Presentation Transcript

1. Phase dating and contagion in the GFC: a smooth transition structural GARCH approach

   George Milunovich – Macquarie University Susan Thorp – University of Technology Sydney Minxian Yang – University of New South Wales
2. Motivation Real estate shocks preceded the 2007-2009 financial crisis but other asset classes including debt and equities received, transmitted and possibly amplified the shocks. We dissect the crisis at the level of structural shocks, tracking changes in simultaneous links between equities, T-bonds and real estate. Stocks (SP 500) Real Estate (FTSE NAREITs) T-Bonds (BOA Merrill Lynch US Treasury Index)
3. Data and Complications Data sample: Time Period: June 2001 – September 2010 Sampling Frequency: Daily No. of Observations: 2296 Investigate possible breaks in the structural relationships due to the GFC Modeling Challenges: Endogenous data Possibility of several regime shifts during the period of the GFC
4. Model Basic Structure for filtered returns Or in vector notation:
5. Endogenous Dating and Estimating the Impact of the GFC In order to account for possible regime shifts in the relationships across the three markets we extend the model as follows where
6. Smooth Transition Functions Sj Shape of the transitions function depends on: the speed of transition through γ > 0. As γ →∞ transition becomes abrupt and the model jumps between the states. the location of transition through c > 0. We allow up to three changes in regime, i.e. four phases 0<c1<c2<c3<1. For a large value of γ if c1≤ xt <c2 then Bt=B1 etc. For information on smooth transition models see Granger (1993), van Dijk, Terasvirta, Frances (2002), Silvennoinen and Terasvirta (2009), amongst others
7. Identification Strategy When the error vector ut=Byt is homoskedastic, the structural matrix B cannot be recovered from the reduced VAR without identifying restrictions. Examples of such restrictions include a) exclusion restrictions, see for example Sims(1980) and Bernanke (1986), b) sign constraints on parameters in B (Blanchard and Diamond (1989)) or c) assumptions about long-run multipliers (Blanchard and Quah (1989)) Recently a number of papers used identification via heteroskesticity to avoid imposing such constraints Sentana and Fiorentini (2001) provide sufficient conditions for identification of factor models in which the factors are heteroskedastic Rigobon (2003) uses discrete regime shifts in volatility to identify SVAR models Rigobon and Sack (2003) suggest that ARCH in structural errors could be used to identify structural VAR models but do not provide exact conditions for identification. Lanne et al (2010) obtain identification of a structural model where heteroskedasticity follows a Markov switching process Klein and Vella (2010) and Lewbel (2010) exploit relationships between heteroskedasticity and exogenous explanatory variables to prove identification Milunovich and Yang (2010) prove joint identification of all structural parameters of SVAR models with ARCH variances
8. Identification Strategy In this paper we use Milunovich and Yang (2010) arguments and extend them to take into account the possibility of regimes shifts as described in this paper. All structural parameters are locally identified at any regular point in the parameter space γ is sufficiently large B0, B1, B2, B3are all invertible and different at least n-1 structural shocks have ARCH effects
9. Estimated Crisis Regime Dates 13 Sep – bailout of Northern Rock 18 Sep – lowering of Fed Funds rate 1 Oct – UBS announces a large write-down of its portfolios 5 Oct – Merrill Lynch reports large losses 10 Oct – establishment of the HOPE NOW alliance to stave off mortgage foreclosures 9 Aug – large European banks report falls in earnings of between 28%-63% one year after the start of the crisis 7 Sep – Fannie Mae and Freddie Mac passed into conservatorship, $100bn provided to each company , both CEOs replaced 10 Sep – Lehman announces $3.9bn loss in 3rd quarter 15 Sep – Lehman files for bankruptcy, BOA buys Merrill Lynch , AIG debt downgraded by all three major rating agencies
10. Variance Decompositions Since the structural parameters are identified and we obtain the estimates of the B matrices in the next step is to try to identify the structural shocks We use the following strategy developed in Dungey et al (2010) A shock is named after the market to which it contributes the largest fraction of its variance Two variable example If then is called the rivariable shock.
11. Variance Decompositions
12. Model Fit – Residual Diagnostics
13. Conclusions We develop an identified Structural GARCH model with smooth transition functions We are able to endogenously date 3 structural breaks and 4 regimes Significant changes are found in the linkages between gov’t debt, real estate and equity which persist into the post-GFC period Direct linkages to and from T-bonds and the other two markets become insignificant over the crisis Impact of equities on real estate increases dramatically during the first phase of the GFC and remains high Impact of real estate on stocks doesn’t change over the crisis but almost halves over the post-GFC period Impact from T-bonds on REITs corrects sign from in the post-GFC period Variance decompositions illustrate the propagation of risk across the three assets, with real estate shocks starting to grow in importance in 2003-2004 period.