![]() Therefore, the aim of the article is to present and formally test three novel statistical learning models for VaR estimation: HCR, HCR-GARCH and HCR-QML-GARCH, which, by considering additional volatility term (due to time context and statistical moments), should be able to perform well in times of market turbulence. Under the conditions of sudden volatility increase, such as during the global economic crisis caused by the Covid-19 pandemic, no classical VaR model worked properly even for the group of the largest market indices. Market risk researchers agree that an ideal model for Value at Risk (VaR) estimation does not exist, different models performance strongly depends on current economic circumstances. Expectiles may address some of the flaws in VaR and expected shortfall-subject to the reservation that no risk measure can achieve exactitude in regulation. Moreover, expectiles are in harmony with gain/loss ratios in financial risk management. For ease of regulatory implementation, expectiles can be defined exclusively in terms of VaR, expected shortfall, and the thresholds at which those competing risk measures are enforced. Expectiles are most readily evaluated as a special class of quantiles. After reviewing practical concerns involving backtesting and robustness, this article more closely examines regulatory applications of expectiles. Indeed, expectiles are the only elicitable law-invariant coherent risk measures. Expectiles offset the weaknesses of value-at-risk (VaR) and expected shortfall. Embraced by the Basel accords, value-at-risk and expected shortfall are the leading measures of financial risk. This article reviews two leading measures of financial risk and an emerging alternative. Our findings confirm the validity of the solution, however we present several directions to develop it further. ![]() To evaluate our model, we have executed an empirical study on the log returns of WIG 20 (Warsaw Stock Exchange Index) in four different time periods throughout 20 with varying levels of observed volatility. The distributions of the innovations considered in the paper are: normal, t and skewed t, however the approach does enable extensions to other distributions as well. We suggest GARCHNet-a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators of probability in GARCH. On the contrary, recent rapid advancement of deep learning methods is said to be capable of describing any nonlinear relationships prominently. However, the lack of nonlinear structure in most of the approaches entails that the conditional variance is not represented in the model well enough. In particular, their high value is often praised in the case of Value-at-Risk. ![]() Classical GARCH models have been proven to give substantially good results in the case of financial modeling, where high volatility can be observed. Abstract: This study proposes a new GARCH specification, adapting a long short-term memory (LSTM) neural network's architecture.
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