Status Quo Effects In Bargaining: An Empirical Analysis of OPEC

We conduct an event analysis on OPEC quota announcements to determine their impact on the stock returns in the oil industry. We find that announcements to reduce the quota are followed by positive excess returns over pre-announcement levels, announcements of no action are met with negative excess returns and announcements to increase the quota have no significant impact on stock market returns. This suggests that there is an asymmetric ability on the part of OPEC to secure agreements. In particular, when demand has increased, agreements are easily forthcoming, while when times are bad the probability of a disagreement is substantially higher. We present further empirical as well as anecdotal evidence to support our interpretation. Finally, we present two simple models of asymmetric information which make predictions consistent with our empirical findings. In the first model, disagreements arise due to a perceived lack of commitment to the agreed upon quota due to the possibility of random shocks. The second model takes a behavioural approach; in particular, disagreements arise because players place more emphasis on their individual quotas than strict profit maximisation dictatesevent study, OPEC, status quo effects

Valuation Perspectives and Decompositions for Variable Annuities with GMWB riders

The guaranteed minimum withdrawal benefit (GMWB) rider, as an add on to a variable annuity (VA), guarantees the return of premiums in the form of peri- odic withdrawals while allowing policyholders to participate fully in any market gains. GMWB riders represent an embedded option on the account value with a fee structure that is different from typical financial derivatives. We consider fair pricing of the GMWB rider from a financial economic perspective. Particular focus is placed on the distinct perspectives of the insurer and policyholder and the unifying relationship. We extend a decomposition of the VA contract into components that reflect term-certain payments and embedded derivatives to the case where the policyholder has the option to surrender, or lapse, the contract early.Comment: 18 pages, proof of Lemma A.1 expanded for clarit

Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free Regularization

We introduce a regularization approach to arbitrage-free factor-model selection. The considered model selection problem seeks to learn the closest arbitrage-free HJM-type model to any prespecified factor-model. An asymptotic solution to this, a priori computationally intractable, problem is represented as the limit of a 1-parameter family of optimizers to computationally tractable model selection tasks. Each of these simplified model-selection tasks seeks to learn the most similar model, to the prescribed factor-model, subject to a penalty detecting when the reference measure is a local martingale-measure for the entire underlying financial market. A simple expression for the penalty terms is obtained in the bond market withing the affine-term structure setting, and it is used to formulate a deep-learning approach to arbitrage-free affine term-structure modelling. Numerical implementations are also performed to evaluate the performance in the bond market.Comment: 23 Pages + Reference

A Fourier interpolation method for numerical solution of FBSDEs: Global convergence, stability, and higher order discretizations

The implementation of the convolution method for the numerical solution of backward stochastic differential equations (BSDEs) introduced in [19] uses a uniform space grid. Locally, this approach produces a truncation error, a space discretization error, and an additional extrapolation error. Even if the extrapolation error is convergent in time, the resulting absolute error may be high at the boundaries of the uniform space grid. In order to solve this problem, we propose a tree-like grid for the space discretization which suppresses the extrapolation error leading to a globally convergent numerical solution for the (F)BSDE. On this alternative grid the conditional expectations involved in the BSDE time discretization are computed using Fourier analysis and the fast Fourier transform (FFT) algorithm as in the initial implementation. The method is then extended to higher-order time discretizations of FBSDEs. Numerical results demonstrating convergence are also presented.Comment: 28 pages, 8 figures; Previously titled 'Global convergence and stability of a convolution method for numerical solution of BSDEs' (1410.8595v1

Nonparametric Estimation and Symmetry Tests for Conditional Density Functions.

We suggest two new methods for conditional density estimation. The first is based on locally fitting a log-linear model, and is in the spirit of recent work on locally parametric techniques in density estimation. The second method is a constrained local polynomial estimator. Both methods always produce non-negative estimators. We propose an algorithm suitable for selecting the two bandwidths for either estimator. We also develop a new bootstrap test for the symmetry of conditional density functions. The proposed methods are illustrated by both simulation and application to a real data set.TESTING ; STATISTICAL ANALYSIS ; ESTIMATION OF PARAMETERS

Short-term load forecasting based on a semi-parametric additive model

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.Short-term load forecasting, additive model, time series, forecast distribution

Statistical Methodological Issues in Studies of Air Pollution and Respiratory Disease.

Epidemiological studies have consistently shown short term associations between levels of air pollution and respiratory disease in countries of diverse populations, geographical locations and varying levels of air pollution and climate. The aims of this paper are: (1) to assess the sensitivity of the observed pollution effects to model specification, with particular emphasis on the inclusion of seasonally adjusted covariates; and (2) to study the effect of air pollution on respiratory disease in Melbourne, Australia.Air pollution; Autocorrelation; Generalized additive models; Seasonal adjustment; Respiratory disease