Key rate thresholds turning defensive assets into ordinary assets

1. Center for Macroeconomic Analysis and Short-term forecasting Tel.: 7-499-129-17-22, Fax: 7-499-129-09-22, e-mail: mail@forecast.ru, http://www.forecast.ru Key rate thresholds turning defensive assets into ordinary assets Alexander Apokin, CMASF NRU-HSE Conference, April 2-5, 2013

2. Research Question • Do most widespread defensive assets turn into ordinary assets for prolonged time periods? • Where are the thresholds for risk-free rate on defensive assets?

3. Research questions What is a defensive asset? • Suppose CAPM holds, thus the yield on asset i is: , where • Defensive assets are assets with , i.e. assets that have negative correlation with the market portfolio (usually the stock index) • Anecdotal evidence is that most widespread defensive assets are oil, gold and Swiss frank

4. Research questions Oil, gold and CHF rolling betas* *30-day rolling betas relative to S&P500, kernel smoothing at 0.95

5. Research questions How defensive asset can transform into an ordinary asset?

6. Research questions How defensive asset can transform into an ordinary asset? • The case for oil was formulated in Kaufman (2012) based on Pindyck (2001):

7. Research questions How defensive asset can transform into an ordinary asset? • SML downward shift because decreases: • lower rates mean lower beta • might mean negative rates for negative-beta assets • Futures markets role (ceteris paribus): • lower rate means lower convenience yield • with lower convenience yield, futures price changes from backwardation to contango – i.e. it is better to receive asset later than to buy&store it • stocks decline and spot price rises until contango is eliminated – rates thus kept positive

8. Research questions How defensive asset can transform into an ordinary asset?

9. Research questions Oil, gold and CHF rolling betas* *Kernel smoothing at 0.95

10. Research questions Oil vs. gold rolling betas* *30-day rolling betas relative to S&P500

11. The Literature Defensive assets in the literature • Macroeconomic variables and oil shocks, based on Hamilton (1983) • Hamilton (2003), Kilian and Park (2009), Conrad et al. (2012) • Financial markets and investment management, based on Jaffe (1989) and Jones and Kaul (1996) : • Oil: Nandha and Faff (2008), Huang et al. (1996) • Gold: Jaffe (1989), Lawrence (2003), Baur et al (2006), Fang et al (2012)

12. The Data

13. 1. Individual asset rate thresholds Центр макроэкономического анализа и краткосрочного прогнозирования 13

14. 1. Individual asset rate thresholds Individual asset rate thresholds • Time-series self-exciting threshold models (SETAR) for each asset separately • Fed funds rate as exogenous regime-switching variable: • FF rate clearly can influence financial markets (e.g. betas) • FOMC is practically unconcerned by betas in its rate decisions

15. 1. Individual asset rate thresholds Estimation results (WTI SETAR) SETAR vs. Linear test Pval 1vs2 19.71 0.5 1vs3 31.88 0.0 2vs3 11.13 0.0 Threshold: 2.61%

16. 1. Individual asset rate thresholds ACF/PACF (WTI SETAR)

17. 1. Individual asset rate thresholds Estimation results (Gold SETAR) SETAR vs. Linear test Pval 1vs2 6.91 0.5 1vs318.70 0.0 2vs3 11.41 0.0 Threshold: 1.81%

18. 1. Individual asset rate thresholds ACF/PACF (GoldSETAR)

19. 1. Individual asset rate thresholds Estimation results (CHF SETAR) SETAR vs. Linear test Pval 1vs2 8.79 0.5 1vs314.32 0.0 2vs3 5.31 0.0 Threshold: 1.00%

20. 1. Individual asset rate thresholds ACF/PACF (CHFSETAR)

21. 1. Individual asset rate thresholds Individual asset rate thresholds • Fed funds rate threshold for beta dynamics is in range of 1.0%-2.6%, tests indicate threshold models are specified correctly • WTI and CHF exhibit no mean reversion under the threshold • Regime change in dynamics is most pronounced in oil

22. 2. Common threshold dynamics Центр макроэкономического анализа и краткосрочного прогнозирования 22

23. 2. Common threshold dynamics Common threshold • Asset prices are apparently interweaved, and so are betas • Thus, threshold VAR approach (Lo and Zivot, 2001) needed: • FF rate stays an exogenous variable • Test rejects hypothesis of common unit root for defensive asset betas

24. 1. Individual asset rate thresholds Estimation results (TVAR) LR test: 1vs2 1vs3 Test 82.4 134.1 P-Val 0.0 0.0 Threshold value: 1.82

25. 1. Individual asset rate thresholds Impulse response (Low regime)

26. 1. Individual asset rate thresholds Impulse response (High regime)

27. 3. Conclusions Центр макроэкономического анализа и краткосрочного прогнозирования 27

28. 4. Conclusions • “Defensive assets” are not defensive per se, some additional conditions (like relatively high rates) needed • The thresholds for asset betas exist and are in the range 1.0-2.6% • There were two distinct periods of low rates, i.e. “low” beta regime with positive betas, and we are in the one since 2008 • Change in beta is the most pronounced for oil market, while in gold and CHF it is still volatile

29. Future research directions • Expanding the list of defensive assets to gov’t bonds • Testing the role of macro shocks vs. risk-free rate shocks • Integrating yield forecasts into threshold multi-factor APT model • Accounting for financialization and producer-speculator structure • Oil market financialization • Non-commercial trader positions for each market • Endogenize futures convenience yields • Building on level data (cointegrated) instead of yield data (stationary) • Testing structural break vs. regime-switching model