Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function
Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance lo...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5511802 |
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| author | Fang Jia Boli Yang |
| author_facet | Fang Jia Boli Yang |
| author_sort | Fang Jia |
| collection | DOAJ |
| description | Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function. |
| format | Article |
| id | doaj-art-dcf9585fa4cc4ca28f6c62fd0b4a6261 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-dcf9585fa4cc4ca28f6c62fd0b4a62612025-02-03T06:43:46ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55118025511802Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss FunctionFang Jia0Boli Yang1School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaInvestment Product Department, Creditease Corp., Beijing 100020, ChinaVolatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.http://dx.doi.org/10.1155/2021/5511802 |
| spellingShingle | Fang Jia Boli Yang Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function Complexity |
| title | Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function |
| title_full | Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function |
| title_fullStr | Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function |
| title_full_unstemmed | Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function |
| title_short | Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function |
| title_sort | forecasting volatility of stock index deep learning model with likelihood based loss function |
| url | http://dx.doi.org/10.1155/2021/5511802 |
| work_keys_str_mv | AT fangjia forecastingvolatilityofstockindexdeeplearningmodelwithlikelihoodbasedlossfunction AT boliyang forecastingvolatilityofstockindexdeeplearningmodelwithlikelihoodbasedlossfunction |