Online Appendix for Demand for Crash Insurance, Intermediary Constraints, and Risk Premia in Financial Markets


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1 Online Appendix for Demand for Crash Insurance, Intermediary Constraints, and Risk Premia in Financial Markets Hui Chen Scott Joslin Sophie Ni August 3, An Extension of the Dynamic Model Our model presented in the paper captures a number of the key features we have found in the data. In particular, the model captures the fact that when equilibrium public buying is low, risk premia may be high as this may correspond to time when dealers are (or act as if they are) more risk averse. However, as in Chen, Joslin, and Tran (2012), wealth moves slowly between the public sector and dealers outside of disasters and only through crash insurance premiums. In this section, we generalize our main model to account for more general time variation in the relative wealth of the public and dealers. Consider the case where the public and dealer not only view the disaster events differently, but also disagree about the future path of the likelihood of disasters. Specifically, consider the more general form of Equation (A9) where dp D dp P = ρ Nt e (1 ρ) t 0 λsds e s 0 θsdw s λ t 0 θ2 s ds. (IA1) Chen: MIT Sloan and NBER Joslin: USC Marshall Ni: HKUST 1
2 and θ s is some process satisfying Novikov s condition. For example, with an appropriate chose of θ t, we may have that the dealer will believe that the dynamics of the λ t are dλ t = κ D ( λ D λ t )dt + σ λ t dw λ,d t, where W λ,d t is a standard Brownian motion under the dealer s beliefs. An example we have in mind is that the dealer may believe that when the intensity is high, it will mean revert more quickly to the steady state than it actually will. When this is the case, the dealer will make bets with the public that the intensity will fall. This will cause the public s relative wealth to grow if the intensity continues to rise. Thus even if the dealers are becoming more risk averse as the intensity rises, there will be a greater demand for crash protection from the public and in equilibrium the net effect can be that the size of the insurance market increases. Without this additional trading incentive, the relative wealth of the public and dealers will be nearly constant over short horizons. This extension allows us to capture some patterns seen in the crisis. In Figure 1 of the paper, we saw that in the early stages of the crisis, the demand for crash insurance spiked and subsequently bottomed out as we reached the later stages. The extended model can capture these types of features. To see this, we extend our base model with the additional assumption that the dealers believe that λ t mean reverts ten times faster than the public (a half life of 0.48 years versus 4.8 years.) For simplicity, we assume that over a two year period the disaster intensity rises from its steady value of 1.7% at a rate of 1%/year to 3.7%. We initialize the public with a planner weight such that they initially represent 25% of consumption. We also model the implied risk aversion of the dealer to remain constant at γ = 4 in the beginning of the sample and then increase quadratically to γ = 6.5 at the end of the period. Figure IA1 plots the resulting market size (Panel A) and risk premium (Panel B), as measured by λ Q λ P. Generally, the patterns we see are qualitatively similar to those found in Figure 1. The public begins buying more insurance as the dealers lose money on their λ bets. As the crisis deepens, the dealers start to become very risk averse and the market dries up to the point where the dealers even become buyers 2
3 0.03 A. Market Size 10 B. Risk premium Net Public Holding λ Q /λ P Time (years) Time (years) Figure IA1: Dealer constraint and derivative supply. This figure plots the equilibrium holding of crash insurance by the public investors (Panel A) and disaster risk premium (Panel B) for a hypothetical history where the intensity rises from 1.7% to 3.7% over a two year period. The public has an initial consumption fraction of The dealer s relative risk aversion is initially γ = 4 and then rises in the second half quadratically to γ = 6.5. of protection. Across this time period, the risk premium at first increases very slowly until the dealers are no longer willing to hold the risk and the premium begins to increase rapidly. 2 Additional Empirical Results This section presents additional empirical results and robustness checks. They include (1) a table of correlations between P N BO and various macroeconomic and financial variables (Table IA1); (2) a systematic analysis of the statistical significance for the returnforecasting regressions using P BN O and P N BON (Table IA2); (3) returnforecasting regression with P NB (and P NBN), which is the public net buy volume for DOTM SPX puts that include both open and close transactions (Table IA3); (4) returnforecasting regression with P NBO 1month (and P NBON 1month ), which is P NBO constructed using only options with one month or less to maturity (Table IA4); (5) subsample returnforecasting regressions with the log dividendprice ratio (Table IA5). 3
4 Table IA1: Correlations of PNBO with Other Variables PNBO: net openbuying volume of DOTM puts (K/S <= 0.85). PNBON: PNBO normalized by past 3month average total options volume from public investors. IP: growth rate of industrial production. Unemploy: unemployment rate. p e: log of price to earning ratio of SP500 stocks. d p: the log of dividend yield of SP500 stocks. ĉay: consumptionwealth ratio. Lev: brokerdealer balance sheet growth of the financial intermediaries. IVSlope: the difference in the implied volatility between onemonth DOTM and ATM SPX puts. Tail: tail risk measure computed from individual stock returns. VP: variance premium. Full sample Precrisis P NBO P NBON P NBO P NBON IP Unemploy d p ĉay Lev IVSlope Tail VP VIX
5 Table IA2: Return Forecasts with P N BO, Statistical Significance This table provides additional results for the statistical significance of the return forecasting regressions using P N BO and P N BON. rt+j t+k represents market excess return from t + j to t + k (k > j 0). Standard errors in parentheses are computed based on Newey and West (1987) with 10 lags. Sq is the statistics based on Muller (2014). The coefficient is significant at 5% if Sq larger than one. Sample period: 1991/ /12. (,, ) denote significance at 1%, 5%, and 10%, respectively based on Newey and West (1987). Return br σ(br) Sq Bootstrap 99% CI br σ(br) Sq Bootstrap 99% CI A: rt+j t+k = ar + br I{bV P,t<0} P NBOt + ɛt+j t+k P NBO P NBON rt t (7.82) 1.2 [47.84, 2.48] (0.28) 1.6 [1.75, 0.03] rt+1 t (10.69) 1.1 [57.87, ] (0.35) 1.6 [1.82, 0.11] rt+2 t (7.45) 0.7 [47.78, 8.95] (0.36) 0.4 [1.49, 0.26] rt+3 t (8.53) 0.3 [42.72, 6.32] (0.29) 0.3 [1.39, 0.33] rt t (20.53) 1.4 [ , ] (0.65) 4.3 [3.81, 1.16] B: rt+j t+k = ar + br I {Jt> J} P NBOt + ɛt+j t+k P NBO P NBON rt t (6.96) 0.9 [47.03, ] (0.39) 0.3 [1.42, 0.19] rt+1 t (8.18) 0.6 [45.24, 7.12] (0.35) 0.7 [1.58, 0.01] rt+2 t (6.81) 0.9 [44.33, ] (0.44) 1.1 [1.87, 0.10] rt+3 t (7.10) 0.3 [29.98, 3.53] (0.30) 0.3 [1.20, 0.41] rt t (17.42) 1.1 [ , ] (1.02) 0.9 [3.97, 0.69] 5
6 Table IA3: Return Forecasts with P NB This table reports the results of the return forecasting regressions using P NB and P NBN. P NB: public net buy volume for DOTM puts (including both open and close transactions). P NBN: P NB normalized by the monthly average public total SPX options volume over the past three months. r t+j t+k represents market excess return from t + j to t + k (k > j 0). Standard errors (σ) in parentheses are computed based on Hodrick (1992). Sample period: 1991/ /12. (,, ) denote significance at 1%, 5%, and 10%, respectively. Return b r σ(b r ) R2 (%) b r σ(b r ) R2 (%) A: r t+j t+k = a r + b r I {bv P,t <0} P NB t + ɛ t+j t+k P NB P NBN r t t (7.37) (0.34) 5.6 r t+1 t (10.22) (0.34) 3.6 r t+2 t (8.93) (0.31) 0.9 r t+3 t (8.26) (0.34) 1.3 r t t (18.00) (0.61) 8.3 B: r t+j t+k = a r + b r I {Jt> J} P NB t + ɛ t+j t+k P NB P NBN r t t (8.06) (0.30) 0.8 r t+1 t (9.67) (0.36) 0.8 r t+2 t (8.59) (0.34) 3.7 r t+3 t (6.36) (0.30) 0.7 r t t (18.04) (0.64) 3.9 6
7 Table IA4: Return Forecasts with Onemonth P N BO This table reports the results of the return forecasting regressions using one month P NBO and P NBON. r t+j t+k represents market excess return from t + j to t + k (k > j 0). Standard errors (σ) in parentheses are computed based on Hodrick (1992). Sample period: 1991/ /12. (,, ) denote significance at 1%, 5%, and 10%, respectively. Return b r σ(b r ) R2 (%) b r σ(b r ) R2 (%) A: r t+j t+k = a r + b r I {bv P,t <0} P NBO t + ɛ t+j t+k P NBO 1month P NBON 1month r t t (6.16) (0.44) 2.6 r t+1 t (14.17) (0.78) 1.6 r t+2 t (11.10) (0.64) 0.0 r t+3 t (9.98) (0.52) 1.8 r t t (27.57) (1.12) 2.0 B: r t+j t+k = a r + b r I {Jt> J} P NBO t + ɛ t+j t+k P NBO 1month P NBON 1month r t t (10.88) (0.69) 0.7 r t+1 t (15.40) (0.82) 3.9 r t+2 t (11.68) (0.68) 2.8 r t+3 t (7.56) (0.62) 0.9 r t t (28.39) (1.53) 5.8 7
8 Table IA5: Return Forecasts with Log DividendPrice Ratio: Subsample Results This table reports the subsample results of the return forecasting regressions using the log dividendprice ratio (d p). r t t+k represents market excess return from month t to t + k. Standard errors (σ) in parentheses are computed based on Hodrick (1992). Sample period: 1991/ /12. (,, ) denote significance at 1%, 5%, and 10%, respectively. Subsample b r σ(b r ) R 2 (%) b r σ(b r ) R 2 (%) obs r t t+k = a r + b r (d t p t ) + ɛ t t+k 3 months 12 months b V P,t < 0, J t J 1.21 (4.69) (11.09) b V P,t < 0, J t < J 6.68 (3.76) (12.39) b V P,t 0, J t J 8.07 (4.08) (12.17) b V P,t 0, J t < J 4.42 (3.60) (12.30)
9 References Chen, Hui, Scott Joslin, and NgocKhanh Tran, 2012, Rare disasters and risk sharing with heterogeneous beliefs, Review of Financial Studies 25, Hodrick, RJ, 1992, Dividend yields and expected stock returns: alternative procedures for inference and measurement, Review of Financial Studies 5, Muller, Ulrich K., 2014, Hac corrections for strongly autocorrelated time series, Journal of Business & Economic Statistics 32, Newey, W. K., and K. D. West, 1987, A simple positive semidefinite, heteroskedasticity and autocorrelation consistent covariance matrix estimator, Econometrica 55,
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