Tesi etd-11122020-172806
Link copiato negli appunti
Tipo di tesi
Corso Ordinario Secondo Livello
Autore
SARTI, MANUEL
URN
etd-11122020-172806
Titolo
Estimating Stochastic Volatility Models with Noisy Data. An indirect inference application.
Struttura
Cl. Sc. Sociali - Scienze Economiche
Corso di studi
SCIENZE ECONOMICHE E MANAGERIALI - SCIENZE ECONOMICHE E MANAGERIALI
Relatori
Tutor Prof. TAMAGNI, FEDERICO
Relatore Prof. CORSI, FULVIO
Relatore Prof. CORSI, FULVIO
Parole chiave
- indirect inference
- Kalman filter
- measurement error
- stochastic volatility
Data inizio appello
03/12/2020;
Disponibilità
completa
Riassunto analitico
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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