{"title":"LightGBM Based Optiver Realized Volatility Prediction","authors":"Yue Wu, Qi Wang","doi":"10.1109/CSAIEE54046.2021.9543438","DOIUrl":null,"url":null,"abstract":"Nowadays, market volatility prediction is the most prominent terms you will hear in the trading market. Realized volatility is the representation of price movements, market's volatility and the trading risks. A little change happened in volatility will affect the expected return on all assets. In this article, we will use the dataset provided by Kaggle platform to predict the volatility. As a leading global electronic market maker, Optiver is dedicated to continuously improving financial markets, creating better access and prices for options, ETFs, cash equities, bonds and foreign currencies on numerous exchanges around the world. The prediction model we used in our paper is LightGBM, which is an iimproved version of XGBoost. We conclude some related work about the prediction of volatility. And we compute our model with others, the result shows that our model LightGBM has a lowest RMSPE score that is 0.211. And compared to it, the RMSPE of other models such as logistic regression, SVM and XGBoost are respectively 0.099. 0.076, 0.034 higher than LightGBM.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSAIEE54046.2021.9543438","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

Nowadays, market volatility prediction is the most prominent terms you will hear in the trading market. Realized volatility is the representation of price movements, market's volatility and the trading risks. A little change happened in volatility will affect the expected return on all assets. In this article, we will use the dataset provided by Kaggle platform to predict the volatility. As a leading global electronic market maker, Optiver is dedicated to continuously improving financial markets, creating better access and prices for options, ETFs, cash equities, bonds and foreign currencies on numerous exchanges around the world. The prediction model we used in our paper is LightGBM, which is an iimproved version of XGBoost. We conclude some related work about the prediction of volatility. And we compute our model with others, the result shows that our model LightGBM has a lowest RMSPE score that is 0.211. And compared to it, the RMSPE of other models such as logistic regression, SVM and XGBoost are respectively 0.099. 0.076, 0.034 higher than LightGBM.
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基于LightGBM的Optiver实现波动率预测
如今,市场波动预测是你在交易市场上听到的最重要的术语。已实现波动率是价格变动、市场波动和交易风险的代表。波动率的微小变化都会影响所有资产的预期收益。在本文中,我们将使用Kaggle平台提供的数据集来预测波动性。作为全球领先的电子做市商,Optiver致力于不断改善金融市场,在全球众多交易所为期权、etf、现金股票、债券和外汇创造更好的准入和价格。我们在论文中使用的预测模型是LightGBM,它是XGBoost的改进版本。本文总结了波动率预测的相关工作。结果表明,我们的模型LightGBM具有最低的RMSPE分数,为0.211。与之相比,其他模型如logistic回归、SVM和XGBoost的RMSPE分别为0.099。0.076,比LightGBM高0.034。
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