Stochastic multifactor models in risk management of energy futures

2020 ◽  
Vol 40 (12) ◽  
pp. 1918-1934
Author(s):  
Zi‐Yi Guo
Keyword(s):  
Risk Management ◽  
Multifactor Models ◽  
Energy Futures

scholarly journals Use of Mulifactor Models with Garch Structure in Carbon Emissions Risk Management

2018 ◽  
Vol 1 (1) ◽  
pp. 407-413
Author(s):  
Aneta Włodarczyk ◽  
Marta Kadłubek
Keyword(s):  
Risk Management ◽  
Carbon Emission ◽  
Phase Iii ◽  
Management Process ◽  
Eu Ets ◽  
Price Changes ◽  
Multifactor Models ◽  
Energy Companies ◽  
The Impact ◽  
The Eu

Abstract Tightening the environmental norms that result from the priorities of the EU 2030 Energy and Climate Package and the reform of the EU ETS have caused the necessity to implement an effective system of managing the risk of carbon dioxide emission and integrate it with the existing enterprise management system. Evaluation of the direction and strength of correlation between EUA price changes and energy companies stock price returns is crucial from point of view the managerial staff making proper decisions about the use of the CO2 emission permits by energy companies. It is an important stage of carbon emission risk management process. The aim of this paper is to verify the possibility of use the multifactor models with GARCH structure as a tool supporting the carbon emission management process in energy companies. Empirical analysis is connected with the estimation of multifactor models with GARCH structure in the Phase II and Phase III of the EU ETS functioning for two groups of Polish energy companies: group of the Respect Index companies and others. Such an approach allows to check whether the Respect Index companies are more robust than others on the carbon emission risk, in particular the EUA price risk associated with the intensification works on modifying the EU ETS functioning. We found that the impact of EUA price changes on energy companies stock returns and their volatility is statistically insignificant in case of all Respect Index companies.

Efficient and exact simulation of the Gaussian affine interest rate models

2016 ◽  
Vol 03 (02) ◽  
pp. 1650009 ◽  
Author(s):  
Vladimir Ostrovski
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Interest Rate ◽  
Conditional Distribution ◽  
Joint Distribution ◽  
Time Dependent ◽  
Financial Industry ◽  
Interest Rate Models ◽  
Multifactor Models

The Gaussian affine interest rate models are widely used in the financial industry for pricing, hedging and also risk management purposes. We consider the multifactor models with time dependent parameters. Usually the models are simulated using some appropriate discretization schema because the joint distribution of the stochastic and discounting factors is not known. We derive the exact joint conditional distribution of the stochastic and discounting factors. Additionally we show how an efficient and exact Monte Carlo simulation of the Gaussian affine interest rate models could be performed.

scholarly journals Dynamic Stochastic Factors, Risk Management and the Energy Futures

2017 ◽  
Vol 10 (9) ◽  
pp. 50 ◽  
Author(s):  
Zi-Yi Guo ◽  
Yangxiaoteng Luo
Keyword(s):  
Risk Management ◽  
Crude Oil ◽  
Goodness Of Fit ◽  
Risk Measures ◽  
Futures Contracts ◽  
Oil Futures ◽  
Energy Futures ◽  
Crude Oil Prices ◽  
Dynamic Stochastic ◽  
Energy Products

The world crude oil prices have dropped dramatically, and consequently the oil market has become very volatile and risky in the last several years. Since energy markets play very important roles in the international economy and have led several global economic crises, risk management of energy products prices becomes very important for both academicians and market participants. Schwartz and Smith’s model (2000) is applied to calculate risk measures of Brent oil futures contracts and light sweet crude oil (WTI) futures contracts. The model includes a long-term factor and a short-term factor. We show that the two factors explain the Samuelson effect well and the model present well goodness of fit. Our back testing results demonstrate that the models provide satisfactory risk measures for listed crude oil futures contracts.

Quality and Risk Management in the IVF Laboratory

2004 ◽  
Author(s):  
David Mortimer ◽  
Sharon T. Mortimer
Keyword(s):  
Risk Management
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