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Intraday return predictability: Evidence from commodity ETFs and their related volatility indices.

ABSTRACT: Using high-frequency data of crude oil, gold, and silver exchange-traded funds (ETFs) and their related volatility indices, we analyse patterns of intraday return predictability, also called intraday momentum, in each market. We find that intraday return predictability exists in all the markets, but the patterns of predictability differ for each market, with different half-hour returns, not necessarily the first half-hour returns of the trading day, exhibiting significant predictability for their last half-hour counterparts, depending on the specific market. The intraday return predictability is stronger on days of higher volatility and larger jumps. Substantial economic value can be generated by a market timing strategy which is constructed upon the intraday momentum, in all the markets under study. Possible theoretical explanations for the intraday return predictability are infrequent portfolio rebalancing investors and late-informed investors.

SUBMITTER: Xu Y

PROVIDER: S-EPMC7480318 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The present work investigates the impact on financial intermediation of distributed ledger technology (DLT), which is usually associated with the blockchain technology and is at the base of the cryptocurrencies' development. "Bitcoin" is the expression of its main application since it was the first new currency that gained popularity some years after its release date and it is still the major cryptocurrency in the market. For this reason, the present analysis is focused on studying its price determination, which seems to be still almost unpredictable. We carry out an empirical analysis based on a cost of production model, trying to detect whether the Bitcoin price could be justified by and connected to the profits and costs associated with the mining effort. We construct a sample model, composed of the hardware devices employed in the mining process. After collecting the technical information required and computing a cost and a profit function for each period, an implied price for the Bitcoin value is derived. The interconnection between this price and the historical one is analyzed, adopting a Vector Autoregression (VAR) model. Our main results put on evidence that there aren't ultimate drivers for Bitcoin price; probably many factors should be expressed and studied at the same time, taking into account their variability and different relevance over time. It seems that the historical price fluctuated around the model (or implied) price until 2017, when the Bitcoin price significantly increased. During the last months of 2018, the prices seem to converge again, following a common path. In detail, we focus on the time window in which Bitcoin experienced its higher price volatility; the results suggest that it is disconnected from the one predicted by the model. These findings may depend on the particular features of the new cryptocurrencies, which have not been completely understood yet. In our opinion, there is not enough knowledge on cryptocurrencies to assert that Bitcoin price is (or is not) based on the profit and cost derived by the mining process, but these intrinsic characteristics must be considered, including other possible Bitcoin price drivers.

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Intraday return predictability: Evidence from commodity ETFs and their related volatility indices.

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