Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model

Xu, Qifa; Chen, Lu; Jiang, Cuixia; Liu, Yezheng

doi:10.1002/for.2817

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…Literature [12] Uses the deep network to operate risk prediction. Literature [13] proposed a novel synthetic mixed data sampling (JE-MIDAS) model. Literature [14] proposed a financial risk early warning model based on improved kernel principal component analysis for public hospitals.…”

Section: Related Researchmentioning

confidence: 99%

Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

2023

Applied Mathematics and Nonlinear Sciences

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This paper is based on the use of recurrent neural networks and LSTM deep neural networks to obtain the financial risk prediction feature sequence in the context of big data. The financial risk prediction feature sequence is used as the input value of the input gate of the LSTM deep neural network model after data filtering, normalization and loss function optimization, and then the financial risk prediction for the output gate of the LSTM deep neural network model. Considering the availability of data, small and medium-sized enterprises listed in A-share companies in the Wind database are selected as sample enterprises, and evaluation indexes are constructed and detected at the same time so as to complete the experimental design of enterprise financial risk prediction in the context of big data. The prediction of enterprise financial risk is empirically analyzed using simulation analysis and statistical analysis. The results show that in the model performance analysis, the average value of ten years of data, the highest value is still the result obtained by LSTM training, 0.761, compared with other models of LSTM deep neural network in static financial risk prediction in the overall best performance. In the case study of Yibai Pharmaceutical, the minimum value of the rate of return, return on total assets, and return on assets were -10.02%, 2.56%, -20.72%, which reflects the fact that the private enterprises still have large profitability space to be mined. This study helps investors or financial institutions such as funds to find out the possible financial risk crisis of listed companies as early as possible to avoid the parties from incurring large financial losses.

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Section: Related Researchmentioning

confidence: 99%

Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

2023

Applied Mathematics and Nonlinear Sciences

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Forecasting Expected Shortfall and Value‐at‐Risk With Cross‐Sectional Aggregation

Wang,

Wang

2024

Journal of Forecasting

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The combination of the conditional autoregressive value‐at‐risk (CAViaR) process with the Fissler–Ziegel (FZ) loss function generates a recently emerging framework (CAViaR‐FZ) for forecasting value‐at‐risk (VaR) and expected shortfall (ES). However, existing CAViaR‐FZ models typically overlook the presence of long‐range dependence, a stylized fact of financial time series. This paper proposes a long‐memory CAViaR‐FZ model using the cross‐sectional aggregation (CSA) method. The CSA method is well‐recognized for its ability to generate a long‐memory process by aggregating an infinite number of short‐memory processes cross‐sectionally. The proposed CSA‐CAViaR‐FZ model flexibly captures long‐memory dynamics in both VaR and ES and includes the original short‐memory CAViaR‐FZ model as a special case. Simulation and empirical results demonstrate that the proposed model outperforms various competing models.

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Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall

Candila

Gallo²,

Petrella

2023

Ann Oper Res

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Although quantile regression to calculate risk measures is widely established in the financial literature, when considering data observed at mixed-frequency, an extension is needed. In this paper, a model is built on a mixed-frequency quantile regressions to directly estimate the Value-at-Risk (VaR) and the Expected Shortfall (ES) measures. In particular, the low-frequency component incorporates information coming from variables observed at, typically, monthly or lower frequencies, while the high-frequency component can include a variety of daily variables, like market indices or realized volatility measures. The conditions for the weak stationarity of the daily return process are derived and the finite sample properties are investigated in an extensive Monte Carlo exercise. The validity of the proposed model is then explored through a real data application using two energy commodities, namely, Crude Oil and Gasoline futures. Results show that our model outperforms other competing specifications, on the basis of some popular VaR and ES backtesting test procedures.

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Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model

Cited by 4 publications

References 53 publications

Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

Forecasting Expected Shortfall and Value‐at‐Risk With Cross‐Sectional Aggregation

Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall

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