A New Example of a Closed Form Mean-Variance Representation

In most finance papers and textbooks mean-variance preferences are usually introduced and motivated as a special case of expected utility theory. In general, the two sufficient conditions to allow this are either quadratic preferences with an arbitrary distribution of stochastic assets, or arbitrary preferences with Normally distributed assets. In the first case, the specific functional form of mean-variance preferences follows naturally. In the second case, the only specific functional form usually provided is the case of negative exponential preferences. In this note, the specific functional form for mean-variance preferences is derived for the much more realistic example of lognormally distributed assets, and constant relative risk aversion (CRRA) preferences.Mean-variance preferences; expected utility; lognormal assets; risk aversion

Barriers To Recovery For Bangor\u27s Buprenorphine Patients

There are several buprenorphine providers at EMMc\u27s Center for Family Medicine serving the greater Bangor, ME region - an area of substantial opiate use. Among the patient population of outpatient buprenorphine users, both locally and nationally, there are high rates of relapse (~32%). In order to decrease relapse rates, it\u27s first imperative to conduct a baseline review of the current buprenorphine population to identify specific types of patients who are at higher risk of relapse. By understanding the barriers to recovery, the office hopes to apply an intervention to the current program, targeting this local demographic more effectively.https://scholarworks.uvm.edu/fmclerk/1098/thumbnail.jp

The Econometric Specification of Input Demand Systems Implied by Cost Function Representations

In the case of input demand systems based on specification of technology by a Translog cost function, it is common to estimate either a system of share equations alone, or to supplement them by the cost function. By adding up, one of the share equations is excluded. In this paper it is argued that a system of n-1 share equations is essentially incomplete, whereas if the n-1 share equations are supplemented by the cost function the implied error structure is inadmissible. Similarly, if the technology is specified by a normalized quadratic cost function, it is common to estimate either a system of n-1 demand equations alone, or to supplement them by the cost function. In both cases, the implied error structure is again inadmissible.Cost Function; Input demands; Share equations; Translog; Normalized Quadratic; Error specification.

Regular and Estimable Inverse Demand Systems: A Distance Function Approach

To be useful for realistic policy simulation in an environment of rapid structural change, inverse demand systems must remain regular over substantial variations in quantities. The distance function is a convenient vehicle for generating such systems. While it directly yields Hicksian inverse demand functions, those functions will not usually have an explicit representation in terms of the observable variables. Note however that this problem need not hinder estimation and could be solved by using the numerical inversion estimation approach. This paper develops the formal theory for using distance functions in this context, and demonstrates the operational feasibility of the method.Inverse Demands; Distance Functions; Numerical Inversion Estimation Method; Separability.

How we Can change your mind: Anodal tDCS to Fp3 alters human stimulus representation and learning.

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The aim of the current work is to advance our understanding of both the mechanisms controlling perceptual learning and the face inversion effect. In the three double blind experiments reported here (total N=144) we have shown that anodal tDCS stimulation (10 mins at 1.5 mA) delivered over the left DLPFC at Fp3 affects perceptual learning and drastically reduces the, usually robust, face inversion effect. In Experiment 1, we found a significantly reduced inversion effect in the anodal group compared to that in the sham group. Experiment 2 replicated the pattern of results found in Experiment 1. In both experiments recognition performance for upright faces in the anodal group was significantly impaired compared to that in the sham group. Finally, using an active control in Experiment 3 (same behavioural task but different tDCS targeted brain area) we showed that the same Fp3 anodal tDCS stimulation effect is not obtained when a different brain area is targeted.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 743702 awarded to Ciro Civile. This project has also received funding from the Economic and Social Research Council (ESRC) New Investigator Grant (Ref. ES/R005532) awarded to Ciro Civile (PI) and I.P.L. McLaren (Co-I)

The Face Inversion Effect: Roles of First and Second-Order Configural Information

PublishedThe face inversion effect (FIE) is a reduction in recognition performance for inverted faces compared with upright faces. Several studies have proposed that a type of configural information, called second-order relational information, becomes more important with increasing expertise and gives rise to the FIE. However, recently it has been demonstrated that it is possible to obtain an FIE with facial features presented in isolation, showing that configural information is not necessary for this effect to occur. In this article we test whether there is a role for configural information in producing the FIE and whether second- or first-order relational information is particularly important. In Experiment 1, we investigated the role of configural information and local feature orientation by using a new type of “Thatcherizing” transformation on our set of faces, aiming to disrupt second-order and local feature orientation information but keeping all first-order properties unaltered. The results showed a significant reduction in the FIE for these “new” Thatcherized faces, but it did not entirely disappear. Experiment 2 confirmed the FIE for new Thatcherized faces, and Experiment 3 establishes that both local feature orientation and first-order relational information have a role in determining the FIE

The Benefit Function Approach to Modeling Price-Dependent Demand Systems: An Application of Duality Theory

In this article we advocate more extensive use of the benefit function in specifying price-dependent or inverse demand models. In particular, we demonstrate how duality theory may be used to establish the inter-relationships between the Marshallian (or Hicksian) inverse demands and Luenberger's adjusted price functions, allowing estimable inverse demands to be derived directly from a benefit function. We also make use of a numerical inversion estimation method to rectify the "unobservability of utility problem" encountered in the empirical analysis of these inverse demands. To illustrate the usefulness of the proposed methods, we estimate two systems of inverse demands for Japanese quarterly fish consumption. Results generally indicate that the proposed methods are promising and operationally feasible so that we have opened up a wider range of empirical inverse demand specifications that can be subjected to tight theoretical restrictions.Benefit Functions; Duality Theory; Numerical Inversion Estimation Method

Nonsimultaneity and Futures Option Pricing: Simulation and Empirical Evidence

Empirical tests of option pricing models are joint tests of the 'correctness' of the model, the efficiency of the market and the simultaneity of price observations. Some degree of nonsimultaeity can be expected in all but the most liquid markets and is therefore evident in many non-US markets. Simulation results indicate that nonsimultaneity is potentially a significant problem in empirical tests of futures option pricing models. Empirical results using Australian data show that a five-minute window for matching transactions does not remove the nonsimultaneity bias for near-the-money and out-of-the money options. A more accurate matching may therefore be required. The nonsimultaneity bias is effectively removed if a five-minute window is employed for in-the-money options.Nonsimultaneity; Futures option; Mispricing