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This study seeks to evaluate the impacts of land rental arrangements on crop insurance and grain marketing decisions.

The analysis is conducted in an Illinois corn‐soybean setting in which optimal marketing and crop insurance decisions are estimated for a risk‐averse producer under typical cash rent and share rent agreements using numerical simulation methods.

Results indicate that the availability of crop insurance impacts the intensity of use of put options under both cash and share rent arrangements. Similar to previous work in this area, revenue insurance is found to cause a substitution away from marketing using put options, while yield insurance is complementary to price risk management alternatives. However, while insurance and marketing play a role under both types of land tenure arrangements, shifting from a cash rent to a share rent agreement provides a relatively greater degree of risk reduction.

The results suggest that additional research is needed to explain trends in land rental contracts. Crop insurance and other federal programs may provide incentives to switch from share leases to cash rent arrangements. Changes to the design of these programs could facilitate risk management for producers more efficiently.

The unique contribution of this study is the comparison of insurance and marketing decisions under both cash rent and share rent agreements for crop land.

The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.

The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.

There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.

Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.

The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.

Empirical evidence of the hedging pressure risk premium exists only in the futures contracts with delivery-related options. Since hedging pressure is supposed to exist for all futures contracts, the empirical evidence raises an interesting empirical question: whether the hedging pressure risk premium is in fact the risk premium associated with the delivery-related options. This chapter contains an empirical test of the non-redundancy between the two related but alternative sources of non-market risks. For the test, we employs a futures risk premia model in which the expected futures returns contain the market risk premium (proxied by the equity market risk premium) and two non-market risk premia (proxied by the hedging pressure effect and by the delivery risk premium reflected in the returns of futures options, respectively). Our main finding is that both the hedging pressure and the delivery risk premia are non-redundant and statistically significant for futures contracts with delivery-related options. This finding implies a substantial degree of segmentations between these futures markets and the underlying asset markets.

Volatility has become a traded commodity, and the value of extricating the implied volatility for a given underlying asset’s market value from observed option premia has long been recognized. This contribution offers a least-squared error approach based on Standardized Options that offers the potential to overcome the well-known problem of “smiles and frowns.”

The purpose of this paper is to develop an option pricing model applicable to US options. The lognormality assumption that has typically been imposed with past binomial and trinomial option pricing models is relaxed. The relaxed lattice model is then used to determine skewness and kurtosis of distributions of futures prices implied from option prices.

The relaxed lattice is based on Gaussian quadrature. The markets studied include corn, soybeans, and wheat. Skewness and kurtosis are implied by minimizing the squared deviations of actual option premia from predicted premia.

Positive skewness is the major source of nonnormality, but both skewness and kurtosis are important as the trinomial model that considers kurtosis has greater accuracy than the binomial model. The out‐of‐sample forecasting accuracy of the relaxed lattice models is better than the Black‐Scholes model in most, but not all cases.

The model might benefit from using option prices from more than one day. The implied skewness and kurtosis were quite variable and using more data might reduce this variability.

Empirical results mostly show positive implied skewness, which suggests extreme price rises were more likely than extreme price decreases.

The relaxed lattice is a new model and the results about implied higher moments are new for these commodities. There are competing models available that should be able to get similar accuracy, so one key advantage of the new approach is its simplicity and ease of use.

One of the particular problems facing agribusiness firms is the relationship between commodity price risk (a source of business risk) and debt repayment ability (a source of financial risk). This study examines the use of commodity‐linked loans applied to agricultural credits. A commodity‐linked loan is a credit instrument whose payoff is contingent on the value of an underlying commodity or portfolio of commodities. The payoff structure includes an option (call or put) rider that provides a payoff if the commodity price rises above or drops below a preset strike price. The payoff is applied directly to the loan. This study introduces the general concept, reviews the literature, and develops and applies a particular model. Simulation results illustrate the interrelationship between options payoffs, strike prices, volatility, and downside financial risk reduction.

The topic of volatility measurement and estimation is central to financial and more generally time-series econometrics. In this chapter, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In particular, in the first sections of this chapter, we discuss important developments in volatility models, with focus on time-varying and stochastic volatility as well as nonparametric volatility estimation. The models discussed share the common feature that volatilities are unobserved and belong to the class of missing variables. We then provide empirical evidence on “small” and “large” jumps from the perspective of their contribution to overall realized variation, using high-frequency price return data on 25 stocks in the DOW 30. Our “small” and “large” jump variations are constructed at three truncation levels, using extant methodology of Barndorff-Nielsen and Shephard (2006), Andersen, Bollerslev, and Diebold (2007), and Aït-Sahalia and Jacod (2009a, 2009b, 2009c). Evidence of jumps is found in around 22.8% of the days during the 1993–2000 period, much higher than the corresponding figure of 9.4% during the 2001–2008 period. Although the overall role of jumps is lessening, the role of large jumps has not decreased, and indeed, the relative role of large jumps, as a proportion of overall jumps, has actually increased in the 2000s.

The Black‐Scholes (B‐S) model in its various formulations has been the mainstay paradigm on option pricing since its basic formulation in 1973. The model has generally been proven empirically robust, despite the well documented empirical evidence of mispricing deep‐in‐the‐money, deep out‐of‐the‐money and, occasionally, at‐the‐money options with near maturities [see Galai (1983)]. Research on explaining the observed pricing anomalies has focused on the variance of the return of the underlying asset, which, in the case of the B‐S model, is assumed to remain invariant over time. The variance term is not directly observable, leading researchers to speculate that pricing discrepancies may be caused by misspecification of this variable. More specifically, interest in the volatility variable has centered about the implied standard deviation (ISD).

The purpose of this paper is to gain a better understanding of the market timing skills displayed by hedge fund managers during the 2007‐08 financial crisis.

The performance of a market timer can be measured through the 1966 Treynor and Mazuy model, provided the regression alpha is properly adjusted by using the cost of an option‐based replicating portfolio, as shown by Hübner. The paper adapts this approach to the case of multi‐factor models with positive, negative or neutral betas. This new approach is applied on a sample of hedge funds whose managers are likely to exhibit market timing skills. This concentrates on funds that post weekly returns, and analyzes three hedge funds strategies in particular: long‐short equity, managed futures, and funds of hedge funds. The paper analyzes a particular period during which the managers of these funds are likely to magnify their presumed skills, namely around the financial and banking crisis of 2008.

Some funds adopt a positive convexity as a response to the US market index, while others have a concave sensitivity to the returns of an emerging market index. Thus, the paper identifies “positive”, “mixed” and “negative” market timers. A number of signs indicate that only positive market timers manage to acquire options below their cost, and deliver economic significant performance, even in the midst of the financial crisis. Negative market timers, by contrast, behave as if they were forced to sell options without getting the associated premium. This behaviour is interpreted as a possible result of re sales, leading them to liquidate positions under the pressure of redemption orders, and inducing negative performance adjusted for market timing.

The paper suggests that the convexity in returns that is generally associated with market timing can be attributed to three sources: timing skills, exposure to nonlinear risk factors, or liquidity pressures. It manages to identify the impact of the latter two effects in the context of hedge funds.

This paper aims to enhance a co-skew-based risk measurement methodology initially introduced in Polimenis, by extending it for the joint estimation of the jump betas for two stocks.

The authors introduce the possibility of idiosyncratic jumps and analyze the robustness of the estimated sensitivities when two stocks are jointly fit to the same set of latent jump factors. When individual stock skews substantially differ from those of the market, the requirement that the individual skew is exactly matched is placing a strain on the single stock estimation system.

The authors argue that, once the authors relax this restrictive requirement in an enhanced joint framework, the system calibrates to a more robust solution in terms of uncovering the true magnitude of the latent parameters of the model, at the same time revealing information about the level of idiosyncratic skews in individual stock return distributions.

Allowing for idiosyncratic skews relaxes the demands placed on the estimation system and hence improves its explanatory power by focusing on matching systematic skew that is more informational. Furthermore, allowing for stock-specific jumps that are not related to the market is a realistic assumption. There is now evidence that idiosyncratic risks are priced as well, and this has been a major drawback and criticism in using CAPM to assess risk premia.

Since jumps in stock prices incorporate the most valuable information, then quantifying a stock's exposure to jump events can have important practical implications for financial risk management, portfolio construction and option pricing.

This approach boosts the “signal-to-noise” ratio by utilizing co-skew moments, so that the diffusive component is filtered out through higher-order cumulants. Without making any distributional assumptions, the authors are able not only to capture the asymmetric sensitivity of a stock to latent upward and downward systematic jump risks, but also to uncover the magnitude of idiosyncratic stock skewness. Since cumulants in a Levy process evolve linearly in time, this approach is horizon independent and hence can be deployed at all frequencies.