This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR) modeling, aiming to enhance accuracy and provide novel insights. Employing daily returns data from 2000 to 2020 for gold, silver, oil, gas, and copper, various combination methods are...

Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering...

Risk estimation or volatility estimation at financial markets, particularly stock exchange markets, is complex issue of great importance to theorists and practitioners. Models used to estimate volatility forecasts are translated into better pricing of stocks and better risk management. The aim...

Using daily observations of the index and stock market returns for the Peruvian case from January 3, 1990 to May 31, 2013, this paper models the distribution of daily loss probability, estimates maximum quantiles and tail probabilities of this distribution, and models the extremes through a...

The paper deals with maritime risk, which we consider important, no doubt, for ship-owners acting in volatile markets. Traditionally, risk is measured by "standard deviation". Other risk measures like "excess kurtosis", "excess skewness", "long-term dependence" and the "catastrophe propensity"...

Purpose - This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods. Design/methodology/approach - Many VaR estimation models...

The aim of the presented study was to assess the quality of VaR forecasts in various states of the economic situation. Two approaches based on the extreme value theory were compared: Block Maxima and the Peaks Over Threshold. Forecasts were made on the daily closing prices of 10 major indices in...

This study aimed to predict the JKII (Jakarta Islamic Index) price as a price index of sharia stocks and predict the loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach) on the daily closing price of JKII from 1 August...

This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and...

This paper aims to replicate the semiparametric Value-At-Risk model by Dias (2014) and to test its legitimacy. The study confirms the superiority of semiparametric estimation over classical methods such as mixture normal and Student-t approximations in estimating tail distribution of portfolios,...