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Climate change scenarios suggest that long periods without rainfall will occur in the future often causing instability of the agricultural products market. The aim of the research was to build a model...
To capture the volatility in the global food commodity prices, we employed two competing models, the thin tailed the normal distribution, and the fat-tailed Student t-distribution models. Results base...
This study's aim is to examine a new version of capital assets pricing model which is called Revised Capital Assets Pricing Model (R-CAPM) in Tehran Stock Exchange (TSE). According to Markowitz theo...
A novel dynamical model for the study of operational risk in banks and suitable for the calculation of the Value at Risk (VaR) is proposed. The equation of motion takes into account the interactions a...
This paper addresses the issue of forecasting the term structure.We provide a unified state-space modeling framework that encompasses different existing discrete-time yield curve models.
This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on ...
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible ...
We study the problem of forecasting volatility for the multifractal random walk model. In order to avoid the ill posed problem of estimating the correlation length T of the model, we introduce a limit...

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