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This study aims to spot wheat data and disaggregated commitment of trader data for CME traded wheat futures to examine the effect of exogenous shocks for hedging positions of Producers and Swap Dealers on cash-futures basis and excess futures returns.

A Bayesian vector autoregression (BVAR) methodology is used to capture volatility transfer effects.

Evidence is presented that institutional short hedging positions play a major role in the pricing of asymmetric information held by Swap Dealers into the basis. The results also indicate that producer hedging contains information when conditions in the supply chain create a shift in long vs short hedging demand. Finally, the results demonstrate that that Swap Dealer short hedging has the greatest effect on risk premium size and historical volatility.

Various proxies for spot prices are used in the literature, although actual spot price data is not common. In addition, stationarity for basis and open interest data is induced using the Baxter-King filter which allows us to work with levels, rather than percentage changes, in the time series data. This provides the ability to directly observe the effect of outright open interest positions for hedgers on contemporaneous innovations in basis and in excess returns. The use of a BVAR methodology represents an improvement over other structural VAR models by capturing contemporaneous systemic effects within an endogeneity based structural framework.

Because VIX has a negative correlation to the S&P 500 index and a number of hedge fund strategies, the literature has suggested that long positions in VIX can reduce the risk and higher moment exposures of these investments. However, the VIX index is not tradeable. VIX futures are traded, but have materially different performance from the VIX index. The front month futures underperformed by over 4% per month between March 2004 and July 2009. Over this time period, the front month futures had a correlation of 0.84 to, and a volatility 60% of that of, the VIX index. The second month futures contract underperformed by almost 1% per month with a correlation of 0.76 and a standard deviation of only 40% of the VIX index. While a significant negative risk premium exists in VIX futures, the attractive positive skewness and excess kurtosis properties of the futures are similar to those of the index. Both VIX futures and the VIX index are asymmetric, rising more quickly as the S&P 500 index falls and falling more slowly as stock prices rise.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

Due to the low crop insurance participation by grain growers in the Pacific Northwest, the performance of insurance programs and the futures market is assessed in this area. Revenue insurance, combined with the futures and government programs, is identified as the optimal risk management portfolio. Although yield risk level, decision maker’s risk preference, and actuarial fairness of premiums can all affect farmers’ choices, the current subsidy policy is most influential. The varying subsidy levels induce farmers’ subsidy‐seeking incentive and suppress the risk‐reducing incentive. There is little diversification effect from growing two crops in the rotation instead of one.

In this paper weekly volatility forecasts are considered with applications to risk management; in particular hedge ratios and VaR calculations, with the aim of identifying the most appropriate model for risk management practice.

The study considers a variety of models, including those typically employed within the risk management industry, such as averaging and smoothing techniques, as well as those favored in academic circles, such as the GARCH genre of models, and a more recent realized volatility approach which incorporates both the simplicity in construction favored by the finance industry and the flexibility and theoretical underpinnings recommended by academics.

The results support the view that this realized volatility measure provides not only superior volatility forecasts per se, but also allows for improved hedge ratio and VaR calculations.

The research findings carry practical implications for the conduct of risk management, namely that volatility forecasts are best obtained using the realized volatility approach.

It is therefore proposed that a future direction for risk management practice may be to utilize such measures, while more generally it is hoped that such approaches may improve the cross‐fertilization of ideas and practice between the academic and practitioner communities.

Accountancy is no mystic, druidic activity but a means devised by practical people to express the use of resources (and any returns from commercial activities) in a common measure—money.

This paper presents a brief review of potential developments in optical discs and online databases in the 1990s, and describes advances in technology that may affect our use of these media, changes in the market that will emerge in the next decade, associated problems that could arise as a result of these developments and finally, some pointers to future trends are illustrated by selected recently‐announced projects and services.

The purpose of this paper is to examine the lead‐lag relationships between the National Stock Exchange (NSE) Nifty stock market index (in India) and its related futures and options contracts, and also the interrelation between the derivatives markets.

The paper uses serial correlation of return series and autoregressive moving average (ARMA) model for studying the lead‐lag relationship between hourly returns on the NSE Nifty index and its derivatives contracts like futures, call and put options. Further, the lead‐lag relation between hourly returns of the derivatives contracts among themselves is also studied using ARMA models.

The ARMA analysis shows that the NSE Nifty derivatives markets tend to lead the underlying stock index. The futures market clearly leads the cash market although this lead appears to be eroding slightly over time. Although the options market leads the cash overall, there is some feedback between the two with the underlying index leading at times. Further, it is found that the index call options lead the index futures more strongly than futures lead calls, while the futures lead puts more strongly than the reverse.

The results imply that the derivative contracts on NSE Nifty lead the underlying cash market. Thus, the derivative markets are indicative of futures price movements and this will certainly be helpful to potential investors to design their risk‐return portfolio while investing in stocks and derivatives contracts.

This paper is an original piece of work towards exploring the lead‐lag relation between NSE Nifty and the derivative contracts. The issue of price discovery on futures and spot markets and the lead‐lag relationship are topics of interest to traders, financial economists, and analysts.

Dialog files suit against ACS On 7 June 1990, Dialog Information Services filed a suit against the American Chemical Society (ACS) in the United States District Court for the District of Columbia. In its suit, Dialog charged that the Society had caused the company $50 million in lost revenues by limiting Dialog's access to information in the CAS database, by withdrawing access to previously available data, and by other monopolistic practices. It seeks recompense of $150 million, including $100 million in punitive damages, and relief from outstanding royalty claims from the ACS.