A key theoretical prediction in financial economics is that under risk neutrality and rational expectations a currency’s forward rates should form unbiased predictors of future spot rates. Yet scores ...of empirical studies report negative slope coefficients from regressions of spot rates on forward rates. We collect 3643 estimates from 91 research articles and using recently developed techniques investigate the effect of publication and misspecification biases on the reported results. Correcting for these biases yields slope coefficients in the intervals (0.23,0.45) and (0.95,1.16) for the currencies of developed and emerging countries respectively, which implies that empirical evidence is in line with the theoretical prediction for emerging economies and less puzzling than commonly thought for developed economies. Our results also suggest that the coefficients are systematically influenced by the choice of data, numeraire currency, and estimation method.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible and trade across multiple spot exchanges at different prices, and futures ...contracts were selectively introduced on Bitcoin (BTC) exchange rates against the U.S. dollar (USD) in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC–USD relative to other exchange rate pairs as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent arbitrage frictions associated with short-sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC–USD futures was beneficial to the Bitcoin spot market by making the underlying prices more informative.
This paper was accepted by Will Cong, Special Section of
Management Science
: Blockchains and Crypto Economics.
Funding:
The authors acknowledge financial support from the Global Risk Institute. P. Augustin acknowledges financial support from the Canadian Derivatives Institute and from the Canada Research Chair Program of the Social Sciences and Humanities Research Council Canada. The paper has benefited significantly from a fellow visit of P. Augustin at the Center for Advanced Studies Foundations of Law and Finance funded by the German Research Foundation, project FOR 2774, and from a visiting position of P. Augustin at the finance department of the University of Luxembourg facilitated through the Inter Mobility Programme of the Luxembourg National Research Fund.
Supplemental Material:
The online appendix and data are available at
https://doi.org/10.1287/mnsc.2023.4900
.
We develop a framework to characterize strategic behavior in sequential markets under imperfect competition and restricted entry in arbitrage. Our theory predicts that these two elements can generate ...a systematic price premium. We test the model predictions using microdata from the Iberian electricity market. We show that the observed price differences and firm behavior are consistent with the model. Finally, we quantify the welfare effects of arbitrage using a structural model. In the presence of market power, we show that full arbitrage is not necessarily welfare-enhancing, reducing consumer costs but increasing deadweight loss.
A popular view is that the surge in the real price of oil during 2003-08 cannot be explained by economic fundamentals, but was caused by the increased financialization of oil futures markets, which ...in turn allowed speculation to become a major determinant of the spot price of oil. This interpretation has been driving policy efforts to tighten the regulation of oil derivatives markets. This survey reviews the evidence supporting this view. We identify six strands in the literature and discuss to what extent each sheds light on the role of speculation. We find that the existing evidence is not supportive of an important role of speculation in driving the spot price of oil after 2003. Instead, there is strong evidence that the co-movement between spot and futures prices reflects common economic fundamentals rather than the financialization of oil futures markets.
In economies with a continuum of agents of different types, pecuniary externalities are removed with market exchanges. Agents choose from among various possible prices they want to prevail in the ...future and buy or sell rights in these market exchanges for future trade. Each agent can choose the exchange it wants without regard to what any other agent is doing. But crucially, the right to trade in each and every exchange is priced. The fee structure has a per-unit price and quantity decomposition: a price, as determined by the exchange chosen, times the quantity of rights acquired.
Using oil futures, we examine expectation formation and how it alters the macroeconomic transmission of shocks. Our empirical framework, where investors learn about the persistence of oil-price ...movements, successfully replicates the fluctuations in oil-price futures since the Late 1990s. By embedding this learning mechanism in an estimated model, we document that an increase in the persistence of TFP-driven fluctuations in oil demand largely accounts for investors' perceptions that oil-price movements became increasingly permanent during the 2000s. Learning alters the macroeconomic impact of shocks, making the responses time dependent and conditional on perceptions of shocks' likely persistence.
In this paper we analyze the short-term spot price behavior of carbon dioxide (CO2) emission allowances of the new EU-wide CO2 emissions trading system (EU ETS). After reviewing the stylized facts of ...this new class of assets we investigate several approaches for modeling the returns of emission allowances. Due to different phases of price and volatility behavior in the returns, we suggest the use of Markov switching and AR-GARCH models for stochastic modeling. We examine the approaches by conducting an in-sample and out-of-sample forecasting analysis and by comparing the results to alternative approaches. Our findings strongly support the adequacy of the models capturing characteristics like skewness, excess kurtosis and in particular different phases of volatility behavior in the returns.
Enterprise Risk Management (ERM) has become one of the most essential subjects in business management. This paper establishes how risk modeling can be applied to supply chain management, specifically ...to supply portfolio procurement decisions of a firm. In a single period setting, parts can be procured via traditional forward contracts, option contracts or spot purchases. Customer demand and spot prices are random and possibly correlated and firm׳s primary suppliers are subject to complete disruptions and yield uncertainties. This paper analyzes several scenarios where the spot market is not available, available for buying only, and available for both buying and selling. This article develops and solves mathematical models considering the risk neutral and risk averse (CVaR) objectives independently or simultaneously. For the special case of normally distributed random variables and a risk neutral objective, optimality properties were developed. A broad numerical study examines the sensitivity of procurement strategies to key problem parameters such as, risk attitude, demand and spot price volatilities, correlation between demand and spot prices and terms of option contracts.
•We developed optimization models for procurement portfolio of a firm.•Risk neutral and risk averse objectives (CVaR) are considered.•Scenario-based LP models can be easily solved using commercial software.•A sample average approximation method (SAA) is proposed to approximate solutions.•Optimality properties are provided for the special case.
This review presents the set of electricity price models proposed in the literature since the opening of power markets. We focus on price models applied to financial pricing and risk management. We ...classify these models according to their ability to represent the random behavior of prices and some of their characteristics. In particular, this classification helps users to choose among the most suitable models for their risk management problems.
•Review on electricity price models.•Bibliographic survey on electricity price models for risk management purposes.•Unification of notation to ease comparison of all existing models
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