Estimation And Forecast Of Carbon Emission Market Volatility Based On Model Averaging Method
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| Author | : Yong Li |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
The carbon market, as a market operating with carbon emission rights for core trading, plays an important role in reducing the production of greenhouse gases and controlling the risk of climate change caused by environmental pollution but also shows complex and changeable dynamic characteristics. Estimating and predicting the evolution of carbon market volatility can not only provide data for the correct measurement of market risk and calculation of value at risk but also provide instructions for the pricing of carbon-related assets and the construction of investment portfolios. In this paper, based on the EUA carbon futures price series, various GARCH models and SV models are used to describe and forecast various properties of volatility, such as spike and thick tail asymmetry and jump characteristics. Considering the complexity of the model, the model averaging methods are used to infer the whole alternative model space to reduce the uncertainty of volatility prediction, which extends the existing research. The empirical results show that the persistence of EUA carbon market volatility is strong and that there is a certain leverage effect consistent with the characteristics of traditional financial markets. However, different from the traditional financial market, the jump of volatility has expressed characteristics of smaller probability but larger amplitude. The MCS test shows that the model averaging methods present higher prediction accuracy, and the prediction errors are smaller than those of the single model. Instead of blindly selecting a single model for carbon market volatility forecasting, investors should take advantage of model averaging methods to reduce information uncertainty under different loss functions in terms of research purpose and actual needs.
| Author | : Chun Liu |
| Publisher | : |
| Total Pages | : 246 |
| Release | : 2007 |
| Genre | : |
| ISBN | : 9780494394700 |
In this thesis, I study the dynamics of the volatility process and focus on estimation and forecasting. Recent research uses high frequency intraday data to construct ex post measures of daily volatility including realized volatility (RV). Chapter 1 is the introduction. In Chapter 2, I use a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. I focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations I demonstrate that the estimation approach is effective in identifying and dating structural breaks. Applied to daily S & P 500 data, I find strong evidence of a single structural break in log(RV). The main effect of the break is on the long-run mean and variance of log-volatility. Chapter 3 uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include HAR specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and leverage term. The Bayesian model averaging provides very competitive density forecasts and consistent but modest improvements in point forecasts over the benchmarks. Applied to equity and exchange rate volatility over several forecast horizons, the Bayesian model averaging provides the best performance compared to the benchmarks including HAR, AR and simple model averaging models. I discuss the reasons for this, including the importance of using realized power variation as a predictor. In the last chapter, I propose a new joint model of volatility and duration in high frequency framework using tick-by-tick data. This model decomposes the conditional variance into different volatility components associated with different transaction horizons. Using stock market data, I demonstrate its superiority over the traditional GARCH counterpart. In addition, I show that a fat-tailed t-distribution for return innovations and a Burr distribution for duration innovations improve density forecasts, compared with normal and exponential distribution, respectively.
| Author | : |
| Publisher | : World Business Pub. |
| Total Pages | : 0 |
| Release | : 2004 |
| Genre | : Business enterprises |
| ISBN | : 9781569735688 |
The GHG Protocol Corporate Accounting and Reporting Standard helps companies and other organizations to identify, calculate, and report GHG emissions. It is designed to set the standard for accurate, complete, consistent, relevant and transparent accounting and reporting of GHG emissions.
| Author | : Bangzhu Zhu |
| Publisher | : Springer |
| Total Pages | : 180 |
| Release | : 2017-05-09 |
| Genre | : Business & Economics |
| ISBN | : 3319576186 |
This book applies the multidisciplinary approaches of econometrics, statistics, finance and artificial intelligence for pricing and forecasting the carbon market in the context of managerial issues. It explores the related issues of pricing and forecasting the carbon market using theoretical models and empirical analyses, demonstrating how the carbon market, as a policy-based artificial market, is complex and influenced by both the market mechanisms and the external heterogeneous environments. By integrating the features of analytical systems, it offers insights to further our scientific understanding of the pricing mechanism and the variable laws governing the carbon market. Moreover, it lays a foundation for dealing with climate change in China and constructing a national carbon market there. Ultimately, it actively contributes to the energy saving and CO2 emission reduction promoted by the carbon market. The carbon market, represented by the European Union Emissions Trading System (EU ETS), is a cost-effective measure for tackling climate change. Furthermore, pricing and forecasting carbon market has been one of the research focuses in the fields of energy and climate change. As a policy tool of the trading mechanism, the carbon market offers a great institutional innovation for coping with climate change. Due to its multiple advantages including saving costs and environment protection, and political feasibility, more and more countries including China have applied the carbon market for carbon dioxide (CO2) emission reduction. Accurately understanding the pricing mechanism and mastering the fluctuating law of carbon market is essential to build a national carbon market for China.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 281 |
| Release | : 2017-06-23 |
| Genre | : Science |
| ISBN | : 0309454204 |
The social cost of carbon (SC-CO2) is an economic metric intended to provide a comprehensive estimate of the net damages - that is, the monetized value of the net impacts, both negative and positive - from the global climate change that results from a small (1-metric ton) increase in carbon-dioxide (CO2) emissions. Under Executive Orders regarding regulatory impact analysis and as required by a court ruling, the U.S. government has since 2008 used estimates of the SC-CO2 in federal rulemakings to value the costs and benefits associated with changes in CO2 emissions. In 2010, the Interagency Working Group on the Social Cost of Greenhouse Gases (IWG) developed a methodology for estimating the SC-CO2 across a range of assumptions about future socioeconomic and physical earth systems. Valuing Climate Changes examines potential approaches, along with their relative merits and challenges, for a comprehensive update to the current methodology. This publication also recommends near- and longer-term research priorities to ensure that the SC- CO2 estimates reflect the best available science.
| Author | : James P. Barrett |
| Publisher | : |
| Total Pages | : 66 |
| Release | : 2002 |
| Genre | : Climatic changes |
| ISBN | : |
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 351 |
| Release | : 2016-08-22 |
| Genre | : Science |
| ISBN | : 0309388805 |
As the nation's economic activities, security concerns, and stewardship of natural resources become increasingly complex and globally interrelated, they become ever more sensitive to adverse impacts from weather, climate, and other natural phenomena. For several decades, forecasts with lead times of a few days for weather and other environmental phenomena have yielded valuable information to improve decision-making across all sectors of society. Developing the capability to forecast environmental conditions and disruptive events several weeks and months in advance could dramatically increase the value and benefit of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices. Over the past decade, the ability to forecast weather and climate conditions on subseasonal to seasonal (S2S) timescales, i.e., two to fifty-two weeks in advance, has improved substantially. Although significant progress has been made, much work remains to make S2S predictions skillful enough, as well as optimally tailored and communicated, to enable widespread use. Next Generation Earth System Predictions presents a ten-year U.S. research agenda that increases the nation's S2S research and modeling capability, advances S2S forecasting, and aids in decision making at medium and extended lead times.
| Author | : National Intelligence Council |
| Publisher | : Cosimo Reports |
| Total Pages | : 158 |
| Release | : 2021-03 |
| Genre | : |
| ISBN | : 9781646794973 |
"The ongoing COVID-19 pandemic marks the most significant, singular global disruption since World War II, with health, economic, political, and security implications that will ripple for years to come." -Global Trends 2040 (2021) Global Trends 2040-A More Contested World (2021), released by the US National Intelligence Council, is the latest report in its series of reports starting in 1997 about megatrends and the world's future. This report, strongly influenced by the COVID-19 pandemic, paints a bleak picture of the future and describes a contested, fragmented and turbulent world. It specifically discusses the four main trends that will shape tomorrow's world: - Demographics-by 2040, 1.4 billion people will be added mostly in Africa and South Asia. - Economics-increased government debt and concentrated economic power will escalate problems for the poor and middleclass. - Climate-a hotter world will increase water, food, and health insecurity. - Technology-the emergence of new technologies could both solve and cause problems for human life. Students of trends, policymakers, entrepreneurs, academics, journalists and anyone eager for a glimpse into the next decades, will find this report, with colored graphs, essential reading.
| Author | : Craig Pirrong |
| Publisher | : Cambridge University Press |
| Total Pages | : 238 |
| Release | : 2011-10-31 |
| Genre | : Business & Economics |
| ISBN | : 1139501976 |
Commodities have become an important component of many investors' portfolios and the focus of much political controversy over the past decade. This book utilizes structural models to provide a better understanding of how commodities' prices behave and what drives them. It exploits differences across commodities and examines a variety of predictions of the models to identify where they work and where they fail. The findings of the analysis are useful to scholars, traders and policy makers who want to better understand often puzzling - and extreme - movements in the prices of commodities from aluminium to oil to soybeans to zinc.
| Author | : John C. Brocklebank, Ph.D. |
| Publisher | : SAS Institute |
| Total Pages | : 384 |
| Release | : 2018-03-14 |
| Genre | : Computers |
| ISBN | : 1629605441 |
To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.
