In financial market risk measurement, Value-at-Risk (VaR) techniques have proven to be a very useful and popular tool. Unfortunately, most VaR estimation models suffer from major drawbacks: the lognormal (Gaussian) modeling of the returns does not take into account the observed fat tail distribution and the non-stationarity of the financial instruments severely limits the efficiency of the VaR ...

convertibility is an important factor in international trade where instruments valued in different currencies will be exchanged. convertibility can be either total manner or a partial manner. however, whatever the type of convertibility, there will be advantages and disadvantages to  the economy of a country of which has  made a convertibility of its national currency. convertibility was studie...

Market risk exposure plays a key role for financial institutions risk management. A possible measure for this exposure is to evaluate losses likely to incur when the price of the portfolio’s assets declines using Value-at-Risk (VaR) estimates, one of the most prominent measure of financial downside market risk. This paper suggests an evolving possibilistic fuzzy modeling approach for VaR estima...

Traditional vector autoregressive (VAR) modeling theory has the defect that it can not effectively utilize the multiple time scale information contained in the inner of variables. In order to discuss multiscale behavior among economic variables and capture variables’ information in different time scale, multiresolution VAR model which can also be called as MVAR model has been established in the...

Value at Risk (VaR) is a popular measure for quantifying the market risk that a financial institution faces into a single number. Due to the complexity of financial markets, the risks associated with a portfolio may vary over time. For accurate VaR estimation, it is necessary to have flexible methods that adapt to the underlying data distribution. In this paper, we consider VaR estimation by us...

Extreme value theory has been widely used for modeling the tails of return distribution. Generalized Pareto distribution (GPD) is popularly acknowledged as one of the major tools in Value-at-Risk (VaR) estimation. As Basel II stipulates the significance level for VaR estimation from previous 5% quantile level to more extremal quantile levels at 1%, it demands a more accurate estimation approach...

Unrestricted reduced form vector autoregressive (VAR) models have become a dominant research strategy in empirical macroeconomics since Sims (1980) critique of traditional macroeconometric modeling. They are however subjected to the curse of dimensionality. In this paper we propose general-to-specific reductions of VAR models and consider computer-automated model selection algorithms embodied i...

2D Free-surface hydraulic modeling tools are commonly used to assess flood hazard for production of maximal water depth (hmax) maps, as support for flood risk assessment. High Resolution (HR) topographic data are big data getting commonly available and used by hydraulic modeling community. Topographical information and its strategy of inclusion in models, are inputs of great importance for over...

Generalized Pareto distributions (GPD) are widely used for modeling excesses over high thresholds (within the framework of the POT-approach to modeling extremes). The aim of the paper is to give the review of the classical techniques for estimating GPD quantiles, and to apply these methods in finance to estimate the Valueat-Risk (VaR) parameter, and discuss certain difficulties related to this ...

Modeling and Estimation of High-dimensional Vector Autoregressions by Sumanta Basu Chair: George Michailidis Vector Autoregression (VAR) represents a popular class of time series models in applied macroeconomics and finance, widely used for structural analysis and simultaneous forecasting of a number of temporally observed variables. Over the years it has gained popularity in the fields of cont...