Measurement of Intraday Volatility in Stock Markets

• Applicant: Professorin Dr. Franziska Peter
• Subject Area: Accounting and Finance ; Statistics and Econometrics
• Term: from 2018 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 389577820

The variance risk premium is commonly considered as a remedy against downside risk when investing in equity. The increased uncertainty due to the Covid-19 pandemic emphasizes the need for investors to account for variance risk and offers a unique setting to analyze the existence, cross-sectional and time variation of individual equity variance risk premia. In particular, as recent developments have shown that different sectors have been affected to a variable degree by the economic, regulatory and social impacts of the Covid-19 pandemic. We will therefore examine the individual equity variance risk premia across different sectors to provide valuable insights into the (changing) valuation of variance risk by investor with special emphasize on the impact of the Covid-19 crisis.In addition, we apply deep learning approaches for (downside) variance prediction by combining artificial neural networks with an attention-based mechanism. Attention mechanisms are part of human perception and imply a selective attention to specific pieces of information at each point in time. Transferred to the context of variance prediction, the application of an attention mechanism allows to provide insights into the selection of key variables for decision making. The flexibility of artificial neural networks with respect to the observed patterns offers an interesting alternative to standard approaches when analyzing intraday stock market data.Considering interdependencies among financial markets on an intraday level, conflicting conclusions exist with respect to the role of the option market within the price discovery process of the underlying stock. We therefore aim at developing a theoretical framework as well as a methodological approach for a time-varying price discovery measure. We plan to focus on generalized autoregressive score models, which allow an observation driven updating process of parameters. This approach is supposed to shed further light on the potentially changing importance of the option markets around firm-specific informational events such as earning announcements.

• DFG Programme: Research Grants