A self-exciting point process with a continuous-time autoregressive moving average intensity process, named CARMA(p, q)-Hawkes model, has recently been introduced. The model generalises the Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p, q) model where the associated state process is driven by the counting process itself. The proposed model preserves the same degree of tractability as the Hawkes process, but it can reproduce more complex time-dependent structures observed in several market data. The paper presents a new model of asset price dynamics based on the CARMA(p, q)-Hawkes model. It is constructed using a compound version of it with a random jump size that is independent of both the counting and the intensity processes and can be employed as the main block for pure jump and (stochastic volatility) jump-diffusion processes. The numerical results for pricing European options illustrate that the new model can replicate the volatility smile observed in financial markets. Through an empirical analysis, which is presented as a calibration exercise, we highlight the role of higher order autoregressive and moving average parameters in pricing options.

Option pricing with a compound CARMA(p, q)-Hawkes

Andrea Perchiazzo;
2025-01-01

Abstract

A self-exciting point process with a continuous-time autoregressive moving average intensity process, named CARMA(p, q)-Hawkes model, has recently been introduced. The model generalises the Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p, q) model where the associated state process is driven by the counting process itself. The proposed model preserves the same degree of tractability as the Hawkes process, but it can reproduce more complex time-dependent structures observed in several market data. The paper presents a new model of asset price dynamics based on the CARMA(p, q)-Hawkes model. It is constructed using a compound version of it with a random jump size that is independent of both the counting and the intensity processes and can be employed as the main block for pure jump and (stochastic volatility) jump-diffusion processes. The numerical results for pricing European options illustrate that the new model can replicate the volatility smile observed in financial markets. Through an empirical analysis, which is presented as a calibration exercise, we highlight the role of higher order autoregressive and moving average parameters in pricing options.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11579/219256
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