We study the intra-horizon value at risk (iVaR) in a general jump diffusion setup and propose a new model of asset returns called displaced mixed-exponential model, which can arbitrarily closely approximate finite-activity jump-diffusions and completely monotone Levy processes. We derive analytical results for the iVaR and disentangle the risk contribution of jumps from diffusion. Estimating the iVaR for several popular jump models using on S&P 100 option data, we find that option-implied estimates are much more responsive to market changes relative to their historical counterparts. Moreover, disentangling jumps from diffusion, jump account for about 90 percent of iVaR on average.
We propose a general particle filtering and learning framework for mixed-frequency state-space models. Our mixed-frequency particle methods use a smoother so as to draw the Bayesian in- ference from low-frequency observations. Our forward smoother is simple and efficient, and the sample path degeneracy is negligible with a small lag size. The backward smoother mitigates the sample path degeneracy effect with quadratic computations that are nevertheless parallelizable. To illustrate our mixed-frequency particle framework, we take the mixed-frequency conditional dynamic linear model with regime switching as an example. In a simulation study, we show that naive treatments of mixed frequencies may severely impact model identification.
We study loan conditions when bank branches close and firms subsequently transfer to a branch of another bank in the vicinity. Such transfer loans allow us for the first time to observe the conditions granted when banks pool-price new applicants. Consistent with recent theoretical work on hold up in bank-firm relationships we find that transfer loans do not receive the discount in loan rates that prevails when firms otherwise switch banks. We hereby critically augment recent empirical evidence on dynamic cycles in loan rates.
We find that stricter merger control legislation increases abnormal announcement returns of targets in bank mergers by 7 percentage points. Analyzing potential explanations for this result, we document an increase in the pre-merger profitability of targets, a decrease in the size of acquirers and a decreasing share of transactions in which banks are acquired by other banks. Other merger properties, including the size and risk profile of targets, the geographic overlap of merging banks and the stock market response of rivals appear unaffected. The evidence suggests that the strengthening of merger control leads to more efficient and more competitive transactions.
Individuals differ profoundly when they decide whether to tell the truth or to be dishonest, particularly in situations where moral motives clash with economic motives, i.e., when truthfulness comes at a monetary cost. These differences should be expressed in the decision network, particularly in prefrontal cortex. However, the interactions between the core players of the decision network during honesty-related decisions involving trade-offs with economic costs remain poorly understood. To investigate brain connectivity patterns associated with individual differences in responding to economic costs of truthfulness, we used functional magnetic resonance imaging and measured brain activations while participants made decisions concerning honesty. We found that in participants who valued honesty highly, dorsolateral and dorsomedial parts of prefrontal cortex were more tightly coupled with the inferior frontal cortex when economic costs were high compared to when they were low. Finer-grained analysis revealed that information flow from the inferior frontal cortex to the dorsolateral prefrontal cortex and bidirectional information flow between the inferior frontal cortex and dorsomedial prefrontal cortex was associated with a reduced tendency to trade off honesty for economic benefits. Our findings provide a novel account of the neural circuitry that underlies honest decisions in the face of economic temptations.
In this paper, a new technique for pricing of European and American Parisian options, that we call the québécoisation method, is developed. We study the pricing of Parisian options in a hyper-exponential jump-diffusion model using the double Laplace-Carson transform with respect to the time to maturity and the residual Parisian time (time to expiration of the Parisian window) of the system of two partial integro-differential equations describing the option price dynamics. The transformed, i.e., québécoised, option price and hedging parameters delta and gamma are computed in a closed form, and the final results are obtained via the two-dimensional Gaver-Stehfest inversion algorithm. Our pricing method is analytically tractable, and it provides important economic insights for pricing and hedging of European and American Parisian options in the presence of jumps.
