Abstract

In the last twenty years, many huge ups and downs have been seen in not only oil prices but also in other spot and derivative' energy prices too. This study has two main purposes. The main purpose of the study is to detect bubbles and their beginning and ending dates in energy derivatives futures prices. Crude oil WTI, natural gas, and heating oil monthly prices are analyzed for the period beginning from 1990 to 2018. Following detecting bubbles, Markov Regime Switching Autoregressive (MSAR) models and Markov Regime Switching Vector Autoregressive (MSVAR) models are used to analyze the movement of the regime-switching mechanism between the bubble dates. The general evidence indicates that the switching mechanism during bubble periods has some mutual similarities as generally their direction is to regime 1 as recession with low/negative returns and high volatility. Following positive return periods in energy prices, mostly after the high return/high volatility periods, the market actors might face bubble collapses.

Highlights

  • IntroductionOil prices, which tended to decline for political and economic reasons in the 1990s, fell to as low as 12 US dollars after the Asian Crisis and soon they began to rise again in 2002

  • Energy prices have undergone major changes since the 1990s

  • The first part gives the results on detecting bubbles with the help of Generalized Sup Augmented Dickey-Fuller (GSADF) tests

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Summary

Introduction

Oil prices, which tended to decline for political and economic reasons in the 1990s, fell to as low as 12 US dollars after the Asian Crisis and soon they began to rise again in 2002. The highest price has been seen for oil prices is 140 US dollars on 1 June 2008. Similar rises have been seen in other energy prices as natural gas and heating oil too. Natural gas prices have been in many huge ups and downs. The natural gas prices exceeding 12 US dollars in 2005 and 2008 are under 3 US dollars in 2019. As well as other energy prices, the heating oil prices exceeding 3.90 US dollars are around 1.96 in 2019. Analyzing those 2-4 times price differences have got increasing importance for researchers

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