680 research outputs found
Optimal Dynamic Risk Sharing when Enforcement is a Decision Variable
Societies provide institutions that are costly to set up, but able to enforce long-run relationships. We study the optimal decision problem of using self-governance for risk sharing or governance through enforcement provided by these institutions. Third-party enforcement is modelled as a costly technology that consumes resources, but permits the punishment of agents who deviate from ex-ante specified allocations. We show that it is optimal to employ the technology whenever commitment problems prevent first-best risk sharing, but never optimal to provide incentives exclusively via this technology. Commitment problems then persist and the optimal incentive structure changes dynamically over time with third-party enforcement monotonically increasing in the relative inequality between agents.Limited Commitment, Risk Sharing, Third-party Enforcement
Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments
The dynamics of stochastic reaction networks within cells are inevitably
modulated by factors considered extrinsic to the network such as for instance
the fluctuations in ribsome copy numbers for a gene regulatory network. While
several recent studies demonstrate the importance of accounting for such
extrinsic components, the resulting models are typically hard to analyze. In
this work we develop a general mathematical framework that allows to uncouple
the network from its dynamic environment by incorporating only the
environment's effect onto the network into a new model. More technically, we
show how such fluctuating extrinsic components (e.g., chemical species) can be
marginalized in order to obtain this decoupled model. We derive its
corresponding process- and master equations and show how stochastic simulations
can be performed. Using several case studies, we demonstrate the significance
of the approach. For instance, we exemplarily formulate and solve a marginal
master equation describing the protein translation and degradation in a
fluctuating environment.Comment: 7 pages, 4 figures, Appendix attached as SI.pdf, under submissio
Moment-Based Variational Inference for Markov Jump Processes
We propose moment-based variational inference as a flexible framework for
approximate smoothing of latent Markov jump processes. The main ingredient of
our approach is to partition the set of all transitions of the latent process
into classes. This allows to express the Kullback-Leibler divergence between
the approximate and the exact posterior process in terms of a set of moment
functions that arise naturally from the chosen partition. To illustrate
possible choices of the partition, we consider special classes of jump
processes that frequently occur in applications. We then extend the results to
parameter inference and demonstrate the method on several examples.Comment: Accepted by the 36th International Conference on Machine Learning
(ICML 2019
How Flexible Can Inflation Targeting Be? Suggestions for the Future of Canada's Targeting Regime
In Canada, inflation targeting is widely agreed to have been a success story, but questions about how the regime might be fine-tuned in 2011 remain open. This Commentary asks how much discretion an inflation-targeting Bank of Canada can be allowed without compromising the credibility of its low inflation goal.monetary policy, Bank of Canada, inflation targeting
Optimal dynamic risk sharing when enforcement is a decision variable
JEL Classification: C73, D60, D91, K49dynamic risk sharing, enforcement
Central counterparties
Central counterparties (CCPs) have increasingly become a cornerstone of financial markets infrastructure. We present a model where trades are time-critical, liquidity is limited and there is limited enforcement of trades. We show a CCP novating trades implements efficient trading behaviour. It is optimal for the CCP to face default losses to achieve the efficient level of trade. To cover these losses, the CCP optimally uses margin calls, and, as the default problem becomes more severe, also requires default funds and then imposes position limits
A variational approach to path estimation and parameter inference of hidden diffusion processes
We consider a hidden Markov model, where the signal process, given by a
diffusion, is only indirectly observed through some noisy measurements. The
article develops a variational method for approximating the hidden states of
the signal process given the full set of observations. This, in particular,
leads to systematic approximations of the smoothing densities of the signal
process. The paper then demonstrates how an efficient inference scheme, based
on this variational approach to the approximation of the hidden states, can be
designed to estimate the unknown parameters of stochastic differential
equations. Two examples at the end illustrate the efficacy and the accuracy of
the presented method.Comment: 37 pages, 2 figures, revise
The Emergence and Future of Central Counterparties
We study the role of a central counterparty (CCP) in controlling counterparty risk. When trading is organized via a centralized exchange with fungible contracts -- as in a futures market -- we show that it is optimal to clear trades via a CCP that uses (i) novation to pool the risk of default and (ii) mutualization of losses to insure against the aggregate cost of default in the form of price risk. We then analyze the design of CCP clearing for over-the-counter (OTC) trades where contracts are customized and, hence, not fungible. A CCP can still offer gains from novation by pooling default risk across all customized contracts. Bargaining in OTC trades leads to an inefficient allocation of default risk across trades. A transfer scheme can alleviate this inefficiency, but necessitates novation being offered by a CCP. Hence, the benefit from CCP clearing for OTC markets goes beyond simple netting as it is a prerequisite for an efficient allocation of default risk in such markets.Central Counterparty, Clearing, Over-the-counter Markets, Novation and Mutualization, Default Risk
The emergence and future of central counterparties
The authors explain why central counterparties (CCPs) emerged historically. With standardized contracts, it is optimal to insure counterparty risk by clearing those contracts through a CCP that uses novation and mutualization. As netting is not essential for these services, it does not explain why CCPs exist. In over-the-counter markets, as contracts are customized and not fungible, a CCP cannot fully guarantee contract performance. Still, a CCP can help: As bargaining leads to an inefficient allocation of default risk relative to the gains from customization, a transfer scheme is needed. A CCP can implement it by offering partial insurance for customized contracts.Risk management ; Over-the-counter markets ; Contracts
Evolution of Feedback Loops in Oscillatory Systems
Feedback loops are major components of biochemical systems. Many systems show
multiple such (positive or negative) feedback loops. Nevertheless, very few
quantitative analyses address the question how such multiple feedback loops
evolved. Based on published models from the mitotic cycle in embryogenesis, we
build a few case studies. Using a simple core architecture (transcription,
phosphorylation and degradation), we define oscillatory models having either
one positive feedback or one negative feedback, or both loops. With these
models, we address the following questions about evolvability: could a system
evolve from a simple model to a more complex one with a continuous transition
in the parameter space? How do new feedback loops emerge without disrupting the
proper function of the system? Our results show that progressive formation of a
second feedback loop is possible without disturbing existing oscillatory
behavior. For this process, the parameters of the system have to change during
evolution to maintain predefined properties of oscillations like period and
amplitude.Comment: Proceedings of the 2009 FOSBE conference in Denver, CO, USA. 4 page
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