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- W66336044 abstract "I. INTRODUCTION This paper builds and assesses scientific stochastic volatility (SV) models for the European Carbon markets and traded among others at NASDAQ OMX Commodities Exchange, European Energy Exchange (EEX) and Intercontinental Commodity Exchange (1) (ICE) (2). The European Commission established in 2005 the European Emission Trading Scheme (EU-ETS), a cap-and-trade scheme for emission allowances (EUA). In Europe, each country defines their total amount of emission allowance in National Allowance plans (NAP). Active trading in organized markets (tradable commodities) occurs when firms abate emissions and sell their allowances, while other firms require more allowances than allocated initially. Three phases has been announced; phase I running from 2005 to 2007, phase II running from 2008 to 2012, and phase III designed to run from 2013 to 2020. The price dynamics of EUAs in the new and immature Phase I market have been studied extensively (e.g. Hinterman (2010), Conrad et al. (2010). Understanding the price dynamics in the more mature markets of Phase II and Phase III of EU-ETS is therefore more relevant for an efficient market design and carbon abatement costs learning. An actively traded global annual carbon market volume of 92 billion [euro] (2010) is highly relevant for carbon traders, risk management and asset allocation (3). In 2012 the Intercontinental Exchange had 91.6% of the total traded market and in January 2013 the exchange surpassed 40.000 (4000) EUA Futures ADV--1.000 tC[O.sub.2]e (EUA Options ADV--1.000 tC[O.sub.2]e) average daily volume. The ICE reported 115 ICE market Emissions members in January 2013. Knowledge of the empirical properties of the forward December carbon prices is important when constructing risk-assessment and management strategies. Market participants who understand the dynamic behaviour of carbon prices are more likely to have realistic expectations about future prices and the risks to which they are exposed. Time-varying volatility is endemic in financial markets. Such risks may change through time in complicated ways, and it is natural to build stochastic models for the temporal evolution in volatility. The main objective of the paper is therefore to structure a scientific model specifying volatility as having its own stochastic process, appropriately describing the evolution of the carbon market volatility. The implementation adapts the MCMC estimator proposed by Chernozhukov and Hong (2003), substantially superior to conventional derivative based hill climbing optimizers for this stochastic class of problems. Moreover, under correct specification of the structural model the normalized value of the objective function is asymptotically [chi square] distributed (and the degrees of freedom is well specified). An appropriate and well-specified SV model for the European carbon markets broaden applications into derivative pricing purposes, risk assessment and management, asset allocation and portfolio management. Stochastic volatility models have an intuitive and simple structure and can explain the major stylized facts of asset, currency and commodity returns. The carbon price volatility investigation is important for several reasons. First, carbon price volatility could hinder investments in new and advanced technology and equipment. Second, if carbon prices and other energy and commodity prices are co-integrated, a volatile market might make it harder for consumers and businesses to predict their raw-material costs (4). In a booming economy, an increase in prices may make it harder for economic growth to occur. Carbon prices move relatively slowly when conditions are calm, while they move faster when there is more news, uncertainty, and trading. One of the most prominent stylized facts of returns on financial assets is that their volatility changes over time. As the most important determinant of the price of an option is the uncertainty associated with the price of the underlying asset, volatility is of paramount importance in financial analysis. …" @default.
- W66336044 created "2016-06-24" @default.
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- W66336044 date "2014-01-01" @default.
- W66336044 modified "2023-09-22" @default.
- W66336044 title "Scientific Stochastic Volatility Models for the European Carbon Markets: Forecasting and Extracting Conditional Moments" @default.
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