A simplified pricing model for volatility futures

We develop a general model to price VIX futures contracts. The model is adapted to test both the constant elasticity of variance (CEV) and the Cox–Ingersoll–Ross formulations, with and without jumps. Empirical tests on VIX futures prices provide out-of-sample estimates within 2% of the actual futures price for almost all futures maturities. We show that although jumps are present in the data, the models with jumps do not typically outperform the others; in particular, we demonstrate the important benefits of the CEV feature in pricing futures contracts. We conclude by examining errors in the model relative to the VIX characteristics

Introduction of electronic trading in index futures : volatility and volume effects

There is continuing debate in the US over full introduction of electronic trading in those index futures contracts that are still traded at the CME via open outcry. Since the late 1990s major international exchanges trading index futures contracts have converted to full electronic trading. Recent empirical studies have focused on effects on bid/ask spreads and related price volatility following these changes. We take a different approach and investigate and test for structural change in conditional volatility and volume effects following the shift to electronic trading in the Australian Share Price Index futures contract. Multiple Switching point GARCH models are employed with the data sampled at 5, 15 and 30-minute intervals from transaction records supplied by the Sydney Futures Exchange. There is significant evidence of structural changes in both the persistence of volatility shocks and simultaneous volume effects following the change to screen trading in this futures market.

A Study on Futures Trading in Commodities With Special Reference to Rubber

Futures trading in Commodities has three specific economic functions viz. price discovery, hedging and reduction in volatility. Natural rubber possesses all the specifications required for futures trading. Commodity futures trading in India attained momentum after the starting of national level commodity exchanges in 2003. The success of futures trading depends upon effective price risk management, price discovery and reduced volatility which in turn depends upon the volume of trading. In the case of rubber futures market, the volume of trading depends upon the extent of participation by market players like growers, dealers, manufacturers, rubber marketing co-operative societies and Rubber Producer’s Societies (RPS). The extent of participation by market players has a direct bearing on their awareness level and their perception about futures trading. In the light of the above facts and the review of literature available on rubber futures market, it is felt that a study on rubber futures market is necessary to fill the research gap, with specific focus on (1) the awareness and perception of rubber futures market participants viz. (i) rubber growers, (ii) dealers, (iii) rubber product manufacturers, (iv) rubber marketing co-operative societies and Rubber Producer’s Societies (RPS) about futures trading and (2) whether the rubber futures market is fulfilling the economic functions of futures market viz. hedging, reduction in volatility and price discovery or not. The study is confined to growers, dealers, rubber goods manufacturers, rubber marketing co-operative societies and RPS in Kerala. In order to achieve the stated objectives, the study utilized secondary data for the period from 2003 to 2013 from different published sources like bulletins, newsletters, circulars from NMCE, Reserve Bank of India (RBI), Warehousing Corporation and traders. The primary data required for this study were collected from rubber growers, rubber dealers, RPS & Rubber Marketing Co-operative Societies and rubber goods manufacturers in Kerala. Data pertaining to the awareness and perception of futures trading, participation in the futures trading, use of spot and futures prices and source of price information by dealers, farmers, manufacturers and cooperative societies also were collected. Statistical tools used for analysis include percentage, standard deviation, Chi-square test, Mann – Whitney U test, Kruskal Wallis test, Augmented Dickey – Fuller test statistic, t- statistic, Granger causality test, F- statistic, Johansen co – integration test, Trace statistic and Max –Eigen statistic. The study found that 71.5 per cent of the total hedges are effective and 28.5 per cent are ineffective for the period under study. It implies that futures market in rubber reduced the impact of price risks by approximately 71.5 per cent. Further, it is observed that, on 54.4 per cent occasions, the futures market exercised a stabilizing effect on the spot market, and on 45.6 per cent occasions futures trading exercised a destabilizing effect on the spot market. It implies that elasticity of expectation of futures market in rubber has a predominant stabilizing effect on spot prices. The market, as a whole, exhibits a bias in favour of long hedges. Spot price volatility of rubber during futures suspension period is more than that of the pre suspension period and post suspension period. There is a bi-directional association-ship or bi-directional causality or pair- wise causality between spot price and futures price of rubber. From the results of the hedging efficiency, spot price volatility, and price discovery, it can be concluded that rubber futures market fulfils all the economic functions expected from a commodity futures market. Thus in India, the future of rubber futures is Bright…!!!

"Information effect" of economic news: SPI futures

The present study investigates the behaviour of Share Price Index (SPI) futures returns, volatility, and trading volume behaviour around the announcement of Current Account Deficit (CAD), Gross Domestic Product (GDP), and Inflation (CPI). The futures market data are sampled at 1-, 5-, and 10-min intervals at the announcement time. After controlling for risk, a significant positive abnormal return can be earned based on the good news release. However, it is unlikely that traders could make an economic profit by exploiting this effect. In this sense, this futures market returns are found to react efficiently to good news. Volatility behaviour around announcements provides the same conclusion. As for the relationship between returns, volatility, and volume upon information arrival, returns are positively related to trading volume, which is inconsistent with the ‘short sales constraint’ theory. Trading volume is found to increase as the level of volatility rises. The redenomination of the SPI futures and options contract from A$100 to A$25 per basis point is found to increase trading volume in excess of that expected due to the redenomination. However, market return and volatility are unaffected by the redenomination.

Market makers v's the general public : a first look at S & P500 futures trade data

This paper considers 15 minute records of trading volume and traded prices coinciding with the reporting intervals required by the Commodity Futures Trading Commission. Records are extracted from trade records for market trade and also two way trade between market makers (CT1) and the general public (CT4) from January 1994 to June 2004. Futures price records are matched with S&P500 cash index price records. Simultaneous volatility models are specified and estimated to test trading volume to futures volatility lead/lag effects and also futures volatility to cash index volatility lead/lag effects. As we disaggregate from the market records to CT1 and CT4 records and further into year to year samples volume to futures volatility leading effects and also futures volatility to cash volatility leading effects dominate. The results raise important issues for risk management and dynamic hedging models employing intra-day trader data. A number of important issues for further analysis are also raised in this paper.

Forecasting stock index volatility : comparing implied volatility and the intraday high-low price range

The intraday high–low price range offers volatility forecasts similarly efficient to high-quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in-sample and out-of-sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.

Dispersion of information or market behaviour : general public trading in S & P 500 index futures

This paper considers 15 minute records of trading volume and traded prices coinciding with the reporting intervals required by the Commodity Futures Trading Commission. Records are extracted from trade records for two way trade between market makers (CTI1) and the general public (CTI4) from January 1994 to June 2004. Futures price records are matched with S&P500 cash index price records. Simultaneous volatility models are specified and estimated to test trading volume to futures volatility lead/lag effects and also futures volatility to cash index volatility lead/lag effects. There is evidence that existing theoretical models of the general public trading behaviour do not explain such behaviour in these very actively traded markets. These effects can depend more on market conditions than what is suggested in theoretical models.

The jump component of S&P 500 volatility and the VIX index

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing of past volatility to generate forecasts. Implied volatility has the potential to reflect information that a model-based forecast could not. This paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information pertaining to future jump activity relative to model-based forecasts. It is found that the VIX index both subsumes information relating to past jump contributions to total volatility and reflects incremental information pertaining to future jump activity. This issue has not been examined previously and expands our understanding of how option markets form their volatility forecasts.

Valuation of wind energy projects: A real options approach

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Valuation of Wind Energy Projects: A Real Options Approach

We address the valuation of an operating wind farm and the finite-lived option to invest in it under different reward/support schemes: a constant feed-in tariff, a premium on top of the electricity market price (either a fixed premium or a variable subsidy such as a renewable obligation certificate or ROC), and a transitory subsidy, among others. Futures contracts on electricity with ever longer maturities enable market-based valuations to be undertaken. The model considers up to three sources of uncertainty: the electricity price, the level of wind generation, and the certificate (ROC) price where appropriate. When analytical solutions are lacking, we resort to a trinomial lattice combined with Monte Carlo simulation; we also use a two-dimensional binomial lattice when uncertainty in the ROC price is considered. Our data set refers to the UK. The numerical results show the impact of several factors involved in the decision to invest: the subsidy per MWh generated, the initial lump-sum subsidy, the maturity of the investment option, and electricity price volatility. Different combinations of variables can help bring forward investments in wind generation. One-off policies, e.g., a transitory initial subsidy, seem to have a stronger effect than a fixed premium per MWh produced.

Volume, volatility, and leverage: A dynamic analysis

This paper uses dynamic impulse response analysis to investigate the interrelationships among stock price volatility, trading volume, and the leverage effect. Dynamic impulse response analysis is a technique for analyzing the multi-step-ahead characteristics of a nonparametric estimate of the one-step conditional density of a strictly stationary process. The technique is the generalization to a nonlinear process of Sims-style impulse response analysis for linear models. In this paper, we refine the technique and apply it to a long panel of daily observations on the price and trading volume of four stocks actively traded on the NYSE: Boeing, Coca-Cola, IBM, and MMM.

Intraday volatility

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Seasonality and the valuation of commodity options

Price movements in many commodity markets exhibit significant seasonal patterns. However, given an observed futures price, a deterministic seasonal component at the price level is not relevant for the pricing of commodity options. In contrast, this is not true for the seasonal pattern observed in the volatility of the commodity price. Analyzing an extensive sample of soybean, corn, heating oil and natural gas options, we find that seasonality in volatility is an important aspect to consider when valuing these contracts. The inclusion of an appropriate seasonality adjustment significantly reduces pricing errors in these markets and yields more improvement in valuation accuracy than increasing the number of stochastic factors.

Structure, conduct and the stochastic volatility of food markets: theory and empirics

The relationship between price volatility and competition is examined. Atheoretic, vector auto regressions on farm prices of wheat and retail prices of derivatives (flour, bread, pasta, bulgur and cookies) are compared to results from a dynamic, simultaneous-equations model with theory-based farm-to-retail linkages. Analytical results yield insights about numbers of firms and their impacts on demand- and supply-side multipliers, but the applications to Turkish time series (1988:1-1996:12) yield mixed results.