Bitcoin Futures—What use are they? Corbet, Shaen; Lucey, Brian; Peat, Maurice ...
Economics letters,
11/2018, Letnik:
172
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Early analysis of Bitcoin concluded that it did not meet the economic conditions to be classified as a currency. Since this conclusion, interest in Bitcoin has increased substantially. We investigate ...whether the introduction of futures trading in Bitcoin is able to resolve the issues that stopped Bitcoin from being considered a currency. Our analysis shows that spot volatility has increased following the appearance of futures contracts, that futures contracts are not an effective hedging instrument, and that price discovery is driven by uninformed investors in the spot market. We therefore argue that the conclusion that Bitcoin is a speculative asset rather than a currency is not altered by the introduction of futures trading.
•This article investigates the effect of the introduction of Bitcoin futures.•The introduction of Bitcoin futures has increased the spot volatility of Bitcoin.•Bitcoin futures are not an effective hedging tool.•Price discovery is driven by uninformed investors in the spot market.•Bitcoin futures did not affect the nature of Bitcoin as a speculative asset rather than a currency.
Price clustering in Bitcoin Urquhart, Andrew
Economics letters,
October 2017, 2017-10-00, 20171001, Letnik:
159
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Investor and media attention in Bitcoin has increased substantially in recently years, reflected by the incredible surge in news articles and considerable rise in the price of Bitcoin. Given the ...increased attention, there little is known about the behaviour of Bitcoin prices and therefore we add to the literature by studying price clustering. We find significant evidence of clustering at round numbers, with over 10% of prices ending with 00 decimals compared to other variations but there is no significant pattern of returns after the round number. We also support the negotiation hypothesis of Harris (1991) by showing that price and volume have a significant positive relationship with price clustering at whole numbers.
•We study the price clustering in Bitcoin.•We find significant evidence of price clustering at round numbers.•However there is no significant pattern in returns after round numbers.•We also show that price and volume have a significant positive relationship with price clustering.•Our analysis supports the negotiation hypothesis of Harris (1991).
Carry Koijen, Ralph S.J.; Moskowitz, Tobias J.; Pedersen, Lasse Heje ...
Journal of financial economics,
February 2018, 2018-02-00, 20180201, Letnik:
127, Številka:
2
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We apply the concept of carry, which has been studied almost exclusively in currency markets, to any asset. A security’s expected return is decomposed into its “carry,” an ex-ante and model-free ...characteristic, and its expected price appreciation. Carry predicts returns cross-sectionally and in time series for a host of different asset classes, including global equities, global bonds, commodities, US Treasuries, credit, and options. Carry is not explained by known predictors of returns from these asset classes, and it captures many of these predictors, providing a unifying framework for return predictability. We reject a generalized version of Uncovered Interest Parity and the Expectations Hypothesis in favor of models with varying risk premia, in which carry strategies are commonly exposed to global recession, liquidity, and volatility risks, though none fully explains carry’s premium.
Demand-Based Option Pricing Gârleanu, Nicolae; Pedersen, Lasse Heje; Poteshman, Allen M.
The Review of financial studies,
10/2009, Letnik:
22, Številka:
10
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We model demand-pressure effects on option prices. The model shows that demand pressure in one option contract increases its price by an amount proportional to the variance of the unhedgeable part of ...the option. Similarly, the demand pressure increases the price of any other option by an amount proportional to the covariance of the unhedgeable parts of the two options. Empirically, we identify aggregate positions of dealers and end-users using a unique dataset, and show that demand-pressure effects make a contribution to wellknown option-pricing puzzles. Indeed, time-series tests show that demand helps explain the overall expensiveness and skew patterns of index options, and cross-sectional tests show that demand impacts the expensiveness of single-stock options as well.
•Techno-economic analysis for large-scale hydrogen production plants via water electrolysis.•Demonstrated hydrogen production at flat rate pricing and a real-time pricing scheme.•An operational ...strategy was proposed for a real-time pricing scheme.•Investigated the influence of capacity factor and storage medium on the levelized cost of hydrogen.•Large-scale hydrogen production is possible at similar cost as SMR.
A techno-economic analysis was performed for large-scale hydrogen production plants (4000–40,000 kgH2/day or approximately 10–100 MW). Two electricity pricing schemes in 8 different geographical locations were considered. The analysis included five Canadian provinces with flat rates and real-time pricing for the wholesale markets in Germany, California, and Ontario. Under flat-rate pricing, the levelized cost of hydrogen produced via water electrolysis ranged from, for example, $4.21 to $4.71/kgH2 in Québec. For wholesale electricity markets, an operational strategy was developed that aims to identify if a posted price is high or low based on historical electricity spot prices. The electricity cost can be reduced by 4%–9% in Germany and by 15%–31% in Ontario and California at a capacity factor of 0.9 by implementing such operational strategy. Electrolytic hydrogen production in Ontario combined with underground storage was found to be the cheapest in the three wholesale electricity markets, resulting in a levelized cost of hydrogen of $2.93–$3.22/kgH2 for alkaline electrolysis and $2.66–$3.54/kgH2 for proton exchange membrane electrolysis. Compared to steam methane reforming at $2.5–$2.8/kgH2 (without carbon capture), the electrolytic hydrogen cost is 6%–27% higher. However, this cost becomes comparable to that from steam methane reforming once carbon capture and storage are included in the analysis. Our results suggest that maximizing the use of the electrolytic systems via high capacity factors can be favorable, especially under integration with wholesale electricity markets.
We study a firm’s optimal pricing policy under commitment. The firm’s objective is to maximize its long-term average revenue given a steady arrival of strategic customers. In particular, customers ...arrive over time, are strategic in timing their purchases, and are heterogeneous along two dimensions: their valuation for the firm’s product and their willingness to wait before purchasing or leaving. The customers' patience and valuation may be correlated in an arbitrary fashion. For this general formulation, we prove that the firm may restrict attention to cyclic pricing policies, which have length, at most, twice the maximum willingness to wait of the customer population. To efficiently compute optimal policies, we develop a dynamic programming approach that uses a novel state space that is general, capable of handling arbitrary problem primitives, and that generalizes to finite horizon problems with nonstationary parameters. We analyze the class of monotone pricing policies and establish their suboptimality in general. Optimal policies are, in a typical scenario, characterized by nested sales, where the firm offers partial discounts throughout each cycle, offers a significant discount halfway through the cycle, and holds its largest discount at the end of the cycle. We further establish a form of equivalence between the problem of pricing for a stream of heterogeneous strategic customers and pricing for a pool of heterogeneous customers who may stockpile units of the product.
This paper was accepted by Yossi Aviv, operations management.
Many products undergo cost reductions over their product life cycles. However, strategic customers may have more incentive to wait if they expect a cost reduction to lead to a price drop. A firm that ...does not face any uncertainty can use pricing strategies such as price commitment and price matching to alleviate the strategic waiting of customers. However, these pricing strategies provide less flexibility than dynamic pricing for a firm facing uncertainty. In this paper, we examine the impact of cost reduction under dynamic pricing, price commitment, and price matching when cost reduction can come from production learning or from technology advancement. The firm makes pricing decisions when facing uncertainty in future cost, and strategic customers decide whether to wait when facing uncertainty in future price. We show that in general the firm’s profit is higher when future cost is more uncertain, but not necessarily when cost reduction is more significant. In addition, production learning and technology advancement can have opposite effects on the optimal pricing decisions and the choice of pricing strategy.
This paper was accepted by Yossi Aviv, operations management
.
It has been recently shown that rough volatility models, where the volatility is driven by a fractional Brownian motion with small Hurst parameter, provide very relevant dynamics in order to ...reproduce the behavior of both historical and implied volatilities. However, due to the non‐Markovian nature of the fractional Brownian motion, they raise new issues when it comes to derivatives pricing. Using an original link between nearly unstable Hawkes processes and fractional volatility models, we compute the characteristic function of the log‐price in rough Heston models. In the classical Heston model, the characteristic function is expressed in terms of the solution of a Riccati equation. Here, we show that rough Heston models exhibit quite a similar structure, the Riccati equation being replaced by a fractional Riccati equation.
Hedging Climate Change News Engle, Robert F.; Giglio, Stefano; Kelly, Bryan ...
The Review of financial studies,
03/2020, Letnik:
33, Številka:
3
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We propose and implement a procedure to dynamically hedge climate change risk. We extract innovations from climate news series that we construct through textual analysis of newspapers. We then use a ...mimicking portfolio approach to build climate change hedge portfolios. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industrybalanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. We discuss multiple directions for future research on financial approaches to managing climate risk.
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