Statistical inferences for price staleness

Kolokolov, Aleksey; Livieri, Giulia; Pirino, Davide

doi:10.1016/j.jeconom.2020.01.021

Cited by 16 publications

(4 citation statements)

References 29 publications

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“…We take the following model of an observed price

and

where

and

are two independent Brownian motions with a length of

; a price of

; and

stands for the case when price is not updated due to price staleness (see [ 20 , 43 ]). Prices are rounded to two digits; thus, the tick size is

.…”

Section: Resultsmentioning

confidence: 99%

Efficiency of the Moscow Stock Exchange before 2022

Shternshis

Mazzarisi

Marmi

2022

Entropy

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This paper investigates the degree of efficiency for the Moscow Stock Exchange. A market is called efficient if prices of its assets fully reflect all available information. We show that the degree of market efficiency is significantly low for most of the months from 2012 to 2021. We calculate the degree of market efficiency by (i) filtering out regularities in financial data and (ii) computing the Shannon entropy of the filtered return time series. We developed a simple method for estimating volatility and price staleness in empirical data in order to filter out such regularity patterns from return time series. The resulting financial time series of stock returns are then clustered into different groups according to some entropy measures. In particular, we use the Kullback–Leibler distance and a novel entropy metric capturing the co-movements between pairs of stocks. By using Monte Carlo simulations, we are then able to identify the time periods of market inefficiency for a group of 18 stocks. The inefficiency of the Moscow Stock Exchange that we have detected is a signal of the possibility of devising profitable strategies, net of transaction costs. The deviation from the efficient behavior for a stock strongly depends on the industrial sector that it belongs to.

show abstract

“…We take the following model of an observed price

and

where

and

are two independent Brownian motions with a length of

; a price of

; and

stands for the case when price is not updated due to price staleness (see [ 20 , 43 ]). Prices are rounded to two digits; thus, the tick size is

.…”

Section: Resultsmentioning

confidence: 99%

Efficiency of the Moscow Stock Exchange before 2022

Shternshis

Mazzarisi

Marmi

2022

Entropy

View full text Add to dashboard Cite

show abstract

“…While the i.i.d. case is natural to provide intuitions, it is highly unrealistic in practice (Kolokolov, Livieri, and Pirino, 2020;Bandi, Kolokolov, Pirino, and Renò, 2020). Below we propose a correction to multipower estimators which is designed to work for a more realistic specification of Bernoulli variables presented by Assumption 1.…”

Section: Bias Of Power Variationmentioning

confidence: 99%

Jumps or Staleness?

Kolokolov

Renò

2023

Journal of Business & Economic Statistics

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Even moderate amounts of zero returns in financial data, associated with stale prices, are heavily detrimental for reliable jump inference. We harness staleness-robust estimators to reappraise the statistical features of jumps in financial markets. We find that jumps are much less frequent and much less contributing to price variation than what found by the empirical literature so far. In particular, the empirical finding that volatility is driven by a pure jump process is actually shown to be an artifact due to staleness.

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“…where W 1 and W 2 are two independent Brownian motions with the length of 2N , N = 10 5 , price P 0 = 100, and ν = 10 −4 . B = 1 stands for the case when price is not updated due to price staleness (see [13,36]). Prices are rounded to two digits, thus the tick size is d = 0.01.…”

Section: Simulationsmentioning

confidence: 99%

Efficiency of the Moscow Stock Exchange before 2022

Shternshis¹,

Mazzarisi²,

Marmi³

2022

Preprint

View full text Add to dashboard Cite

This paper investigates the degree of efficiency for the Moscow Stock Exchange. A market is called efficient if prices of its assets fully reflect all available information. We show that the degree of market efficiency is significantly low for most of the months from 2012 to 2021. We calculate the degree of market efficiency by (i) filtering out regularities in financial data and (ii) computing the Shannon entropy of the filtered return time series. We have developed a simple method for estimating volatility and price staleness in empirical data, in order to filter out such regularity patterns from return time series. The resulting financial time series of stocks' returns are then clustered into different groups according to some entropy measures. In particular, we use the Kullback-Leibler distance and a novel entropy metric capturing the co-movements between pairs of stocks. By using Monte Carlo simulations, we are then able to identify the time periods of market inefficiency for a group of 18 stocks. The inefficiency of the Moscow Stock Exchange that we have detected is a signal of the possibility of devising profitable strategies, net of transaction costs. The deviation from the efficient behavior for a stock strongly depends on the industrial sector it belongs.

show abstract

Statistical inferences for price staleness

Cited by 16 publications

References 29 publications

Efficiency of the Moscow Stock Exchange before 2022

Efficiency of the Moscow Stock Exchange before 2022

Jumps or Staleness?

Efficiency of the Moscow Stock Exchange before 2022

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