Spread, Volatility, and Volume Relationship in Financial Markets and Market Maker's Profit Optimization

Sarkissian, Jack

doi:10.2139/ssrn.2799798

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…As a result of limited liquidity, the bid-ask spread have in many cases historically been high. This is a general trend that can be observed for markets with low liquidity [16]. The result is that there often is a large gap between the prices that someone is willing to buy for (BID), and the prices the seller is demanding (ASK).…”

Section: Market Liquidity and Large Spreadsmentioning

confidence: 80%

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Internal hydro- and wind portfolio optimisation in real-time market operations

Riddervold¹,

Aasgård²,

Haukaas³

et al. 2021

Preprint

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In this paper aspects related to handling of intraday imbalances for hydro and wind power are addressed. The definition of imbalance cost is established and used to describe the potential benefits of shifting from plant-specific schedules to a common load requirement for wind and hydropower units in the same price area. The Nordpool intraday pay-as-bid market has been the basis for evaluation of imbalances, and some main characteristics for this market has been described. We consider how internal handling of complementary imbalances within the same river system with high inflow uncertainty and constrained reservoirs can reduce volatility in short-term marginal cost and risk compared to trading in the intraday market. We have also shown the the imbalance cost for a power producer with both wind and hydropower assets can be reduced by internal balancing in combination with sales and purchase in a pay-as-bid intraday market.

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Section: Market Liquidity and Large Spreadsmentioning

confidence: 80%

“…4. With this production as basis for providing bids to the intraday market, the power producer would be willing to increase production by 16 MWh in hour 1 at a price of 24.2 EUR/MWh. The producer would also be willing to reduce production in this segment.…”

Section: Dynamics For Marginal Cost (Mc) In Hydro-based Power Systemsmentioning

confidence: 99%

Internal hydro- and wind portfolio optimisation in real-time market operations

Riddervold¹,

Aasgård²,

Haukaas³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…This uncertainty is an intrinsic property of price and cannot be fully eliminated. It can be diminished by adding more liquidity to the market, but it cannot be fully taken away [9,10].…”

Section: δ = −mentioning

confidence: 99%

“…The end result of the measurement depends on the SPECIFICS of how that measurement is performed! Following these considerations, quantum theory of price formation was proposed and developed by the author in [9][10][11][12][13][14][15][16][17], treating price formation as a quantum-chaotic process rather than classical-chaotic process. In this approach securities are governed by the stochastic price operator, whose spectrum defines the prices that the security can attain when it is transacted.…”

Section: δ = −mentioning

confidence: 99%

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Quantum coupled-wave theory of price formation in financial markets: Price measurement, dynamics and ergodicity

Sarkissian¹

2020

Physica A: Statistical Mechanics and its Applications

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We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders.In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to "bid" and "ask" eigenstates, while randomness of price operator results in price fluctuations that destroy oscillatory effects. We show that this theory adequately captures behavior of bid-ask spread and allows to model bid and ask price dynamics in a coordinated way. We also discuss ergodicity properties of price formation and show how directional price movement occurs due to ergodicity violation in a quantum process instead of the commonly believed forces acting on price. This theory has wide range of applications such as trade execution modeling, large order pricing and risk valuation for illiquid securities. Stochastic calculus vs. financial marketsModern quantitative finance is built around stochastic calculus treating price movements as random walk [1][2][3]. In doing so it implicitly relies on certain assumptions, that are supposed to ensure similarity between the two processes. Over the decades, countless models have been produced based on this similarity. However, in practice these models are almost always used without verifying the validity of the underlying assumptions. Such careless application of models can easily lead to lack of control, position mismanagement and financial losses [4][5][6]. Let us therefore examine, what criteria ensure similarity between price movements and random walk.

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