In this paper the value at risk (VaR) forecasts are compared using three different GARCH models; ARCH(1), GARCH(1,1) and EGARCH(1,1). The implemented method is a one-day ahead out of sample forecast of the VaR. The forecasts are evaluated using the Kupiec test with a five percent significance level. The focus is on three different markets; commodities, equities and exchange rates. The goal of t...

Research related to aggregation, robustness, and model uncertainty of regulatory risk measures, for instance, Value-at-Risk (VaR) and Expected Shortfall (ES), is of fundamental importance within quantitative risk management. In risk aggregation, marginal risks and their dependence structure are often modeled separately, leading to uncertainty arising at the level of a joint model. In this paper...

The paper estimate 1-day Value at Risk (VaR) taking into consideration the financial integration of Indian capital market (BSE-SENSEX and NSE-NIFTY) with other global indicators and its own volatility using daily returns covering the period January 2003 to December 2009. The paper specifies a generalized autoregressive conditional heteroscedasticity (GARCH) framework to model the phenomena of v...

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...

This paper argues that inferring long-horizon asset-return predictability from the properties of vector autoregressive (VAR) models on relatively short spans of data is potentially unreliable. We illustrate the problems that can arise by re-examining the findings of Bekaert and Hodrick (1992), who detected evidence of in-sample predictability in international equity and foreign exchange markets...

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...

A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The “observation” of the S...

Modeling pronunciation variations is a critical part of spontaneous Mandarin speech recognition. Such variations include both complete changes and partial changes. Complete changes can usually be modeled by using an alternate phone to replace the canonical phone. Partial changes, which cannot be modeled by conventional methods are variations within the phoneme and include diacritics. In this pa...

We propose a Bayesian stochastic search approach to selecting restrictions for Vector Autoregressive (VAR) models. For this purpose, we develop a Markov Chain Monte Carlo (MCMC) algorithm that visits high posterior probability restrictions on the elements of both the VAR regression coefficients and the error variance matrix. Numerical simulations show that stochastic search based on this algori...

The essential JIL-1 histone H3S10 kinase is a key regulator of chromatin structure that functions to maintain euchromatic domains while counteracting heterochromatization and gene silencing. In the absence of the JIL-1 kinase, two of the major heterochromatin markers H3K9me2 and HP1a spread in tandem to ectopic locations on the chromosome arms. Here we address the role of the third major hetero...