This project investigates the forecasting problem of tail risk in financial markets and its implications for financial regulation. What keep risk managers awake at night are fast downfalls of unusual magnitude. These might trigger systemic spirals that could bring down the system. Dealers are concerned that market liquidity might dry up. And private investors sweat that the it will be difficult to climb out of the downside. Forecasting the far-left tail in any risk class or asset type is thus of great interest.
Predictions of near-extreme price movements require understanding of what drives tail dynamics. The static properties of return distribution are important and well researched in existing approaches, but that’s not enough. It is the inter-temporal dependencies that allow one to forecast. Although conditional quantiles oftentimes do move in sync with conditional volatility, tail dynamics may contain more than volatility clustering.
Across a wide range of assets, our preliminary studies show that the lengths of memory of conditional quantiles at different level indeed vary. Further, both far left and far right tail contain risk factors that are independent of those responsible for volatility clustering. This should have important implications for asset pricing in general and financial risk management in particular. We plan to substantiate this research by investigating various theoretical extensions and by applying the method to the real-world practice of financial risk management. Successful development of this project would result in novel business solutions that are readily useful to market participants as well as financial regulators.
