|Title||Downside risk measurement in regime switching stochastic volatility|
Risk measurement is important to firms to enable management of risks, and ensure profitability during different firm and market events. In particular, downside risk is an important risk measure as it is a coherent risk measure, and it is also compatible with industry risk management approaches such as stop losses. Whilst regime switching models have been used for downside risk measurement, the regime switching models for stochastic volatility dynamics have been limited and so restrict risk measurement. In this paper we propose a new regime switching model that incorporates non-trivial stochastic volatility dynamics, hence we are able to measure risk more realistically. We derive the downside risk measure associated with our regime switching model, for risk measurement including and excluding jump risk. We prove that the regime switching model converges to the underlying continuous time asset pricing model, hence our risk measurement is consistent. We provide a discretisation for the variance risk process, which is locally consistent and enables computational implementation. We also provide numerical experiments to illustrate our method.
|Journal||Journal of Computational and Applied Mathematics|
|Accepted author manuscript|
CC BY-NC-ND 4.0
File Access Level
Open (open metadata and files)
|Digital Object Identifier (DOI)||https://doi.org/10.1016/j.cam.2020.112845|