scholarly journals Empirical Evidence on Time-Varying Hedging Effectiveness of Emissions Allowances under Departures from the Cost-of-Carry Theory

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Kai Chang
Keyword(s):  
Futures Market ◽  
Time Varying ◽  
Conditional Volatility ◽  
Conditional Correlation ◽  
Spot Prices ◽  
Market Participants ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Cost Of Carry ◽  
The Cost

Under departures from the cost-of-carry theory, traded spot prices and conditional volatility disturbed from futures market have significant impacts on futures price of emissions allowances, and then we propose time-varying hedge ratios and hedging effectiveness estimation using ECM-GARCH model. Our empirical results show that conditional variance, conditional covariance, and their correlation between between spot and futures prices exhibit time-varying trends. Conditional volatility of spot prices, conditional volatility disturbed from futures market, and conditional correlation of market noises implied from spot and futures markets have significant effects on time-varying hedge ratios and hedging effectiveness. In the immature emissions allowances market, market participants optimize portfolio sizes between spot and futures assets using historical market information and then achieve higher risk reduction of assets portfolio revenues; accordingly, we can obtain better hedging effectiveness through time-varying hedge ratios with departures from the cost-of-carry theory.

scholarly journals HOW EFFECTIVE IS FUTURES AS AN INSTRUMENT OF HEDGING AGAINST PRICE RISK? -A STUDY BASED ON SPOT AND FUTURE PRICES OF GOLD

2016 ◽  
Vol 4 (9) ◽  
pp. 143-150
Author(s):  
Shafeeque Muhammad ◽  
Thomachan
Keyword(s):  
Spot Market ◽  
Futures Market ◽  
Price Risk ◽  
Time Varying ◽  
Risk Hedging ◽  
Hedging Effectiveness ◽  
Cash Market ◽  
Hedge Ratios ◽  
Opposite Position

This paper examines the role of commodity futures market as an instrument of hedging against price risk. Hedging is the practice of offsetting the price risk in a cash market by taking an opposite position in the futures market. By taking a position in the futures market, which is opposite to the position held in the spot market, the producer can offset the losses in the latter with the gains in the former. Both static and time varying hedge ratios have been calculated using VECM-MGARCH model. Variance of return from hedge portfolio has been found to be low. Further hedging effectiveness has been observed to be around 12%.

The Optimal Wheat Futures Hedge at the Euronext Paris from a Farmer’s Perspective

2020 ◽  
Vol 69 (1) ◽  
pp. 49-63
Author(s):  
Teresa Vollmer
Keyword(s):  
Commodity Prices ◽  
Futures Market ◽  
Futures Contracts ◽  
Risk Minimization ◽  
Price Shocks ◽  
Market Participants ◽  
Hedge Ratios ◽  
Optimal Dynamic ◽  
One Year ◽  
The Cost

Futures contracts are extensively used by commer-cial market participants to hedge commodities against the risk of adverse price fluctuations. But although farmers have faced increased volatility in commodity prices in recent years, only very few of them actively use hedging as a risk management instrument. In this article we analyze the hedging potential of the Euronext milling wheat futures market for German farmers based on the estimation of optimal static as well as optimal dynamic hedge ratios. We find that both hedging approximately one year and half a year before harvesting leads to a reduction in the variance of returns compared with unhedged portfolios. But this risk minimization is achieved at the cost of lower returns on average. In addition we find that margin calls might be one of the reasons why so few farmers hedge since they cause liquidity problems especially in marketing years with unanticipated price shocks.

Optimal Hedge Ratio and Hedging Effectiveness in Stock Futures Market: Evidence from National Stock Exchange, India

2019 ◽  
Vol 118 (3) ◽  
pp. 137-152
Author(s):  
A. Shanthi ◽  
R. Thamilselvan
Keyword(s):  
Banking Sector ◽  
Stock Exchange ◽  
Research Work ◽  
Futures Market ◽  
Least Square ◽  
Time Varying ◽  
Hedge Ratio ◽  
Hedging Effectiveness ◽  
Out Of Sample ◽  
Optimal Hedge

The major objective of the study is to examine the performance of optimal hedge ratio and hedging effectiveness in stock futures market in National Stock Exchange, India by estimating the following econometric models like Ordinary Least Square (OLS), Vector Error Correction Model (VECM) and time varying Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model by evaluating in sample observation and out of sample observations for the period spanning from 1st January 2011 till 31st March 2018 by accommodating sixteen stock futures retrieved through www.nseindia.com by considering banking sector of Indian economy. The findings of the study indicate both the in sample and out of sample hedging performances suggest the various strategies obtained through the time varying optimal hedge ratio, which minimizes the conditional variance performs better than the employed alterative models for most of the underlying stock futures contracts in select banking sectors in India. Moreover, the study also envisage about the model selection criteria is most important for appropriate hedge ratio through risk averse investors. Finally, the research work is also in line with the previous attempts Myers (1991), Baillie and Myers (1991) and Park and Switzer (1995a, 1995b) made in the US markets

Pricing of Futures Contracts

2018 ◽  
Author(s):  
Timothy A. Krause
Keyword(s):  
Fair Value ◽  
Commodity Futures ◽  
Futures Contracts ◽  
Spot Price ◽  
Futures Prices ◽  
No Arbitrage ◽  
Spot Prices ◽  
Cost Of Carry ◽  
Contract Expiration ◽  
The Cost

This chapter examines the relation between futures prices relative to the spot price of the underlying asset. Basic futures pricing is characterized by the convergence of futures and spot prices during the delivery period just before contract expiration. However, “no arbitrage” arguments that dictate the fair value of futures contracts largely determine pricing relations before expiration. Although the cost of carry model in its various forms largely determines futures prices before expiration, the chapter presents alternative explanations. Related commodity futures complexes exhibit mean-reverting behavior, as seen in commodity spread markets and other interrelated commodities. Energy commodity futures prices can be somewhat accurately modeled as a generalized autoregressive conditional heteroskedastic (GARCH) process, although whether these models provide economically significant excess returns is uncertain.

One-Factor and Two-Factor Dynamic Hedging of Futures Contracts with Different Maturities for Emissions Allowances

2013 ◽  
Vol 380-384 ◽  
pp. 4529-4536
Author(s):  
Chang Kai ◽  
Zhen Yu
Keyword(s):  
Risk Reduction ◽  
Market Price ◽  
Futures Contracts ◽  
Effectiveness Evaluation ◽  
Time Varying ◽  
Slight Effect ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Hedging Policy ◽  
Speed Change

Unexpected market information have a different speed change to market price of futures contracts with different maturities, and the paper estimates one-factor and two-factor dynamics hedge ratios and hedging effectiveness evaluation. One-factor and two-factor hedge ratios of futures contracts with different maturities for emissions allowances have time-varying trends. Compared with one-factor hedging, with an increase of span period, market participations can achieve a slight effect on risk reduction of portfolio revenues of futures contracts with different maturities by using two-factor hedge ratios, and especially two-factor hedging policy exhibits better hedging effectiveness for longer-term span period of futures contracts with different maturities for emissions allowances.

The Unbiasedness and Hedging Effectiveness in KOSPI200 Futures Market

2007 ◽  
Vol 15 (1) ◽  
pp. 73-100
Author(s):  
Seok Kyu Kang
Keyword(s):  
Risk Management ◽  
Risk Premium ◽  
Futures Market ◽  
Spot Price ◽  
Sample Period ◽  
Futures Prices ◽  
Spot Prices ◽  
Hedging Effectiveness ◽  
Futures Price ◽  
Rolling Windows

This study is to examine the unblasedness hypothesis and hedging effectiveness in KOSPI20() futures market. The unbiasedness and efficiency hypothesis is carried out using a cointegration methodology. And hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS hedge model. and constant correlation bivariate GARCH (1. 1) hedge model based on rolling windows. The sample period covers from May. 3. 1996 to December. 8, 2005. The empirical results are summarized as follows: First, there exists the cOintegrating relationship between realized spot prices and futures prices of the 10 day. 22 day. 44 day. and 59 day prior to maturity. Second. futures prices of backward the 10 day. 22 day. 44 day from maturity provide unbiased forecasts of the realized spot prices. The KOSPI200 futures price is likely to predict accurately future KOSPI200 spot prices without the trader having to pay a risk premium for the privilege of trading the contract. Third. for shorter maturity. the futures price appears to be the best forecaster of spot price. Forth, bivariate GARCH hedging effectiveness outperforms the naive and OLS hedging effectiveness. The implications of these findings show that KOSPI200 futures market behaves as unbiased predictor of future spot price and risk management instrument of KOSPI200 spot portfolio.

Market efficiency and the basis in the European Union Emissions Trading Scheme

2014 ◽  
Vol 41 (4) ◽  
pp. 615-628 ◽  
Author(s):  
Andros Gregoriou ◽  
Jerome Healy ◽  
Nicola Savvides
Keyword(s):  
Emissions Trading ◽  
Eu Ets ◽  
The European Union ◽  
Content Type ◽  
Spot Prices ◽  
Emissions Trading Scheme ◽  
Cost Of Carry ◽  
Trading Scheme ◽  
The Cost ◽  
The Eu

Purpose – The purpose of this paper is to investigate the validity of the cost of carry model by examining the time series properties of the deviation between future and spot prices in the European Union Emissions Trading Scheme (EU-ETS) over the time period 2005-2012. The paper utilizes a non-linear mean reverting adjustment mechanism, and discovers that although deviations of future from spot prices can exhibit a region of non-stationary behaviour, overall they are stationary indicating market efficiency in the trading of carbon permits. Design/methodology/approach – The methodology involves non-linear mean reverting unit root tests. Findings – The findings provide insights into the functioning of the EU-ETS market. They suggest that it is informationally efficient and does not permit arbitrage between spots and futures. Originality/value – The authors are the first study to examine efficiency in the EU-ETS by investigating the validity of the cost of carry model. The authors are also the only study to look at efficiency in both Phase I and Phase II of the scheme.

Comparison of commodity future pricing approaches with cointegration techniques

2015 ◽  
Vol 02 (01) ◽  
pp. 1550002 ◽  
Author(s):  
Christian Stepanek
Keyword(s):  
Risk Premium ◽  
Inventory Level ◽  
Convenience Yield ◽  
Price Expectations ◽  
Spot Prices ◽  
Cointegration Tests ◽  
Cost Of Carry ◽  
The Cost ◽  
Commodity Future ◽  
Theory Of Storage

Commodity future prices are explained either by price expectations and a risk premium in the theory of normal backwardation or with the theory of storage in a cost of carry valuation. Both approaches are compared in separate equations with Johansen cointegration tests. The data sample contains five LME metals with maturities of 3–27 months and real inventory data. It is found that expected spot prices explain only short maturity future prices. But the cost of carry approach, with the inventory level-dependent convenience yield, explains prices for all maturities.

Volatility persistence and trading volume in an emerging futures market

2010 ◽  
Vol 11 (3) ◽  
pp. 296-309 ◽  
Author(s):  
Pratap Chandra Pati ◽  
Prabina Rajib
Keyword(s):  
Capital Market ◽  
Trading Volume ◽  
Price Volatility ◽  
Futures Market ◽  
Market Volatility ◽  
Leverage Effect ◽  
Time Varying ◽  
Conditional Volatility ◽  
Content Type ◽  
Futures Contract

PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.

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