Arbitrage Trading Research Articles

Techno-economic analysis of battery storage systems and hydrogen-based storage systems as an alternative to grid expansion in the medium voltage grid in Germany

<abstract> <p>The decentralization of the energy system in Germany is leading to enormous investments in grid expansion, as the current regulation creates an obligation to expand the power grid to eliminate bottlenecks. Meanwhile, opportunities to leverage grid-friendly control of storage systems are neglected to alleviate the need for investment. For this reason, it is necessary to investigate intelligent alternatives to grid expansion, such as storage systems, to efficiently integrate distributed technologies into the power system and reduce the need for grid expansion. In this work, two representative configurations of a medium voltage grid in Germany are developed for the years 2022 and 2050, and different storage systems are compared economically with the grid expansion in a model-based simulation. Hydrogen storage and battery storage were chosen as storage systems. The results show that grid expansion is the least expensive option if only the grid expansion costs are included in the analysis. However, if additional uses for the storage systems are considered, the battery storage systems are more economical. While in the scenario for 2050 the grid expansion causes costs of approx. 56,000 EUR per year, revenues of at least 58,000 EUR per year can be achieved via the revenue opportunities of the battery storage, representing a 3.5% margin. Heat extraction, arbitrage trading, and avoidance of grid expansion in superimposed grid levels were integrated as additional revenue streams/sources. A robust data basis and cost degressions were assumed for the simulations to generate meaningful results. Overall, hydrogen storage systems are economically inferior to battery storage systems and grid expansion for this use case. The results demonstrate the complexity of analyzing the trade-offs in terms of storage as an alternative to grid expansion as well as the opportunities presented using battery storage instead.</p> </abstract>

On the potential of arbitrage trading on the German intraday power market

On the potential of arbitrage trading on the German intraday power market

Information Processing in an Asset Market Experiment with Algorithmic Arbitrage Trading

Information Processing in an Asset Market Experiment with Algorithmic Arbitrage Trading

What keeps stablecoins stable?

Using trades between the stablecoin treasury and private investors, we quantify how improved arbitrage design stabilizes the price of the dominant stablecoin, Tether. We identify two 2019 design reforms: migration of Tether from the Omni to the Ethereum blockchain and decentralization of issuance. These reforms increased investor access to arbitrage trading with the treasury, reducing the absolute size of peg deviations by half. Further evidence for the importance of arbitrage design is present in the stability mechanism of the stablecoin DAI and in the creation of authorized merchants for the pegged coin WBTC.

Estimation of Internal Rate of Return for Battery Storage Systems with Parallel Revenue Streams: Cycle-Cost vs. Multi-Objective Optimisation Approach

This paper assesses the profitability of battery storage systems (BSS) by focusing on the internal rate of return (IRR) as a profitability measure which offers advantages over other frequently used measures, most notably the net present value (NPV). Furthermore, this study proposes a multi-objective optimisation (MOO) approach to IRR estimation instead of relying on the simple linear optimisation and compares the results to the popular linear optimisation with battery cycle-cost penalty. The analysis is conducted under perfect foresight conditions by considering multiple revenue streams: arbitrage trading in the day-ahead and intraday markets, peak shaving, participating in the primary reserves market, and from photovoltaic (PV) power-generation unit. Data are collected for the German power market for 2017 and 2021. The results show that MOO approach yields similar IRR estimates to the cycle-cost model in 2017. However, higher market volatility and increased electricity prices in 2021 resulted in tangible differences. The analysis shows that, if such conditions are coupled with a low battery capacity price, the MOO method significantly outperforms the cycle-cost model. The effects of battery calendar lifetime and state of charge which decrease profitability are also considered. Nevertheless, a noticeable rise in profitability in 2021 relative to 2017 could provide enough compensation to address the issue of relatively poor viability track record.

Quantum computational quantitative trading: high-frequency statistical arbitrage algorithm

Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading by utilizing variable time condition number estimation and quantum linear regression. The algorithm complexity has been reduced from the classical benchmark O(N 2 d) to , where N is the length of trading data, and d is the number of stocks, κ 0 is the condition number and ϵ is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.

Exchange-Traded Funds Ownership and Stock Volatility

This study investigates whether exchange-traded funds (ETFs) increase the nonfundamental volatility of stocks in their baskets via the arbitrage channel. Since the price and the net asset value of the ETF underlying stocks are tied by arbitrage, liquidity shocks to the ETFs may propagate to the underlying stocks through arbitrage trading, which is called the liquidity trading hypothesis. We test the implications of this hypothesis using a sample of ETFs that track the KOSPI200 index. We address the potential endogenous relationship between ETF ownership and stock volatility using the KOSPI200 index reconstitution event as an instrumental variable in the two-stage least squares regression framework; we find the effect of ETF ownership on stock volatility is positive and significant. We also provide empirical evidence that foreign and domestic institutional investors exhibit more frequent trading in the opposite direction and higher-order imbalances for stocks with higher ETF ownership. In summary, our findings suggest that the growth of the ETFs in the Korean stock market may have had an unintended consequence of the higher volatility of ETFunderlying stocks.

COVID-19 Effects on Arbitrage Trading in the Energy Market

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on the performance of arbitrage trading in the energy market using daily data covering the period between 1 January 2015 and 5 December 2021. The investigation was achieved by utilizing a parametric pairs-trading model, where pairs of energy-related securities, including futures, stocks and ETFs traded in the United States, are formed. The empirical results suggest that the out-of-sample performances of pair trading declined sharply in the face of COVID-19. Dividing the whole data sample into two sub-samples, we found that the strategy performed well before COVID-19 but yielded poor results in the pandemic era. The analysis presented in this paper could serve as a benchmark for arbitrage-based trading models in the energy market during the pandemic.

The influence of frequency containment reserve on the efficiency of a hybrid stationary large-scale storage system

Large-scale battery storage systems are predestined for balancing the fluctuating feed-in from renewable energies and supporting the electricity grid due to their high efficiency. As a result, large-scale storage has gained importance in the market for frequency support reserve (FCR). While the first LSS projects had to be planned without operational experience a couple of years ago, new LSS can benefit from the evaluation of measured field data. Especially the real-word efficiencies are key information for electricity procuring costs and arbitrage trading. To contribute filling this information gap, this paper investigates the efficiency of the hybrid LSS, its transformers, its inverters, the individual battery technologies (two types of lead-acid and three types of lithium-ion batteries) and the influence of FCR provision on efficiency. High-resolution field measurements of the hybrid 6 MW/7.5 MWh battery storage system “M5BAT” at the FCR market over several years serve as the basis. The data-based efficiency analysis reveals a high round-trip efficiency of 72.8% (with self-consumption: 66.2%) for the LSS in operation. In this context, the lithium-ion batteries have a higher round-trip efficiency of 97.4% than the lead-acid batteries with 85%. Low load on the transformers and the inverters leads to an average power-weighted power efficiency of less than 95%. The results presented can be used to model LSS and its various system components and battery technologies in order to further analyse the participation in chosen energy markets.

Statistical arbitrage powered by Explainable Artificial Intelligence

Machine learning techniques have recently become the norm for detecting patterns in financial markets. However, relying solely on machine learning algorithms for decision-making can have negative consequences, especially in a critical domain such as the financial one. On the other hand, it is well-known that transforming data into actionable insights can pose a challenge even for seasoned practitioners, particularly in the financial world. Given these compelling reasons, this work proposes a machine learning approach powered by eXplainable Artificial Intelligence techniques integrated into a statistical arbitrage trading pipeline. Specifically, we propose three methods to discard irrelevant features for the prediction task. We evaluate the approaches on historical data of component stocks of the S&P500 index and aim at improving not only the prediction performance at the stock level but also overall at the stock set level. Our analysis shows that our trading strategies that include such feature selection methods improve the portfolio performances by providing predictive signals whose information content suffices and is less noisy than the one embedded in the whole feature set. By performing an in-depth risk-return analysis, we show that the proposed trading strategies powered by explainable AI outperform highly competitive trading strategies considered as baselines.

Covered Interest Parity Arbitrage

Abstract To understand deviations from covered interest parity (CIP), it is crucial to account for heterogeneity in funding costs across both banks and currency areas. For most market participants, the no-arbitrage relation holds fairly well when implemented using marginal funding costs and risk-free investment instruments. However, a few high-rated banks do enjoy CIP-arbitrage opportunities. Dealers avert inventory imbalances stemming from lower-rated banks’ usage of FX swaps to obtain dollar funding by inducing opposite (arbitrage) flows from high-rated banks. Arbitrage trades are difficult to scale, however, because funding costs increase as soon as arbitrageurs increase positions. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Statistical arbitrage trading on the intraday market using the asynchronous advantage actor–critic method

In this paper, we focus on statistical arbitrage trading opportunities involving the continuous exploitation of price differences arising during an intraday trading period with the option of closing positions on the balancing market. We aim to maximise the reward–risk ratio of an autonomous trading strategy. To find an optimal trading policy, we propose utilising the asynchronous advantage actor–critic (A3C) algorithm, a deep reinforcement learning method, with function approximators of two-headed shared deep neural networks. We enforce a risk-constrained trading strategy by limiting the maximum allowed position, and conduct state engineering and selection processes. We introduce a novel reward function and goal-based exploration, i.e. behaviour cloning. Our methodology is evaluated on a case study using the limit order book of the European single intraday coupled market (SIDC) available for the Dutch market area. The majority of hourly products on the test set return a profit. We expect our study to benefit electricity traders, renewable electricity producers and researchers who seek to implement state-of-art intelligent trading strategies.

Evidence of arbitrage trading activity: The case of Chinese metal futures contracts

Several unique insights are documented based on a study of selected metal futures contracts traded in the U.S. and China. Based on our unique measures, we present intra market evidence that the U.S. gold and silver futures markets reflect a fully arbitraged market and U.S. copper nearly so. In contrast, the Chinese gold and silver futures markets appear to reflect an un-arbitraged market whereas the Chinese copper market is characterized as somewhat arbitraged. We explore various reasons for this evidence as well as document the differences in gold, silver, and copper futures markets between China and the U.S.

Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming

The rising share of intermittent renewable energy production in energy systems increasingly poses a threat to system stability and the price level in energy markets. However, the effects of renewable energy production onto electricity markets also give rise to new business opportunities. The expected increase in price differences increases the market potential for storage applications and combinations with renewable energy production. The value of storage depends critically on the operation of the storage system.In this study, we evaluate large-scale photovoltaic (PV) storage systems under uncertainty, as renewable energy production and electricity prices are fundamentally uncertain. In comparison to households who largely consume the stored energy themselves, the major business case for large-scale PV and storage systems is arbitrage trading on the electricity markets. The operation problem is formulated as a Markov decision process (MDP). Uncertainties of renewable energy production are integrated into an electricity price model using ARIMA-type approaches and regime switching. Due to non-stationarity and heteroskedasticity of the underlying processes, an appropriate stochastic modeling procedure is developed. The MDP is solved using stochastic dynamic programming (SDP) and recombining trees (RT) to reduce complexity taking into account the different time scales in which decisions have to be taken. We evaluate the solution of the SDP problem against Monte Carlo simulations with perfect foresight and against a storage dispatch heuristic. The program is applied to the German electricity and reserve power market to show the potential increase in storage value with higher price spreads, and evaluate a possible imposition of the feed-in levy onto energy directly stored from the common grid.

Techno-financial investigation of second-life of Electric Vehicle batteries for energy imbalance services in the Irish electricity market

As EVs becoming a viable choice in transportation systems, worries arise about the massive amount of environmentally hazardous electrochemical waste generated at end of life. A sustainable life cycle approach suggests exploring well-defined waste management strategies to deal with the upcoming waste. These strategies either aim at recycling if there is no potential for a second life or repurposing if the end-of-life automobile batteries are sufficiently healthy. However, the health of end-of-life batteries are not the only measure for their feasibility in a second application and it partly depends on the financial viability as well. Second life market alignment, the sensible second-use profit-making considering revenue-cost balance as well as other suppliers’ side inertias and preferences for battery secondary supply instead of reuse must be enlightened in a circular waste management system. This work aims to address some of the financial considerations of battery reuse by conducting a marginal revenue-cost analysis for energy services in the Irish electricity market while incorporating the battery technical limitations into the assessment. The marginal analysis shows how much investment in reused batteries would keep the financial balance of the system. Results show that the investment of retired batteries with 90% SOH for arbitrage trading in the imbalance energy market would be profitable when their purchase price is below average 60% of the batteries’ original price, while for 85% and 80%, this value is estimated up to 25% and 20%, respectively. However, hybridizing their operation with other non-energy services would make their investment more profitable.

PROBLEMS OF THE USE OF COINTEGRATION PAIRS FOR PAIRS TRADING

This paper describes a pairs trading strategy using cointegration approach. If cointegrated pairs are thought of as such pairs, whose linear combination is a stationary process, that is, a process with stable statistical properties, then any deviation from these characteristics will be transient. If you know that such a deviation has happened, that is, a departure from the long-term equilibrium, you can forecast the direction of stock price movements and execute lucrative trades accordingly. When the difference between stock prices exceeds the prediction, we must sell the overpriced asset and acquire the undervalued one, then close the deals when the price ratio returns to long-term equilibrium. This is one form of statistical arbitrage trading strategy. During the research, it was discovered that cointegration is dependent on a variety of factors, including the time period under consideration, and that this is not the only issue. When more recent data is given more weight, it is suggested that methods for determining cointegration be developed. The importance of setting the conditions for entering and terminating a transaction, as well as the possibility of “disappearing” cointegration are also noted as issues with employing cointegrated pairings for pair trading.

ETF Arbitrage and Daily Cash Flow

This article examines ETF creations and redemptions around price deviations and finds that the expected arbitrage trades are relatively rare in a broad sample of equity index ETFs. In the absence of these trades, price deviations persist much longer. Creation and redemption activity appears to be constrained when exchange conditions would lead to a costlier arbitrage trade, and the size of the price deviations mainly impact the likelihood rather than the amount of trading. The authors also find some evidence that creations and redemptions are less likely to trade on price deviations when they would be required to trade the underlying stocks against broad market movements. Their results suggest that several factors may discourage the built-in ETF arbitrage mechanism and that investors may receive poorer trade execution in these conditions as a result.

Optimal pool composition of commercial electric vehicles in V2G fleet operation of various electricity markets

The market ramp-up of electromobility is shifting vehicle-to-grid (V2G) issues into the focus of research and industry. Electric vehicles (EVs) have the potential to support the trend towards renewable energies in their role as storage units during idle times. To participate in balancing power and energy markets, EVs are pooled via aggregators. Instead of a random composition, aggregators can smartly compose their pools and add only those vehicles that actually contribute to the pool’s performance, gaining advantages over competitors. The optimization methods presented in this paper form optimized pool combinations based on the power and energy capability profiles of commercial EVs. Genetic algorithms are used to determine the revenues of the possible pools per participating EV. The use cases analyzed are the provision of balancing power on the frequency containment reserve (FCR) market of Central Europe and energy arbitrage trading on the European power exchange intraday continuous and day-ahead auction spot markets. The results show that through smart pool composition, an aggregator can increase revenue per vehicle by up to seven-fold across the markets compared to randomly assembled pools. In the Central European market, for example, the potential V2G revenues on the FCR market (380 €) exceeded those of arbitrage trading (28 € − 203 €) in 2020. In a simulation, we show the increased degradation of the vehicle battery in V2G operation compared to sole use for mobility with a smart charging strategy. However, the additional revenue can make V2G financially worthwhile, depending on costs for measuring equipment, bidirectional charging stations, and aggregator costs.

The cost of overconfidence in public information

We investigate the effects of investor overconfidence in public information on cross-sectional asset returns. The results show that investors in the US equity market are overconfident about public signals for mature firms that are relatively easy to price—old, large, and dividend-paying firms, value firms, and firms with a higher proportion of tangible assets, little external financing, and low sales growth. However, the effects of the overconfidence on cross-sectional stock returns are reversed quickly and comprise more than half of the short-term return reversals. The risk-adjusted cost of being overconfident about the noisy public signals, measured by return reversals of hedge portfolios formed on unexpected responses, is over 1.1% per month in the first month after portfolio formation, and is still significant despite the active arbitrage trading in the 2000s.

Revenue opportunities by integrating combined vehicle-to-home and vehicle-to-grid applications in smart homes

A smart integration of electric vehicles (EVs) in the future energy system will be crucial in decarbonizing the energy sector. Bidirectional EVs can provide flexibility for the system and generate revenues for the user through multiple use cases. We model both exclusive photovoltaic (PV) self-consumption optimization and the combined usage of PV self-consumption optimization and arbitrage trading for a household with an unmanaged, smart, and bidirectional charging EV in a linear (LP) and mixed-integer linear programming (MILP). Since power flows in a typical household are low, varying non-linear charging and discharging efficiencies of the bidirectional EV in the MILP result in more realistic revenues that are 30% lower than in the LP with fixed efficiencies. For a typical German household using a bidirectional EV for optimizing PV self-consumption, these revenues are about 310 €/a, mostly generated during the summer. Arbitrage trading well complements this vehicle-to-home use case in the winter months, resulting in revenues up to 530 €/a. These significant revenue potentials can lead to more profitable and interactive EVs incentivizing users to change from internal combustion vehicles to electric mobility.