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This article aims to compare distinct metrics of the value at risk (VaR), differing from prior studies with respect about compare three asset categories belonging to seven countries. Since VaR inception, several approaches were developed to improve the loss estimation accuracy. However, there is hardly a universal consensus on which approach is the most appropriate, since VaR depends on statistical properties of the target asset and the market in which it is traded. It is relevant to compare the results obtained not only among the assets, but also among the markets in which they are traded, considering their specifics properties to verify if there is any pattern of the methods for the data. Considering the three asset categories, the semiparametric and non-parametric models obtained the lowest rejections number. It was also found that the models tested were not effective for the estimation of exchange rate VaR, which may be due to more relevant risks than the market in it asset price formation. Five models belonging to the parametric, semiparametric, and non-parametric approaches were tested. The analyses were divided in two, aiming to test the VaRs performances in distinct economic cycles; the first analyses considered a 1,000 days estimation window, while the second one considered a 252 days estimation window. To validated the results statistically, were applied the Kupiec and the Christoffersen tests. The results show that the conditional VaR and historical simulation have the best performance to estimate VaR. Comparing the markets, Chinese assets were the ones with the highest average number of tests rejections, which can be a consequence of its closed economy. Finally, it was found that shorter estimation window tends to perform better for high volatility assets, while longer window tends for lower volatility assets. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this article is to model a minute series of exchange rates for the EUR/USD pair using the singular spectrum analysis (SSA) and ARIMA-GARCH methods and evaluate which one offers better forecasts for a five-minute horizon. Originality/value: Despite being a successful technique in other branches of science, the application of SSA in finance is quite new. Furthermore, exchange rate modeling is a complex problem, comprising statistical concepts and properties. However, despite the complexity, the analysis of this series is extremely important for several agents playing, directly or indirectly, a role in the economy and the financial market. Design/methodology/approach: Time series models were estimated using the ARIMA-GARCH and SSA techniques, taking into account three samples of the ask exchange rate (closing): uptrend, downtrend, and no well-defined trend. Findings: The forecasts carried out by the SSA were the ones closest to the original observations for the three cases. Regarding the quality measurements, SSA obtained the best results for both uptrend and downtrend samples; for the sample with no well-defined trend, the findings indicated that the ARIMA-GARCH technique attained better results. However, it was concluded that the SSA forecasts, regarding exchange rates during the studied period, are more appropriate than the ones obtained by the ARIMA-GARCH model, regardless of the market movement. [ABSTRACT FROM AUTHOR]