An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

2017 ◽  
Vol 12 (3) ◽  
pp. 252-266 ◽  
Author(s):  
Don Cyr ◽  
Lester Kwong ◽  
Ling Sun
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Tail Dependence ◽  
Copula Function ◽  
Bivariate Distribution ◽  
Nonlinear Relationship ◽  
Futures Prices ◽  
The Relationship ◽  
Jel Classifications

AbstractThis paper explores the nonlinearities of the bivariate distribution of Bordeaux en primeur, or wine futures, prices and Parker “barrel ratings” for the period of 2004 through 2010. In particular, copula-function methodology is introduced and employed to examine the nature of the bivariate distribution. Our results show a significant nonlinear relationship between Parker ratings and wine prices, characterized by significant positive tail dependence and higher correlation between high ratings and high prices. Marginal distributions for Parker ratings and wine prices are then identified and Monte Carlo simulation is employed to operationalize the relationship for risk-management purposes. (JEL Classifications: C19, G13, L66)

scholarly journals Application of Duration Measure in Quantifying the Sensitivity of Project Returns to Changes in Discount Rates

2019 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Vahidreza Yousefi ◽  
Siamak Haji Yakhchali ◽  
Jolanta Tamošaitienė
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Project Management ◽  
Discount Rate ◽  
Construction Projects ◽  
Project Manager ◽  
Management Systems ◽  
Duration Measure ◽  
The Relationship

In this research, the concept of Duration with a new application in project management has been defined. The Duration of each project provides the project manager with a combined measure containing concepts of return, cost and time of the project. Further in this article, the changes in project return, based on different assumptions such as discount rate, have been examined. To examine the effect of the changes in these factors, the Monte Carlo simulation has been used. The relationship between these factors is nonlinear which reflects the great importance of investment on appropriate risk management systems. The data from a set of construction projects have been used in order to verify the results of this study. Similar relationships can be expected to exist in other industries as well.

Who Will Replace Parker? A Copula Function Analysis of Bordeaux En Primeur Wine Raters

2019 ◽  
Vol 14 (2) ◽  
pp. 133-144 ◽  
Author(s):  
Don Cyr ◽  
Lester Kwong ◽  
Ling Sun
Keyword(s):  
Function Analysis ◽  
Rank Correlation ◽  
Tail Dependence ◽  
Copula Function ◽  
Nonlinear Relationship ◽  
Upper Tail Dependence ◽  
Linear And Nonlinear ◽  
Jel Classifications

AbstractThe influence of the wine rater Robert Parker Jr. on Bordeaux wine extended over a 40-year period, with a particular impact on en primeur wine prices. Consequently, his announcement in 2015 that he would no longer rate en primeur wines creates some uncertainty for many chateaux that have purposely designed their production with his palate and preferences in mind. Although the wine rater Neal Martin was named by Parker to be his successor in terms of en primeur wine ratings, there are several other wine critics who have consistently rated en primeur wines over several years. Consequently, we employ copula function analysis to explore which wine critics’ ratings exhibit the closest linear and nonlinear relationship, for right bank en primeur wines, with those of Parker. The study employs data over the period of 2005 through 2012, during which time several wine critics, including Neal Martin for the period of 2010–2012, rated en primeur wines alongside Parker. Our results indicate that of the wine critics that continue to rate en primeur wines, the ratings of James Suckling exhibit the highest rank correlation and also bivariate upper tail dependence, identified through copula function analysis, with those of Parker. (JEL Classifications: C19, G13, L66)

On Using Monte Carlo Simulations for Project Risk Management

2016 ◽  
pp. 150-173
Author(s):  
Cristiana Tudor ◽  
Maria Tudor
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Decision Makers ◽  
Project Schedule ◽  
Project Risk ◽  
Monte Carlo Simulation Technique ◽  
Basic Probability ◽  
The Cost

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.

scholarly journals Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 325
Author(s):  
Emad Mohamed ◽  
Parinaz Jafari ◽  
Simaan AbouRizk
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Wind Farm ◽  
Historical Data ◽  
Expert Knowledge ◽  
Bivariate Distribution ◽  
Expert Elicitation ◽  
Illustrative Case ◽  
Input Modeling

Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).

scholarly journals Some Results on Measures of Interaction between Paired Risks

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 88 ◽  
Author(s):  
Rui Fang ◽  
Xiaohu Li
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Risk Measures ◽  
Dependence Structure ◽  
Marginal Distributions ◽  
Numerical Examples ◽  
Risk Contribution ◽  
Actuarial Risk

Co-risk measures and risk contribution measures have been introduced to evaluate the degree of interaction between paired risks in actuarial risk management. This paper attempts to study the ordering behavior of measures on interaction between paired risks. For various co-risk measures and risk contribution measures, we investigate how the marginal distributions and the dependence structure impact on the level of interaction between paired risks. Also, several numerical examples based on Monte Carlo simulation are presented to illustrate the main findings.

scholarly journals Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo

2019 ◽  
Vol 7 (1) ◽  
pp. 133-149
Author(s):  
Martin Burda ◽  
Louis Bélisle
Keyword(s):  
Monte Carlo ◽  
Garch Model ◽  
Tail Dependence ◽  
Copula Function ◽  
Multivariate Garch ◽  
Hamiltonian Monte Carlo ◽  
Dynamic Copula ◽  
Multivariate Garch Model ◽  
Financial Asset Returns ◽  
Parameter Constraints

AbstractThe Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series. The model allows for capturing more flexible dependence patterns than a multivariate GARCH model and also generalizes static copula dependence models. Nonetheless, the model is subject to a number of parameter constraints that ensure positivity of variances and covariance stationarity of the modeled stochastic processes. As such, the resulting distribution of parameters of interest is highly irregular, characterized by skewness, asymmetry, and truncation, hindering the applicability and accuracy of asymptotic inference. In this paper, we propose Bayesian analysis of the CMGARCH model based on Constrained Hamiltonian Monte Carlo (CHMC), which has been shown in other contexts to yield efficient inference on complicated constrained dependence structures. In the CMGARCH context, we contrast CHMC with traditional random-walk sampling used in the previous literature and highlight the benefits of CHMC for applied researchers. We estimate the posterior mean, median and Bayesian confidence intervals for the coefficients of tail dependence. The analysis is performed in an application to a recent portfolio of S&P500 financial asset returns.

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