L. Mhalla
J. Hambuckers
M. Lambert

extremal connectedness of hedge funds (replication data)

We propose a dynamic measure of extremal connectedness tailored to the short reporting period and unbalanced nature of hedge funds data. Using multivariate extreme value regression techniques, we estimate this measure conditional on factors reflecting the economic uncertainty and the state of the financial markets, and derive risk indicators reflecting the likelihood of extreme spillovers. Empirically, we study the dynamics of tail dependencies between hedge funds grouped per investment strategies, as well as with the banking sector. We show that during crisis periods, some pairs of strategies display an increase in their extremal connectedness, revealing a higher likelihood of simultaneous extreme losses. We also find a sizable tail dependence between hedge funds and banks, indicating that banks are more likely to suffer extreme losses when the hedge fund sector does. Our results highlight that a proactive regulatory framework should account for the dynamic nature of the tail dependence and its link with financial stress.

Data and Resources

Suggested Citation

Mhalla, L.; Hambuckers, J.; Lambert, M. (2022): Extremal connectedness of hedge funds (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022327.072429