Scenario Reduction for Futures Market Trading in Electricity Markets

This study measures the connectedness of natural gas and electricity spot returns to their futures returns with different maturities. We employ the Henry Hub and the Pennsylvania, New Jersey, and Maryland (PJM) Western Hub Peak as the natural gas price indicator and the wholesale electricity price indicator, respectively. We also use each commodity’s spot prices and 12 types of futures prices with one to twelve months maturities and realize results in fourfold. First, we observe mutual spillover effects between natural gas futures returns and learn that the natural gas futures market is integrated. Second, we observe the spillover effects from natural gas futures returns to natural gas spot returns (however, the same is not evident for natural gas spot returns to natural gas futures returns). We find that futures markets have better natural gas price discovery capabilities than spot markets. Third, we observe the spillover effects from natural gas spot returns to electricity spot returns, and the spillover effects from natural gas futures returns to electricity futures returns. We learn that the marginal cost of power generation (natural gas prices) is passed through to electricity prices. Finally, we do not observe any spillover effects amongst electricity futures returns, except for some combinations, and learn that the electricity futures market is not integrated.

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

Restaurant Business ◽

10.26643/rb.v118i3.7637 ◽

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

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Hedging Effectiveness in Korean Gold Futures Market

Journal of Economic Studies ◽

10.30776/jes.36.3.6 ◽

2018 ◽

Vol 36 (3) ◽

pp. 147-164

Author(s):

Moohwan KIM

Keyword(s):

Futures Market ◽

Hedging Effectiveness ◽

Gold Futures

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The profitability of algorithmic trading systems based on football sentiment

International Sports Studies ◽

10.30819/iss.42-1.04 ◽

2020 ◽

Vol 42 (1) ◽

pp. 33-46

Author(s):

Raúl Gómez-Martínez ◽

Camila Marqués-Bogliani ◽

Jessica Paule-Vianez

Keyword(s):

Financial Markets ◽

Investment Decisions ◽

Futures Market ◽

Algorithmic Trading ◽

Investment Strategies ◽

Measurable Effect ◽

Behavioural Finance ◽

Trading Systems ◽

European Football

Behavioural finance has shown that investment decisions are the result of not just rational but also emotional brain processes. On the assumption that emotions affect financial markets, it would seem likely that football results might have a measurable effect on financial markets. To test this, this study describes three algorithmic trading systems based exclusively on the results of three top European football teams (Juventus, Bayern München and Paris St Germain) opening long or short positions in the next market season of the futures market of the index of each country (MIB (Milano Italia Borsa), DAX (Deutscher Aktien Index) and CAC (Cotation Assistée en Continu). Depending on the outcome of the last game played a long position was taken after a victory and a short position after a draw or defeat. The results showed that the algorithmic systems were profitable in the case of Juventus and Bayern whereas in the case of PSG, the system was profitable, but in an inverse way. This study shows that investment strategies that take account of sports sentiment could have a profitable outcome.

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