stochastic discount factor
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2022 ◽  
Author(s):  
Po-Hsuan Hsu ◽  
Hsiao-Hui Lee ◽  
Tong Zhou

Patent thickets, a phenomenon of fragmented ownership of overlapping patent rights, hamper firms’ commercialization of patents and thus deliver asset pricing implications. We show that firms with deeper patent thickets are involved in more patent litigations, launch fewer new products, and become less profitable in the future. These firms are also associated with lower subsequent stock returns, which can be explained by a conditional Capital Asset Pricing Model (CAPM) based on a general equilibrium model that features heterogeneous market betas conditional on time-varying aggregate productivity. This explanation is supported by further evidence from factor regressions and stochastic discount factor tests. This paper was accepted by Karl Diether, finance.


2021 ◽  
Vol 14 (3) ◽  
pp. 100
Author(s):  
A. Ronald Gallant ◽  
George Tauchen

We developed a model-free Bayesian extraction procedure for the stochastic discount factor under a yield curve prior. Previous methods in the literature directly or indirectly use some particular parametric asset-pricing models such as with long-run risks or habits as the prior. Here, in contrast, we used no such model, but rather, we adopted a prior that enforces external information about the historically very low levels of U.S. short- and long-term interest rates. For clarity and simplicity, our data were annual time series. We used the extracted stochastic discount factor to determine the stripped cash flow risk premiums on a panel of industrial profits and consumption. Interestingly, the results align very closely with recent limited information (bounded rationality) models of the term structure of equity risk premiums, although nowhere did we use any theory on the discount factor other than its implied moment restrictions.


2021 ◽  
pp. 101000
Author(s):  
David Newton ◽  
Emmanouil Platanakis ◽  
Dimitrios Stafylas ◽  
Charles Sutcliffe ◽  
Xiaoxia Ye

Author(s):  
Mikhail Chernov ◽  
Drew Creal

Abstract Exposures of expected future nominal depreciation rates to the current interest rate differential violate the UIP hypothesis in a pattern that is a nonmonotonic function of horizon. Forward expected nominal depreciation rates are monotonic. We explain the two patterns by simultaneously incorporating the weak form of PPP into a joint model of the stochastic discount factor, the nominal exchange rate, and domestic and foreign yield curves. Departures from PPP generate the first pattern. The risk premiums for these departures generate the second pattern. Thus, the variance of the stochastic discount factor is related to the real exchange rate.


2020 ◽  
Vol 33 (5) ◽  
pp. 1980-2018 ◽  
Author(s):  
Valentin Haddad ◽  
Serhiy Kozak ◽  
Shrihari Santosh

Abstract The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, is more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.


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