The Lead Lag Relationship Between Spot and Futures Markets In the Energy Sector PDF Download
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Author:
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Languages : en
Pages : 50
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Author:
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Languages : en
Pages : 50
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Author: Joseph K. W. Fung
Publisher:
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Category : Short selling (Securities)
Languages : en
Pages : 37
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Author: Joseph K. W. Fung
Publisher:
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Category :
Languages : en
Pages :
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Book Description
We examine the lead-lag relation between index futures and the underlying index under three types of short-selling restrictions on stocks in Hong Kong. Our results indicate that lifting short-selling restrictions can enhance the informational efficiency of the stock market relative to the index futures. We also investigate the impact of two market characteristics, market conditions and the magnitude of mispricing on the lead-lag relations under different short-selling regimes. Our findings suggest that if we remove restrictions, the contemporaneous price relation between the futures and cash markets becomes stronger particularly in the falling market and when the cash market is relatively overpriced.
Author: Vijay Kumar
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659144936
Category :
Languages : en
Pages : 92
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Indian capital markets have been witnessed a major transformation and structural changes over the past one decade as a result of ongoing financial sector reforms initiated by the Government. This study investigated the lead lag relationship between the spot and futures equity market in India, both in terms of return and volatility, examines the lead lag relationship between the spot and futures markets for asymmetric information and also incorporate price co-integration relationship between spot and futures markets in the lead lag relationship analysis. We employed data in the study consists of intraday price histories from JAN 2001 to November 2005 for the nearby contract of nifty index futures and Index.We find a strong contemporaneous relationship between futures and cash prices, along with some significant evidence that futures markets leads spot market during times of high volatility. Consequently, reactions in futures markets are faster, and movements in futures prices lead spot price fluctuations.
Author: Onem Ozocak
Publisher:
ISBN:
Category : Futures market
Languages : en
Pages : 366
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Author: Menzie David Chinn
Publisher:
ISBN:
Category : Futures
Languages : en
Pages : 34
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"This paper examines the relationship between spot and futures prices for energy commodities (crude oil, gasoline, heating oil markets and natural gas). In particular, we examine whether futures prices are (1) an unbiased and/or (2) accurate predictor of subsequent spot prices. We find that while futures prices are unbiased predictors of future spot prices, with the exception those in the natural gas markets at the 3-month horizon. Futures do not appear to well predict subsequent movements in energy commodity prices, although they slightly outperform time series models"--National Bureau of Economic Research web site.
Author: Chris Brooks
Publisher:
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Category :
Languages : en
Pages :
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This paper examines the lead-lag relationship between the FTSE 100 index and index futures price employing a number of time series models. Using ten-minutely observations from June 1996-1997, it is found that lagged changes in the futures price can help to predict changes in the spot price. The best forecasting model is of the error correction type, allowing for the theoretical difference between spot and futures prices according to the cost of carry relationship. This predictive ability is in turn utilised to derive a trading strategy which is tested under real-world conditions to search for systematic profitable trading opportunities. It is revealed that although the model forecasts produce significantly higher returns than a passive benchmark, the model was unable to outperform the benchmark after allowing for transaction costs.
Author: Xuemei Xiao
Publisher:
ISBN:
Category :
Languages : en
Pages : 74
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Book Description
This thesis investigates the relationship between speculation in futures markets and expected and unexpected volatility in the spot markets for 21 different commodities. I use the index of adequate speculation, INDADSP, and the index of excess speculation, INDEXSP, developed and estimated by Shanker (2017), to capture the degree of speculation required to meet hedging demand, and the degree of speculation in excess of hedging demand, respectively. For comparison, I also use Working's (1960) speculative index T, as a measure of speculation. I estimate the expected volatility (EV) and unexpected volatility (UEV) of the spot market using a GARCH model. The empirical results indicate that the GJR-GARCH model with a Student's t distribution for the error term is the most appropriate model, among the GARCH-family of models, to capture the volatility of 17 of the 21 spot commodity returns. However, the results of feeder cattle indicate the exists of serial correlation of the residuals for all three GARCH model I used, so I drop it and do the further analysis for the rest of 20 commodities and financial contracts. For each commodity, I create time series of matched weekly indices of speculation, expected volatility and unexpected volatility. Next, I investigate the long-run and short-run relationships between volatilities and speculation using an autoregressive distributed lag model. The results indicate that there is a long term relationship between expected and unexpected volatility and the speculative indices, for all commodities, except the Euro, Eurodollar, and U.S. T-bond, and a short term relationship between volatilities and speculation for all commodities. Finally, I apply the Toda-Yamamoto test to investigate the causal relationship between speculation in futures markets and volatility in spot markets. I find that speculation tends to lead expected volatility more than unexpected volatility for the majority of commodities/financial assets. Expected volatility, rather than unexpected volatility, tends to lead speculation for a majority of commodities/financial assets. There is a bidirectional causality between expected volatility and INDADSP, INDEXSP, and T and between unexpected volatility and INDEXSP for several different commodities and financial assets. However, there is no bidirectional causality between unexpected volatility and the speculative indices INDADSP and T for all 20 commodities/financial assets.
Author: Duminda Kuruppuarachchi
Publisher:
ISBN:
Category :
Languages : en
Pages : 49
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Book Description
We investigate sector level information spillover dynamics between energy and other futures market sectors using a novel common factor approach. The heteroscedastic principal component common factors are derived for each market sector using the daily returns of 176 leading futures contracts traded globally during the period 2005-2011 and used them in our analysis. We find that energy sector has the highest degree of commonality among the 8 sectors that we studied. Conditional correlations between energy and non-energy futures market sectors are highly persistent. Granger causality tests in mean, variance, and value at risk reveal that the volatility spillover from the energy sector is more prominent than the mean and extreme market risk spillovers. Extreme energy market shocks have an asymmetric effect on some non-energy market sectors. Impulse response analysis reveals that shocks to energy futures have a significant potential impact on other sectors especially during GFC and EUC crisis. The impact of the extreme market events of the energy sector is dominated by WTI oil futures. Consistency of our findings with the existing literature based on individual asset-to-asset spillovers reveals that our results are quite robust. The proposed common factors are important for other applications as well such as benchmark indices in marker sector-specific asset pricing models.
Author: S. Chatterjee
Publisher: Springer
ISBN: 8132219791
Category : Business & Economics
Languages : en
Pages : 491
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Book Description
The changing dynamics of business worldwide have led organizations to look beyond traditional managerial practices while at the same time attempting to retain their core competitive advantages. This development has called upon academicians and practitioners alike to reassess the different aspects of business management such as macroeconomic variables, the nature of the market, the changing features of the workplace, the new work ethos, and/or employer-employee exchanges. In this context, the book provides essential insights on industry innovations, academic advances and policy movements with regard to recovering markets in India and around the globe. The individual papers highlight potential avenues that could allow industry to better understand and respond to the global crisis. The book collects research papers presented at the Global Conference on Managing in Recovering Markets (GCMRM), held in March 2014. Seven international and 120 national business schools and management universities were represented at the conference, the first in a series of 13 planned under the GCMRM agenda for 2014–17. The book includes more than 30 research papers chosen from a pool of 118 presented at the conference, all of which have undergone a rigorous blind review process.
