abstract in this research by using a synchronous equivalents system, the functions of supply and demand of any kind of meat and corn in fars province, iran are considered. the purpose of this study is to predict the functions of supply and the price of meat and corn, recognition and analysis of effective issued on supply and price of this sections, considering the effect of shocks and the cross...

Event risk is the risk that a portfolio’s value can be affected by large jumps in market prices. Event risk is synonymous with “fat tails” or “jump risk”. Event risk is one component of “specific risk,” defined by bank supervisors as the component of market risk not driven by market-wide shocks. Standard Value-at-Risk (VaR) models used by banks to measure market risk do not do a good job of cap...

Vector autoregressions (VAR) are extensively used to model economic time series. The large number of parameters is the main diicult with VAR models, however. To overcome this, Litterman (1986) suggests to use a Bayesian strategy to estimate the VAR, equation by equation, where, a priori, the lags have decreasing importance (known as Litterman Prior). In this paper, a VAR model is analyzed throu...

The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly a...

A four-dimensional ensemble variational (4D-EnVar) data assimilation has been developed for a limited area model. The integration of tangent linear and adjoint models, as applied in standard 4D-Var, is replaced with the use of an ensemble of non-linear model states to estimate fourdimensional background error covariances over the assimilation time window. The computational costs for 4D-En-Var a...

Value-at-Risk (VaR) is widely used as a tool for measuring the market risk of asset portfolios. However, alternative VaR implementations are known to yield fairly different VaR forecasts. Hence, every use of VaR requires choosing amongst alternative forecasting models. This paper undertakes two case studies in model selection, for the S&P 500 index and India’s NSE-50 index, at the 95% and 99% l...

It is well known that many countries around the world depend on the US as their major trade partner. As a result, if something does happen to US economy it surely will affect the economy of all these countries. In this study, we investigate the relationship between the US and four Asian emerging stock markets namely Hong Kong, India, South Korea and Malaysia using monthly data between 1996 and ...

In this paper we study the model risk of Expected Shortfall (ES), extending the results of Boucher et al. (2014) on model risk of Value-at-Risk (VaR). We propose a correction formula for ES based on passing three backtests. Our results show that for the DJIA index, the smallest corrections are required for the ES estimates built using GARCH models. Furthermore, the 2.5% ES requires smaller corr...

In this study, we propose an improved fuzzy multi-objective portfolio selection model (VaR-MOPSM) with distinct risk measurements. The VaR-MOPSM can precisely evaluate the investment and increase the probability of obtaining the expected return. When building the model, fuzzy Value-at-Risk (VaR), which can directly reflect the greatest loss of a selection case under a given confidence level, is...

This paper introduces a Value-at-Risk (VaR) model to generate route choices for a hazmat shipment based on a specified risk confidence level. The objective is to determine a route which minimizes the likelihood that the risk will be greater than a set threshold. Several properties of the VaR model are established. An exact solution procedure is proposed and tested to solve the single-trip probl...