High Heterogeneous Information and Investment under Uncertainty

A sudden change in investment environment shifts objective uncertainty (characterized by parameters that determine the distribution of returns) and at the same time heightens subjective uncertainty (about the data generating parameters) unevenly across investors. For a given state of economy, the uncertainty facing the investor is the sum of the uncertainty in the data and the uncertainty of the investors assessment of the expected return distribution. In this model the option value of waiting to invest depends not only on the objective uncertainty as in the traditional theory but varies systematically with investor information and Bayesian updating of outlook for the project. Simulation of the model suggests that during a state characterized by greater uncertainty and higher potential expected return investment will be by an abnormally high percentage of informed investors and may increase overall. For over 10,000 instances of firm-level FDI data for Korea from 1996 to 2001, regression results are consistent with the hypothesis that disproportionably more FDI is made by experienced (hence more informed) investors during heightened uncertainty.uncertainty, investor information, option value, Bayesian updating, FDI

Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.Adaptation to Climate Change, Bayesian Network, Uncertainty

Structural instrumentation and monitoring of the Block Island Offshore Wind Farm

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Heterogeneous Information and Investment under Uncertainty

A sudden change in investment environment shifts objective uncertainty (characterized by parameters that determine the distribution of returns) and at the same time heightens subjective uncertainty (about the data generating parameters) unevenly across investors. For a given state of economy, the uncertainty facing the investor is the sum of the uncertainty in the data and the uncertainty of the investor's assessment of the expected return distribution. In this model the option value of waiting to invest depends not only on the objective uncertainty as in the traditional theory but varies systematically with investor information and Bayesian updating of outlook for the project. Simulation of the model suggests that during a state characterized by greater uncertainty and higher potential expected return investment will be by an abnormally high percentage of informed investors and may increase overall. For over 10,000 instances of firm-level FDI data for Korea from 1996 to 2001, regression results are consistent with the hypothesis that disproportionably more FDI is made by experienced (hence more informed) investors during heightened uncertainty

Particle filter-based Gaussian process optimisation for parameter inference

We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using these log-likelihood estimates, a surrogate objective function is created by utilizing a Gaussian process model. Finally, we use a heuristic procedure to obtain a revised parameter iterate, providing an automatic trade-off between exploration and exploitation of the surrogate model. The method is profiled on two state space models with good performance both considering accuracy and computational cost.Comment: Accepted for publication in proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, August 2014. 6 pages, 4 figure

Components of the Czech Koruna Risk Premium in a Multiple-Dealer FX Market

The paper proposes a continuous time model of an FX market organized as a multiple dealership. The model reflects a number of salient features of the Czech koruna spot market. The dealers have costly access to the best available quotes. They interpret signals from the joint dealer-customer order flow and decide upon their own quotes and trades in the inter-dealer market. Each dealer uses the observed order flow to improve the subjective estimates of the relevant aggregate variables, which are the sources of uncertainty. One of the risk factors is the size of the cross-border dealer transactions in the FX market. These uncertainties have diffusion form and are dealt with according to the principles of portfolio optimization in continuous time. The model is used to explain the country, or risk, premium in the uncovered national return parity equation for the koruna/euro exchange rate. The two country premium terms that I identify in excess of the usual covariance term (a consequence of the 'Jensen inequality effect') are the dealer heterogeneity-induced inter-dealer market order flow component and the dealer Bayesian learning component. As a result, a 'dealer-based total return parity' formula links the exchange rate to both the 'fundamental' factors represented by the differential of the national asset returns, and the microstructural factors represented by heterogeneous dealer knowledge of the aggregate order flow and the fundamentals. Evidence on the cross-border order flow dependence of the Czech koruna risk premium, in accordance with the model prediction, is documented.Bayesian learning, FX microstructure, optimizing dealer, uncovered parity.

Practical guidelines for modelling post-entry spread in invasion ecology

In this article we review a variety of methods to enable understanding and modelling the spread of a pest or pathogen post-entry. Building upon our experience of multidisciplinary research in this area, we propose practical guidelines and a framework for model development, to help with the application of mathematical modelling in the field of invasion ecology for post-entry spread. We evaluate the pros and cons of a range of methods, including references to examples of the methods in practice. We also show how issues of data deficiency and uncertainty can be addressed. The aim is to provide guidance to the reader on the most suitable elements to include in a model of post-entry dispersal in a risk assessment, under differing circumstances. We identify both the strengths and weaknesses of different methods and their application as part of a holistic, multidisciplinary approach to biosecurity research