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CARBON STOCHASTIC VOLATILITY MODEL ESTIMATION AND INFERENCE: FORECASTING (UN-) CONDITIONAL MOMENTS

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Author(s): Per Bjarte Solibakke | Sjur Westgaard | Gudbrand Lien

Journal: Economics and Finance Review
ISSN 2047-0401

Volume: 1;
Issue: 6;
Start page: 69;
Date: 2011;
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Keywords: Stochastic Volatility | Bayesian Estimators | Metropolis-Hastings Algorithm | Markov Chain Monte Carlo (MCMC) Simulations | GSM-Projection-Reprojection

ABSTRACT
This paper applies the General Scientific Model methodology of Gallant and McCulloch implementing MCMC simulation methodologies to build a multifactor stochastic volatility model for the NASDAQ OMX carbon front December forward contracts. Stochastic volatility is the main way time-varying volatility is modeled in financial markets. Our main objective is therefore to structure a scientific model specifying volatility as having its own stochastic process. Appropriate model descriptions broaden the applications into derivative pricing purposes, risk assessment and asset allocation. The paper reports risk and portfolio measures, conditional one-step-ahead moments, particle filtering for one-step-ahead conditional volatility, conditional variance functions for evaluation of shocks, analysis of multi-step-ahead dynamics, and conditional persistence. The analysis adds market insight and enables forecasts to be made, thus building up methodologies for developing valid scientific models for commodity market applications.

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