Discussion: Realtime forecasting with macro-finance models in the presence of a zero lower bound by Lewis and Krippner

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1 Discussion: Realtime forecasting with macro-finance models in the presence of a zero lower bound by Lewis and Krippner Torsten Ehlers Bank for International Settlements The opinions represent those of the presenter and not necessarily the BIS. Restricted

2 What the paper is about Forecasting with yield curves Long literature showing that yield curves contain information which can help to predict macro variables (eg growth) Incorporate macro variables into the model ( macro-finance model, but here only for the forecasting part) Term-structure models usually have latent (unobservable) factors; but macro variables can explain quite a bit of variation in these latent factors -> include them to improve the model Adapt the model to deal with the zero lower bound Technical issue, since yield curve models do not restrict the instantaneous rate to be above zero -> models would imply unrealistic short-term rates in economies with zero rates and QE Restricted 2

3 My (only) comment and some questions Forecasting section is very nicely done, only one comment: Different types of restrictions need more explanation Which brings me to my questions 1. What kind of possible economic interpretation can be given to the four factors of the yield curve model (Level, Slope, Bow, and Phi)? 2. What is generating the additional forecasting power? Importantly, how is this related to the possible economic interpretation of the yield curve factors? 3. How does this compare to the economic interpretation (and forecasting power) of no-arbitrage affine term structure models? Restricted 3

4 Trying to make sense of the yield curve literature Generally, yield curve models try to explain the dynamics of government bond yield (no default risks) AND the maturity structure (term structure) at the same time Generally two strands: Nelson-Siegel (1987) type of models (this paper) and another strand based on Cox, Ingersoll and Ross (1985) [Dai and Singleton (2000) is a modern version] Both types of models can explain almost 100% of the variation of yields over time and across maturities with 3-4 factors What is the difference and what can these models tell us? Restricted 4

5 Yield curve models ala Dai and Singleton (2000) what do they do? Most important feature: no-arbitrage conditions Risk-adjusted (!) returns should be the same across all maturities -> expectations hypothesis does not hold Can decompose yields into The risk-neutral / expected rate Term premium (risk premium that investors demand for possible unexpected changes in future short rates) Restricted 5

6 The US term structure of interest rates pre-crisis and during the crisis Yield curve is steeper Lower expected rates Higher term premia Restricted 6

7 And post-crisis Yield curve is less steep Negative term premia Expected rates have increased by 70bp Restricted 7

8 Affine no-arbitrage yield curve factors and forecasting Typically, in forecasting exercises, researchers have used the latent factors themselves, rather than the risk-neutral rate versus termpremium decomposition (why?) The latent factors have no direct economic interpretation, but The first latent factor (like a principal component, though confusingly it is also called level factor) is often said to be related to (long-term) inflation (expectations) The second ( slope ) is often related to economic activity, but the literature is divided on the third ( bow ) factor (Krippner finds that it is related to the cyclical components of inflation and growth) Ang and Piazzesi (2003) conjecture that, because of the above, the level factor helps to forecast (long-term) inflation and the slope factor helps to forecast economic growth (Moench (2012) finds some helpful forecasting properties of the bow factor, too) Restricted 8

9 Trying to make sense of the yield curve literature - continued Other strand is Nelson-Siegel (1987), which shows that 3 factors (level, slope, bow (here the names make sense)) can explain most of the yield curve dynamics Very widely used model to (easily) estimate the term-structure of government bond yields Purely statistical description, no no-arbitrage conditions (at each point in time): BUT, can be amended to ensure that no-arbitrage conditions are met (Christensen, Diebold and Rudebusch (2011)) Restricted 9

10 Flexibility of Nelson-Siegel type of yield curve models From Diebold and Li (2006) Restricted 10

11 Nelson-Siegel (1987) yield curve model Restricted 11

12 The Krippner arbitrage-free Nelson-Siegel model results Restricted 12

13 The Krippner arbitrage-free Nelson-Siegel model results Can we say that in 2008 the model does not detect a significant long-term impact, but a massive medium-term impact and a fairly strong short-term impact on yields? Restricted 13

14 More questions (since I could not find any answers ) How do the factors (in particular slope and bow) relate to negative term premia (which seem to be a consensus)? If they relate at all, what would this imply for their forecasting significance? Ang and Piazzesi (2003) argue that imposing no-arbitrage restrictions in their model improves its forecasting properties Is there a relation to your full restrictions VAR model? If so, it does not seem that imposing the AFNS restrictions improves forecasting power in your setup Maybe my questions give some ideas for future research Restricted 14

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