The real-time predictive content of asset price bubbles for macro forecasts

Transcription

1 The real-time predictive content of asset price bubbles for macro forecasts Benjamin Beckers DIW Berlin, Macroeconomics and Graduate Center June 23, 2015 Financial support by the Deutsche Forschungsgemeinschaft (DFG) is gratefully acknowledged.

2 Research Questions and Contribution (How well) Can asset price bubbles be detected in real-time? Review performance of popular (HP-filter) and recently proposed bubble indicators (recursive ADF-tests by Phillips et al. (2011, 2013)). Propose combination indicators, aggregating the information contained in individual indicators. Can bubble indicators improve forecasts for output and inflation? Horse-race of indicators against a simple OLS-forecast model containing common predictors from Stock & Watson (2001) following Assenmacher-Wesche & Gerlach (2010). Extend analysis of Assenmacher-Wesche & Gerlach (2010) by acknowledging real-time dimension of the data.

3 Outline 1 Research Question 2 Bubble indicators 3 Simulation results 4 Forecast results 5 Conclusion

4 HP-filter indicator (Assenmacher-Wesche & Gerlach, 2010) Excessive price deviations from slow moving fundamentals 10% threshold for stock, 7.5% for house prices Strong trend smoothing with parameter 100, Recursive or rolling trend estimates One-sided HP filter for real-time application

5 Detecting asset price bubbles by unit root tests Starting point: Rational bubbles P t = r E t [P t+1 + D t+1 ] ( 1 = 1 + r i=0 ) i E t [D t+i] + lim i = P f t + B t, with B t = r E t[b t+1 ]. ( ) 1 i E t [P t+i] 1 + r Test log real prices and fundamentals separately for explosive roots (H 0 : δ = 1 vs. H 1 : δ > 1) z t = µ z +δz t 1 + J iid φ j z t j +v t, v t N(0, σ 2 v ), z t = {p t, d t } j=1

6 Unit-root tests (PWY11, PSY13) Forward recursive regression to obtain sequence of right-tailed ADF-test statistics ADF τ, τ = τ 0, τ 0 + 1,..., T Binary bubble indicator with B τ = 1 if H 0 is rejected for prices but not for dividends PWY11: recursive or rolling estimation PSY13: forward recursive, backward rolling estimation to allow for an automatic restarting of the monitoring procedure after a bubble collapse Individual vs. log-ratio tests: Larger power vs. accounting for relative growth rates P t 1 ( ) 1 + r r D ( ) 1 i t = E t [ D t+i]+b t I (0) if B t = 0 r 1 + r i=1

7 Frequency of detecting X bubbles: One-bubble scenario Number of detected bubbles Zero One Two Zero One Two Emergence date τ e = 40 τ e = 120 HP rec HP rol PWY11 rec: individual PWY11 rec: ratio PWY11 rol : individual PWY11 rol : ratio PSY13: individual PSY13: ratio Combination: κ = Combination: κ = Combination: κ =

8 U.S. stock price bubbles

9 Direct OLS forecasts Direct h-step forecasts of IP and CPI y t+h = µ + P p=1+r δ p y t p+1 + Q q=1+s xi x t q+1β q + ε t+h r, s xi publication lags of dependent y and explanatory variable x i, respectively x t : IP, CPI, 3 month t-bill rate, 10Y-3M spread, unemployment rate, binary bubble indicator Fixed P and Q or specific-to-general variable and lag length selection by iterative addition of in-sample significant regressors Sample 1983M M12, Start of out-of-sample forecast period: 1997M12

10 Forecasting output with stock price bubbles Forecast horizon (in months) Model Benchmark HP rec 0.982* 0.971* 0.963* 0.981* 1.007* HP rol 0.994* 0.984* 0.979* 0.995* 1.035* 1.080* PWY11 rec: ind 0.995* 0.986* 0.970* 0.976* 1.003* 0.959* PWY11 rec: ratio * 0.988* * PWY11 rol : ind 0.979* 0.959* 0.942* 0.955* PWY11 rol : ratio * 1.041* PSY13: ind 0.994* 0.988* 0.976* 0.974* 0.972* 0.924* PSY13: ratio * Combi: κ = * 0.956* 0.937* 0.950* * Combi: κ = * 0.957* 0.936* 0.952* 0.993* 0.985* Combi: κ = * 0.958* 0.940* 0.959*

11 Forecasting output with stock price bubbles No real-time data Forecast horizon (in months) Model Benchmark HP rec HP rol PWY11 rec: ind PWY11 rec: ratio PWY11 rol : ind PWY11 rol : ratio PSY13: ind PSY13: ratio Combi: κ = Combi: κ = Combi: κ =

12 Forecasting output with stock + house price bubbles Forecast horizon (in months) Model Benchmark HP rec 0.988* 0.979* HP rol 0.994* 0.984* 0.979* 0.994* 1.033* 1.077* PWY11 rec: ind 0.995* 0.986* 0.970* 0.976* 1.003* 0.959* PWY11 rec: ratio 0.994* 0.987* * PWY11 rol : ind 0.977* 0.952* 0.936* 0.941* PWY11 rol : ratio PSY13: ind * 0.983* PSY13: ratio Combi: κ = * 0.934* 0.916* 0.917* Combi: κ = * 0.952* 0.932* 0.933* Combi: κ = * 0.950* 0.930* 0.932*

13 Conclusion HP-filter as used by AWG10 signals too many bubbles. Asset price bubble indicators by PWY11 and PSY13 can detect commonly accepted bubble periods in real-time but signals are often instable. Results are very sensitive to timing and number of bubbles in the sample, and the exact indicator specification. Testing prices and dividends separately is found to be superior over testing their log-ratio. Combination indicators can provide insurance against high uncertainty of individual signals. Bubble indicators by PSY13 and combination indicators significantly improve output forecasts. Real-time dimension needs to be acknowledged.

14 Thank you for your attention. DIW Berlin Deutsches Institut für Wirtschaftsforschung e.v. Mohrenstraße 58, Berlin

15 References Assenmacher-Wesche, K. & Gerlach, S. (2010). Monetary Policy and Financial Imbalances: Facts and Fiction. Economic Policy, 25, Bernanke, B. & Gertler, M. (1999). Monetary Policy and Asset Price Volatility. Economic Review, Q IV, Bordo, M. D. & Jeanne, O. (2002). Monetary Policy and Asset Prices: Does Benign Neglect Make Sense? International Finance, 5(2), Campbell, J. Y. & Shiller, R. J. (1987). Cointegration and Tests of Present Value Models. Journal of Political Economy, 95(5), pp Cecchetti, S. G., Genberg, H., & Wadhwani, S. (2002). Asset Prices in a Flexible Inflation Targeting Framework. Working Paper 8970, National Bureau of Economic Research.

16 References Fatás, A., Kannan, P., Rabanal, P., & Scott, A. (2009). Lessons for Monetary Policy from Asset Price Fluctuations. World Economic Outlook, Chapter 3, International Monetary Fund. Kohn, D. L. (2006). Monetary Policy and Asset Prices. Speech at Monetary Policy: A Journey from Theory to Practice, a European Central Bank Colloquium held in honor of Otmar Issing, Frankfurt, Germany. Phillips, P. C. B., Shi, S.-P., & Yu, J. (2013). Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500. Cowles Foundation Discussion Paper Phillips, P. C. B., Wu, Y., & Yu, J. (2011). Explosive Behavior in the 1990 s NASDAQ: When Did Exuberance Escalate Asset Values? International Economic Review, 52(1), Stock, J. H. & Watson, M. W. (2001). Forecasting Output and Inflation: The Role of Asset Prices. NBER Working Papers 8180, National Bureau of Economic Research, Inc. Trichet, J.-C. (2005). Asset price bubbles and monetary policy. Speech at Bridgewater College, Bridgewater, Virginia.