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Corsi integrativi di II livello

SARTI, MANUEL

etd-11122020-172806

Estimating Stochastic Volatility Models with Noisy Data. An indirect inference application.

Cl. Sc. Sociali - Scienze Economiche

SCIENZE ECONOMICHE E MANAGERIALI - SCIENZE ECONOMICHE E MANAGERIALI

Tutor Prof. TAMAGNI, FEDERICO
Presidente Prof. BARONTINI, ROBERTO
Relatore Prof. CORSI, FULVIO
Membro Prof.ssa CHIAROMONTE, FRANCESCA
Membro Prof. DOSI, GIOVANNI
Membro Prof. FAGIOLO, GIORGIO
Membro Prof. MINA, ANDREA
Membro Prof.ssa NUTI, SABINA
Membro Prof. NUVOLARI, ALESSANDRO
Membro Prof. TESTA, FRANCESCO

• indirect inference
• Kalman filter
• measurement error
• stochastic volatility

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completa

Stochastic volatility models are able to reproduce many empirical regularities in financial time-series data, but their estimation is a challenging task. Indirect inference is often used for this purpose, even if it provides inconsistent estimates when data affected by measurement error are employed, for instance Realized Variance. To tackle this issue, Rossi and Santucci de Magistris (2018) suggest estimating, jointly with the model parameters, an additional parameter capturing noise component. In contrast, we propose a Kalman Filter approach, to obtain robust estimates from observed data. The estimation of a stochastic volatility model on noisy data is carried out, following the indirect inference procedure according to both the methods mentioned, in order to compare their performances. Results show that the solution we propose may be a viable alternative to the noise specification approach, to cope with measurement error in observed data when indirect inference is applied.

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