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SOFR Futures and Options is the practical guide through the maze of the transition from LIBOR. In the first section, it provides an in-depth explanation of the concepts involved: * The repo market and the construction of SOFR * SOFR-based lending markets and the term rate * The secured-unsecured basis * SOFR futures and options and their spread contracts * Margin and convexity Applying these insights, the second section offers detailed worked-through examples of hedging loans, swaps, bonds, and floors with SOFR futures and options, supported by interactive spreadsheets accessible on the web. The gold standard resource for professionals working at financial institutions, SOFR Futures and Options also belongs in the libraries of students of finance and business, as well as those preparing for the Chartered Financial Analyst exam.

Motivated by the challenges faced by the telecom industry during the past decade, in this paper we study a dynamic capacity expansion problem for service firms. There is a random demand for the firm's capacity in each period: the demand in excess of the capacity is lost, and revenue is generated for the fulfilled demand. At the beginning of each period, the firm might increase its capacity through purchasing equipment for immediate delivery, which is constrained by a random supply limit, or it might sign a future contract for equipment delivery in the following period. We assume that the firm's capacity might partially become obsolete due to natural deterioration or technology innovation. We aim at characterizing optimal capacity expansion strategies and comparing the profit functions as well as the optimal control policies of different options. Specifically, we show that the optimal capacity expansion policy for the current period is determined by a base-stock policy. Compared with the case where no future contracts are available, the optimal control parameters of capacity expansion are always smaller. We further show that when the obsolescence rate is deterministic, the optimal policy for capacity expansion through future contracts is also a base-stock type. The results are extended to the cases with stochastically dependent capacity supply limits and stochastically dependent demand processes, which establish the robustness of the optimal policy in various market conditions.

We identify two types of risk premia in commodity futures returns: spot premia related to the risk in the underlying commodity, and term premia related to changes in the basis. Sorting on forecasting variables such as the futures basis, return momentum, volatility, inflation, hedging pressure, and liquidity results in sizable spot premia between 5% and 14% per annum and term premia between 1% and 3% per annum. We show that a single factor, the high-minus-low portfolio from basis sorts, explains the cross-section of spot premia. Two additional basis factors are needed to explain the term premia.

Commodity derivatives are becoming an increasingly important part of the global derivatives market. Here we develop a tractable stochastic volatility model for pricing commodity derivatives. The model features unspanned stochastic volatility, quasi-analytical prices of options on futures contracts, and dynamics of the futures curve in terms of a low-dimensional affine state vector. We estimate the model on NYMEX crude oil derivatives using an extensive panel data set of 45,517 futures prices and 233,104 option prices, spanning 4082 business days. We find strong evidence for two predominantly unspanned volatility factors.

Research background: Market participants have been trying to forecast future price movements and create tools to facilitate making the right investment decisions since the beginning of the operation of stock exchanges. As a result, there are an increasing number of methods, tools, strategies and models to make the decision process which is becoming extremely complicated. Purpose: to maximize the simplification of trade rules and to check whether it is possible to parameterize transactions based on the length of price movements in order that the system built in this way would generate profits. Research methodology: empirical research was conducted on data from the period between 20/01/1998 and 29/06/2018 covering listing futures contracts for the WIG20. First, the length of the price movements was determined according to the closing rate, then the frequency of individual lengths of the price movements was determined so transaction parameters were fixed. Next, the parameters were optimized and the rates of return from the tested options were examined. Result: It is possible to parameterize transactions based on the length of price movements and to create a simple investment strategy which generates profits. In the audited period, the optimal length of traffic was 25 points with a simultaneous use of a profit/loss ratio of 1 : 1, 1 : 2 or 1 : 3. Novelty: an original investment strategy based on the parameterization of transactions that is based on length of price movement and profit/loss ratio.

The authors found that, concurrent with the rapidly growing index investment in commodity markets since the early 2000s, prices of non-energy commodity futures in the United States have become increasingly correlated with oil prices; this trend has been significantly more pronounced for commodities in two popular commodity indices. This finding refiects the financialization of the commodity markets and helps explain the large increase in the price volatility of non-energy commodities around 2008.

Order flow is toxic when it adversely selects market makers, who may be unaware they are providing liquidity at a loss. We present a new procedure to estimate flow toxicity based on volume imbalance and trade intensity (the VPIN toxicity metric). VPIN is updated in volume time, making it applicable to the high-frequency world, and it does not require the intermediate estimation of non-observable parameters or the application of numerical methods. It does require trades classified as buys or sells, and we develop a new bulk volume classification procedure that we argue is more useful in high-frequency markets than standard classification procedures. We show that the VPIN metric is a useful indicator of short-term, toxicity-induced volatility.

Despite their widespread use as predictors of the spot price of oil, oil futures prices tend to be less accurate in the mean-squared prediction error sense than no-change forecasts. This result is driven by the variability of the futures price about the spot price, as captured by the oil futures spread. This variability can be explained by the marginal convenience yield of oil inventories. Using a two-country, multi-period general equilibrium model of the spot and futures markets for crude oil we show that increased uncertainty about future oil supply shortfalls under plausible assumptions causes the spread to decline. Increased uncertainty also causes precautionary demand for oil to increase, resulting in an immediate increase in the real spot price. Thus the negative of the oil futures spread may be viewed as an indicator of fluctuations in the price of crude oil driven by precautionary demand. An empirical analysis of this indicator provides evidence of how shifts in the uncertainty about future oil supply shortfalls affect the real spot price of crude oil.

We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

In Colombia, coffee futures contracts represent essential financial agreements that allow producers and buyers to establish prices, quality, and conditions for future transactions in the coffee market. Despite the evident benefits of stability and predictability, this practice faces significant sustainability challenges that threaten its long-term viability. One of the reasons is the significant lack of transparency in the supply chain. Farmers, affected by abrupt price fluctuations and adverse weather conditions such as the El Niño phenomenon, experience an increase in market prices, leading to the non-delivery of the final product, and contract breaches as they find better prices in the local market. In this context, smart contracts emerge as a promising technological solution to address these problems. These contracts enable the verification of each step in the process, from harvest to final sale, within a blockchain. Therefore, this research designs a smart contract managed through a platform called SmartBeanFutures, which records the clauses of futures contracts using the IERC721 framework, allowing the generation of a unique and non-repeatable asset. It aims to sell, promote, and manage coffee sale prices during the agreement’s signing, creating a transparent environment for chain actors. This proposal undergoes evaluation in a test environment, providing farmers access to the designed platform. Following the validation of the proposal, it was identified that over 74% would use this type of contract in their agricultural processes, highlighting that implementing this technology contributes to eliminating intermediaries in the chain and gives farmers more control over their participation in the market.