.38 bps (iShares-BarclaysTerm10-15). Overall, the iSharesBarclaysTerm family exhibits largest whilst Lyxor ETFs exhibit the smallest average TE1s. Lyxor ETFs actually over-performed relevant indices in 2007 and 2009. db x-trackers exhibited the most consistent performance during the sample period. While iShares-Barclays family continues to exhibit similar TE1s, the rest of the ETF families exhibited a sharp drop in the TE1 levels in 2009. In 2010, iShares-eb.rexx and Lyxors were the only ETFs with a sharp increase in average TE1.

  • Performance and sample TE1This table represents ETFs annual returns (%) and TE1 presented in basis points [in brackets]. Positive basis points indicate undeperformance of the ETFs.

    YeariSharesdb x-trackLyxorBarclays T.iBoxx L.S.C.eb.rexxMean20071.10 [23]1.35 [8]1.51 [5]5.00 [16]2.29 [-7]20089.99 [28]8.99 [24]12.35 [32]8.17 [17]9.13 [17]20094.39 [28]3.64 [9]1.91 [7]2.79 [15]4.46 [-3]201012.45 [20]7.88 [16]14.58 [68]7.80 [15]8.37 [33]2007-106.58 [27]4.95 [13]6.10 [19]5.37 [16]5.86 [10]Median20072.15 [23]1.89 [7]2.66 [9]5.03 [16]2.97 [10]200810.37 [29]9.77 [24]11.52 [36]8.97 [17]9.43 [17]20094.21[27]3.73 [8]2.78 [10]4.19 [15]4.81 [0]201014.08 [21]6.53 [14]12.03 [63]9.37 [16]8.12 [31]2007-106.51 [26]5.22[13]6.29[16]6.62 [16]5.91[12]Min2007-3.42 [19]-3.29 [3]-3.59 [-11]3.42 [15]-0.2 [99]20086.42 [24]6.48 [18]7.13 [15]-3.54 [12]6.48 [15]20092.23 [21]1.19 [6]-1.59 [-12]-3.21 [13]2.13 [-7]20104.14 [13]1.87 [10]5.42 [29]-7.77[12]3.36 [4]Max20073.52 [26]3.42 [12]3.51 [13]6.44 [16]3.55 [20]200812.8 [31]11.53 [27]18.44 [47]13.81 [19]10.76 [18]20097.39 [39]5.34 [11]3.47 [15]5.29 [16]5.40 [17]201016.38 [25]17.21 [30]28.02 [127]14.37 [18]13.51 [63]

  • Ranking of funds based on TE2All sample ETFs have statistically significant average (mean) tracking errors at the 1% level of significance. This result is robust to the use of weekly or monthly instead of daily price series.Overall, iShares funds which replicate Barclays Term indices exhibit the smallest while Lyxor ETFs exhibit the largest values of TE2. TE2 (based on monthly returns) range from 1.1 basis points (iShares-BarclaysTerm1-3) to 43.83 (LyxorMTS10-15). The results also suggest that ETFs tracking higher maturity indices have typically higher levels of tracking error. This is the case for all sample ETFs except for db iBoxx Sov 5-7. There are also some differences in the way TE2s changed during the sample period. iBoxx Liquidity and eb.rexx, for example, exhibited the highest TE2 in 2009. This is consistent with high weightings for Greece (in iBoxx liquidity) and Germany (in eb.rexx), the two countries with extremely volatile interest rates during 2009. Barclays, db, and Lyxor indices, however, exhibited the highest TE2 during 2008.Lyxor funds have particularly high TE2.

  • Sample TE2 This table presents results for