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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.

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.

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.

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.

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.

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.

Commodities are back following a stellar run of price performance, attracting financial investor attention. What are the fundamental reasons to hold commodities? One reason is the exposure offered to underlying risk factors. In this paper, I assess the macro risk exposure offered by commodity futures and test whether these risks are priced, using Merton's (1973) intertemporal capital asset pricing model for a sample of commodity prices covering the period January 1973 - February 2008. I find that commodity futures offer a hedge against lower interest rates and that investors are willing to accept lower expected returns for this position. Although some commodities are also a hedge against U.S. dollar depreciation, this risk is not priced.

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.

For this study of the simple properties of commodity futures as an asset class, an equally weighted index of monthly returns of commodity futures was constructed for the July 1959 through December 2004 period. Fully collateralized commodity futures historically have offered the same return and Sharpe ratio as U.S. equities. Although the risk premium on commodity futures is essentially the same as that on equities for the study period, commodity futures returns are negatively correlated with equity returns and bond returns. The negative correlation is the result, primarily, of commodity futures' different behavior over a business cycle. Commodity futures are positively correlated with inflation, unexpected inflation, and changes in expected inflation. Imagine an asset class that has returns that (1) are the same as those on the U.S. stock market but (2) are less volatile than stock returns, (3) are negatively correlated with the returns on stocks and bonds, and (4) are positively correlated with inflation. The asset class is an investment in commodity futures. Despite being an old asset class, commodity futures are not widely appreciated. Futures contracts are agreements to buy or sell a commodity at a future date at a price that is agreed upon today. Except for collateral requirements, futures contracts do not require a cash outlay for either buyers or sellers. The buyer of a futures contract is, on average, compensated by the seller of futures if the futures price is set below the expected spot price at the time of the expiration of the futures contract. The opposite is true when the futures price is set above the expected future spot price. In 1930, John Maynard Keynes postulated that sellers of futures (hedgers) would, on average, compensate the buyers of futures (speculators)-a situation he referred to as \"normal backwardation.\" By examining the returns to futures over long periods, we indirectly tested this Keynesian prediction. We constructed a dataset of returns on individual commodity futures going back as far as 1959. The dataset combines information about individual commodity futures prices obtained from the Commodity Research Bureau (covering, among other exchanges, the Chicago Board of Trade and Chicago Mercantile Exchange) and the London Metal Exchange. We computed investment returns by rolling positions in individual futures contracts forward over time. Commodity futures were combined into an equally weighted index, and much of the article is concerned with the behavior of this index. We show that over a 45-year period, a diversified investment in collateralized commodity futures earned historical returns that are comparable to U.S. stock returns. The economic rationale for these returns is the reward that investors in commodity futures receive for providing price insurance to commodity producers. The reward for providing price protection (rather than foreseeable trends in commodity prices) is the key to the returns that a futures investor can expect. Individual commodity futures can be very volatile, but much of this volatility can be avoided by investing in a diversified index of commodity futures. The average historical returns to the equally weighted index of commodity futures has exceeded the return on U.S. T-bills by about 5 percent a year. This excess return is about the same as the historical risk premium on the S&P 500 Index over the 1959-2004 period, but the commodity futures index had a slightly lower standard deviation than the S&P 500. The relatively low volatility of the commodity futures index stems from the fact that the pairwise correlations between individual commodity futures are relatively low. Commodity futures are less risky by other standards. First, the distribution of commodity futures returns is skewed to the right, whereas equity return distributions are skewed to the left. In other words, relative to a normal bell-shaped curve, equities experience proportionally more crashes whereas the \"crashes\" in commodities most often occur on the upside, leading to positive returns to investors in commodity futures. Second, commodity futures have the ability to diversify portfolios of stocks and bonds. The sources of the diversification benefits are the ability of commodity futures to provide a (partial) hedge against inflation-stocks and bonds are poor hedges by comparison-and to partially offset the cyclical variation in the returns of stocks and bonds. Finally, when we compared an investment in our index with a portfolio of stocks of commodity-producing companies, we found that these portfolios are not close substitutes: The stocks of commodity producers are more correlated with the broad stock market than with an index of commodity futures.