Volatility prediction in the financial market helps to understand the profit and involved risks in investment. However, due to irregularities, high fluctuations, and noise in the time series, predicting volatility poses a challenging task. In the recent Covid-19 pandemic situation, volatility prediction using complex intelligence techniques has attracted enormous attention from researchers worldwide. In this paper, a novel and simple approach based on the robust least squares method in two approaches a) with least absolute residuals (LAR) and b) without LAR, have been applied to the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) for a period of ten years. For a deeper analysis, the volatility time series has been decomposed into long-term trends, and seasonal, and random fluctuations. The data sets have been divided into parts viz. training data set and testing data set. The validation results have been achieved using root mean square error (RMSE) values. It has been found that robust least squares method with LAR approach gives better results for volatility (RMSE = 0.01366) and its components viz. long term trend (RMSE = 0.10087), seasonal (RMSE = 0.010343) and remainder fluctuations (RMSE = 0.014783), respectively. For the first time, generalized prediction equations for volatility and its three components have been presented. Young researchers working in this domain can directly use the presented prediction equations to understand their data sets.
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
Southwestern University of Finance and Economics（西南财经大学）；Inboc Technologies Ltd.（Inboc科技有限公司）；University of Edinburgh（爱丁堡大学）；ArrayStream Technologies Ltd.（ArrayStream科技有限公司）；University of Glasgow（格拉斯哥大学）
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions
Oxford-Man Institute of Quantitative Finance, University of Oxford（牛津大学牛津-曼量化金融研究院）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；University of Oxford（牛津大学）
