Shocks to the equity capital ratio of financial intermediaries and the predictability of stock return volatility

Abstract

This paper shows that shocks to the equity capital ratio of financial intermediaries (CRFI) have predictive ability for stock realized volatility, from both in-sample and out-of-sample perspectives. The revealed predictability is also of economic significance, in that it examines the performance of portfolios constructed on the basis of CRFI forecasts of stock volatility. Robustness test results suggest that CRFI provides different information from traditional macro variables. Further analysis shows that simple linear regression is good enough in capturing predictive relationships between CRFI and stock volatility.

High-Frequency Forecasting of Stock Volatility Based on Model Fusion and a Feature Reconstruction Neural Network

“…In 2005, Awartani and Corradi proposed that symmetric and asymmetric GARCH is applicable to symmetric and asymmetric stock volatility forecasting [15]. In 2021, Feng He and Libo Yin experimentally argued that linear regression models can also be effective in predicting stock volatility [16].…”