Option Pricing Under Quantum Theory of Securities Price Formation

Sarkissian, Jack

doi:10.2139/ssrn.2848014

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…There are also causal effects on the spread of the amount of trading. A small trading volume reduces the size of the spread, which in turn adds liquidity to the market and improves price accuracy (Sarkissian 2016). According to Le and Gregoriou (2020), there is a strong relationship between higher trading volume and higher spread due to asymmetric information effects.…”

Section: Literature Reviewmentioning

confidence: 99%

Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Guijarro

Clemente

Saleemi

2021

JRFM

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Market liquidity has an immediate impact on the execution of transactions in financial markets. Informed counterparty risk is often priced into market liquidity. This study investigates whether microblogging data, as a non-financial information tool, is priced along with market liquidity dimensions. The analysis is based on the Australian Securities Exchange (ASX), and from the results, we conclude that microblogging content in pessimistic periods has a higher impact on liquidity and its dimensions. On a daily basis, pessimistic investor sentiments lead to higher trading costs, illiquidity, a larger price dispersion and a lower trading volume.

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Section: Literature Reviewmentioning

confidence: 99%

Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Guijarro

Clemente

Saleemi

2021

JRFM

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show abstract

“…Meanwhile, there is also a causal effect of trading volume on spread. Small trading volume adds more liquidity to stocks and hence helps improve price accuracy and reduces spread (Sarkissian, 2016). Easley and O'Hara (1992) also suggest that volume has a positive impact on the size of spread.…”

Section: Volume-based Measuresmentioning

confidence: 99%

How Do You Capture Liquidity? A Review of the Literature on Low‐frequency Stock Liquidity

Gregoriou

2020

Journal of Economic Surveys

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Researchers have various ways to measure liquidity but most of them come with both merits and demerits. This study provides a literature review of low-frequency liquidity measures with a primary focus on liquidity measurement as well as its implication on asset pricing. Based on the dimension it captures, a range of existing low-frequency measures are divided into four categories of liquidity proxies including transaction cost, volume, price impact, and multidimensionbased measures. We review some well-established liquidity proxies, a new bid-ask spread estimator and price impact ratios proposed recently. Finally, we discuss how good low-frequency liquidity measures are at capturing standard liquidity benchmarks, which are constructed from high-frequency intraday data.

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“…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%

“…Thus, the dynamics of probability amplitudes is governed by Eq. (17). Writing it for a two-level system, we have:…”

Section: State Dynamics and Execution Probabilitymentioning

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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Option Pricing Under Quantum Theory of Securities Price Formation

Cited by 3 publications

References 2 publications

Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

How Do You Capture Liquidity? A Review of the Literature on Low‐frequency Stock Liquidity

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

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