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Articles by Q. Dai
Total Records ( 2 ) for Q. Dai
  Z Liu , Z Yu , N Liu , C Zhao , J Hu and Q. Dai
 

In our efforts for cloning novel I2-superfamily conotoxins using the signal peptide sequence, we identified a novel conotoxin Lt12.4 from Conus litteratus. This gene has a framework XII (-C-C-C-C-CC-C-C-), which is distinct from the cysteine pattern I2-superfamily conotoxin (-C-C-CC-CC-C-C-). Subsequently, we found the signal peptide sequence of Lt12.4 by 5'-RACE. Using this new sequence, we identified another five novel conotoxins with this cysteine pattern from four Conus species (Conus eburneus, Conus imperialis, Conus marmoreus, and C. litteratus). These novel conotoxins have the same cysteine pattern as the reported Gla-TxX and Gla-MII, and may contain Gla residues. Furthermore, they have the highly conserved signal peptide and hypervariable mature peptide sequences, and widely exist in Conus species. Therefore, it could be defined as a new superfamily of E-conotoxins.

  A Le , K. J Singleton and Q. Dai
 

This article develops a rich class of discrete-time, nonlinear dynamic term structure models (DTSMs). Under the risk-neutral measure, the distribution of the state vector Xt resides within a family of discrete-time affine processes that nests the exact discrete-time counterparts of the entire class of continuous-time models in Duffie and Kan (1996) and Dai and Singleton (2000). Under the historical distribution, our approach accommodates nonlinear (nonaffine) processes while leading to closed-form expressions for the conditional likelihood functions for zero-coupon bond yields. As motivation for our framework, we show that it encompasses many of the equilibrium models with habit-based preferences or recursive preferences and long-run risks. We illustrate our methods by constructing maximum likelihood estimates of a nonlinear discrete-time DTSM with habit-based preferences in which bond prices are known in closed form. We conclude that habit-based models, as typically parameterized in the literature, do not match key features of the conditional distribution of bond yields.

 
 
 
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