This paper examines the pricing of dynamic horizon specific network risk in the cross-section of stock returns. We suggest how to track such dynamic network connections on a daily basis using time-varying parameter vector auto-regressions. Empirically, we characterize the short-term and long-term risks from a large-scale dynamic network on all S P500 constituents' return volatilities. Consistent with theory, we show that stocks with high sensitivities to dynamic network risk earn lower returns. A two-standard deviation increase in long-term (short-term) network risk loadings associate with a 14.73\% (12.96 \%) drop in annualized expected returns.
This paper examines the pricing of short-term and long-term dynamic network risk in the cross-section of stock returns. Stocks with high sensitivities to dynamic network risk earn lower returns. We rationalize our finding with economic theory that allows the stochastic discount factor to load on network risk through the precautionary savings channel. A one-standard deviation increase in long-term (short-term) network risk loadings associate with a 7.66\% (6.71 \%) drop in annualized expected returns.
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dynamic horizon specific
short-term and long-term dynamic
D
We suggest how to track such dynamic network connections on a daily basis using time-varying parameter vector auto-regressions. Empirically, we characterize the short-term and long-term risks from a large-scale dynamic network on all S
R
P500 constituents' return volatilities. Consistent with theory, we show that stocks with
Stocks with
R
A two-standard
We rationalize our finding with economic theory that allows the stochastic discount factor to load on network risk through the precautionary savings channel. A one-standard
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14.73\% (12.96
7.66\% (6.71