Conditional Distribution Approach Research Articles

The Effects of Environmental Regulation on the Singapore Stock Market

This study examines the impact of environmental regulation on the Singapore stock market using the event study methodology. Several asset pricing models are used to estimate sectoral abnormal returns. Additionally, we estimate the change in systematic risk after the introduction of the carbon tax and related regulation. We conduct various robustness tests, including the Corrado non-parametric ranking test, the Chesney non-parametric conditional distribution approach, a representation of market integration, and Fama–French five-factor model. We find evidence showing that the environmental regulations tend to achieve their desired effects in Singapore in which several big polluters (including industrial metals and mining, forestry and papers, and electrical equipment and services) were negatively affected by the announcements of environmental regulations and carbon tax. In addition, our results indicate that the electricity sector, one of the biggest polluters, was negatively affected by the announcement of environmental regulations and carbon tax. We also find that environmental regulations seem to boost the performance of environmentally-friendly sectors whereby we find the alternative energy industry (focusing on new renewable energy technologies) experienced a sizeable positive reaction following the announcements of these regulations.

The effects of the Paris climate agreement on stock markets: evidence from the German stock market

ABSTRACTWe investigate the effects of the 2016 Paris Climate Agreement on the German stock market by considering the impact of 20 announcements pertaining to the Agreement on 17 industries. The event study methodology is used for this purpose, together with several robustness tests, such as the nonparametric rank test and non-parametric conditional distribution approach. The change in systematic risk following the announcements is captured by using various risk models. In general, we find that the Paris Climate Agreement is achieving its objectives in the short run. Our results show that the announcements affected polluting industries in terms of risk and return. Furthermore, we observe two distinct diamond risk structures when (1) Conference of the Parties (COP) 21 took place, and (2) the Agreement came into force.

The financial effects of Trumpism

This paper investigates the effects of the 2016 US presidential election, and the events occurring in the run-up to election day, on the US stock market. We examine 47 events, starting with Donald Trump's announcement that he would contest the presidential election, using the event study methodology and asset pricing models. Furthermore, we conduct several robustness tests, including the Corrado ranking test, the non-parametric conditional distribution approach, adjustment for market integration, the removal of firms with firm-specific news, and the use of more than one asset pricing model. We observe that the US stock market was affected by the 2016 presidential election and that the US stock market was highly responsive when Trump secured the Republican nomination. The life insurance sector was one of the most negatively affected sectors due to Trump's intention to replace Obamacare. In addition, our results show that the events around the most recent election can lead to diamond risk structures.

Nonlinear relationship between crude oil price and net futures positions: A dynamic conditional distribution approach

This study examines the dynamic relationship between crude oil prices and net futures positions using a dynamic conditional density that can take account of time-varying bimodality. The shape of conditional density is modeled directly by specifying functional coefficients. We find that when the crude oil price is on the rise (decline), speculators tend to take long (short) positions to make profits and hedgers tend to take short (long) positions to cover the risk in the physical market. On the other hand, speculators have a positive effect on the price whereas hedgers have a negative effect. Therefore, when the price is on the rise (decline), speculators tend to push it up (pull it down) while hedgers tend to pull it down (push it up). This effect becomes stronger in the recent period. Moreover, the sharp increase of the crude oil price can be explained by speculating and hedging behavior through conditional higher-order moments.

Secure attribute sharing of linked microdata

Two organizations that have records on the same collection of individuals can benefit from sharing attributes on these individuals. The combined data, with records linked on certain common identifying information, is termed linked microdata. Linked microdata attributes can add considerable value to organizations by enabling them to perform analysis that can provide important information on individual (or record-level) data items. We illustrate practical examples of the need and benefits of sharing linked microdata and identify important privacy issues relating to this context. Based on a conditional distribution approach, we develop a procedure (SASH) for sharing masked attributes in linked microdata that addresses these privacy issues. Our experimental results show that SASH achieves a priori expectations of analytical usefulness, without either party having to provide true values of attribute data. Our results also show that an ad hoc approach such as data swapping, cannot achieve privacy without sacrificing usefulness or vice versa. Our study should provide immediate practical benefits to organizations interested in secure attribute sharing of linked microdata.

Modeling Conditional Distributions for Functional Responses, With Application to Traffic Monitoring via GPS-Enabled Mobile Phones

Motivated by problems involving a traffic monitoring system in which trajectory data are obtained from Global Positioning System-enabled mobile phones, we propose a novel approach to functional regression modeling, where instead of the usual mean regression the entire distribution of functional responses is modeled conditionally on predictors. An approach that sensibly balances flexibility and stability is obtained by assuming that the response functions are drawn from a Gaussian process, the mean and covariance function of which depend on predictors. The dependence of the mean function and covariance function of the response on the predictors is modeled additively. We demonstrate the proposed methods by constructing predicted curves and corresponding prediction regions for traffic velocity trajectories for a future time period, using current traffic velocity fields as predictor functions. The proposed functional regression and conditional distribution approach is of general interest for functional response settings, where in addition to predicting the conditional mean response function one is also interested in predicting the covariance surface of the random response functions, conditional on predictor curves.

Impacts of Weather and Time Horizon Selection on Crop Insurance Ratemaking: A Conditional Distribution Approach

An important issue in the agricultural risk and actuarial literatures is the extent to which sample period selection affects the accuracy of insurance rating. This is typically more problematic in agriculture than in other types of insurance because of the variation in weather through time. A conditional Weibull distribution approach is developed that explicitly models the interaction of weather, technology, and other variables on probabilistic yield outcomes to address this issue. Results from an application with an extensive producer-level yield dataset representing commercial-scale Illinois firms suggest that the impact of weather heterogeneity on risk estimation across reasonable samples is likely not as great as is often claimed. The results also suggest that yield risk is decreasing significantly through time and indicate the presence of trend acceleration. A rating analysis indicates that violations in the risk evolution assumptions of the rating approaches used in the Federal Crop Insurance Program—which implicitly assume increasing yield risk through time when yields trend—result in severely biased rates, with typical overstatements of 200% to 400% for Midwest corn.

A conditional distribution approach to uniform sampling on spheres and balls in Lp spaces

Liang and Ng (Metrika 68:83–98, 2008) proposed a componentwise conditional distribution method for Lp-uniform sampling on Lp-norm n-spheres. On the basis of properties of a special family of Lp-norm spherical distributions we suggest a wide class of algorithms for sampling uniformly distributed points on n-spheres and n-balls in Lp spaces, generalizing the approach of Harman and Lacko (J Multivar Anal 101:2297–2304, 2010), and including the method of Liang and Ng as a special case. We also present results of a numerical study proving that the choice of the best algorithm from the class significantly depends on the value of p.

Tools for Sampling Multivariate Archimedean Copulas

A hurdle for practical implementation of any multivariate Archimedean copula was the absence of an efficient method for generating them. The most frequently used approach named conditional distribution one, involves differentiation step for each dimension of the problem. For this reason, it is not feasible in higher dimension. Marshall and Olkin proposed an alternative method, which is computationally more straightforward than the conditional distribution approach. We present the tools necessary for understand it and use it. We introduce the Laplace Transform and its role in the generation of multivariate Archimedean copulas. In order to cover the gap between the theory and its practical implementation VBA code and R one are provided.

Simulation of random vectors with given moments

We extend in this article the simulation method of scalar random variables with given moments, which was developed initially by Devroye, to n-dimensional random vectors. Using the conditional distribution approach, we show that the vector simulation problem can be reduced to simulate scalar random variables with given moments, which are solutions of given linear systems.

A Multi-Disciplinary Approach for Reservoir Characterization in the Glenn Pool Field, Oklahoma: ABSTRACT

Abstract Glenn Pool, a mature marginal oil field, is located on the Northeastern Oklahoma Platform. It has been under production since the first discovery in 1905. Several reservoir treatments have been implemented in the last 50 years following the primary production. However, high reservoir complexity resulted in a large volume of oil to be left in place. Our research is focused on a 160-acre block (Self Unit) where a detailed multi-disciplinary (Geology, Geophysics, Petroleum Engineering) reservoir study was conducted. The purpose of the study is to improve the secondary recovery performance of the field through the use of proper reservoir description and better reservoir management. Prior to drilling a cooperative project well (Uplands Self #82) Self Unit was studied with conventional methods. Stratigraphic framework of the Glenn Sand reservoir has been established through a series of stratigraphic cross sections. Based on well log correlations the reservoir was divided into six discrete genetic intervals (DGI). Channel-fill, splay, channel-mouth bar, levee and interdistributary mudstone facies were recognized from well log profiles and core analysis. Attempts were made in simulating geology using simple kriging methods; results were not entirely satisfactory. Uplands Self #82 was drilled in late December, 1993. The project objectives for drilling the well were: 1) evaluate reservoir predictions; 2) collect data using conventional and advanced technologies. Facies architectural characterization before drilling was reasonably successful. Advanced technologies including microresistivity imaging log and crosswell tomography data were acquired as well as the conventional well log suites and core. Simulation of the DGI distribution was undertaken using truncated Gaussian simulation method. Simulation results strongly agree when comparing probability input distributions with the output distributions. Porosity distribution was simulated using the simulated annealing method, and permeability distribution was transformed from porosity using a conditional distribution approach. For a selected well location (Self #82) the comparison between simulated and core porosity/permeability is very good. Crosswell transmission and migrated reflection tomography images between Self #82 and three offset wells constrained lateral reservoir continuity. A reservoir management plan was developed from reservoir performance simulation, well test data, facies architecture and crosswell tomography.