Location of Investors and Capital Flight

This paper utilizes a very simple model to study the timing and determinants of speculationagainst a fixed exchange rate regime when investors are heterogeneous because of locationaldifferences. Location matters because resident players may incur smaller costs when takinga short-position, are less exposed to exchange rate risk, possess better information quality,have more knowledge about each others information sets, due to asymmetries in tax treatment,or because of the presence of government guarantees. Our model clarifies the respective rolesplayed by local and international investors during episodes of capital flight as well as theresulting room of maneuver for policymakers in emerging markets.Locational heterogeneity; Private information; Exchange rate volatility; Illiquidity; Capital flight

Improved treatment of uncertainty in hydrologic modelling: combining the strengths of global optimisation and data assimilation

Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must therefore be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. In practice, however, because of errors in the model structure and the input (forcing) and output data, this has proven to be difficult, leading to considerable uncertainty in the model predictions. This paper surveys the limitations of current model calibration methodologies, which treat the uncertainty in the input-output relationship as being primarily attributable to uncertainty in the parameters and presents a simultaneous optimization and data assimilation (SODA) method, which improves the treatment of uncertainty in hydrologic modeling. The usefulness and applicability of SODA is demonstrated by means of a pilot study using data from the Leaf River watershed in Mississippi and a simple hydrologic model with typical conceptual components