The Experts below are selected from a list of 12585 Experts worldwide ranked by ideXlab platform
Anna A Obizhaeva - One of the best experts on this subject based on the ideXlab platform.
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Market Microstructure invariance a dynamic equilibrium model
2020Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:We derive invariance relationships in a dynamic, infinite-horizon, equilibrium model of adverse selection with risk-neutral informed traders, noise traders, Market makers, and with endogenous information production. Scaling laws for bet size and transaction costs require the assumption that the effort required to generate one bet does not vary across securities and time. Scaling laws for pricing accuracy and Market resiliency require the additional assumption that private information has the same signal-to-noise ratio across Markets. Prices follow a martingale with endogenously derived stochastic volatility. Returns volatility, pricing accuracy, liquidity, and Market resiliency are connected by a specific proportionality relationship. The model solution depends on two state variables: stock price and hard-to-observe pricing accuracy. Invariance makes predictions operational by expressing them in terms of log-linear functions of easily observable variables such as price, volume, and volatility.
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dimensional analysis and Market Microstructure invariance
2017Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:Market Microstructure is the subfield of finance and econophysics1 which studies how prices result from the process of trading securities. Large trades move prices2 and incur trading costs. Here we combine dimensional analysis, leverage neutrality, and a principle of Market Microstructure invariance to derive scaling laws which express transaction costs functions, bid-ask spreads, bet sizes, number of bets, and other financial variables in terms of trading volume and volatility. For example, Market liquidity is proportional to the cube root of the ratio of dollar volume to return variance. We illustrate the scaling by showing that bid-ask spreads in Russian stocks indeed scale with the cube root. In addition to being of interest to risk managers and traders, these scaling laws provide scientific benchmarks for evaluating controversial issues related to high frequency trading, Market crashes, and liquidity measurement as well as guidelines for designing policies in the aftermath of financial crisis.
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a practitioner s guide to Market Microstructure invariance
2016Co-Authors: Albert S Kyle, Anna A Obizhaeva, Mark KritzmanAbstract:The authors present a hypothesis of Market Microstructure invariance, which follows from the assumption that risk transfer and transaction costs are the same for all stocks when trades are converted to bets, calendar time is converted to business time, and return volatility is converted to dollar volatility. This hypothesis generates simple operational formulas for determining the distribution of bet sizes, trading patterns, and transaction costs as nonlinear functions of volume and volatility.
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Market Microstructure invariance empirical hypotheses
2016Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:Using the intuition that financial Markets transfer risks in business time, “Market Microstructure invariance” is defined as the hypotheses that the distributions of risk transfers (“bets”) and transaction costs are constant across assets when measured per unit of business time. The invariance hypotheses imply that bet size and transaction costs have specific, empirically testable relationships to observable dollar volume and volatility. Portfolio transitions can be viewed as natural experiments for measuring transaction costs, and individual orders can be treated as proxies for bets. Empirical tests based on a data set of 400,000+ portfolio transition orders support the invariance hypotheses. The constants calibrated from structural estimation imply specific predictions for the arrival rate of bets (“Market velocity”), the distribution of bet sizes, and transaction costs.
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dimensional analysis and Market Microstructure invariance
2016Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:This paper combines dimensional analysis, leverage neutrality, and a principle of Market Microstructure invariance to derive scaling laws expressing transaction costs functions, bid-ask spreads, bet sizes, number of bets, and other financial variables in terms of dollar trading volume and volatility. The scaling laws are illustrated using data on bid-ask spreads and number of trades for Russian stocks. These scaling laws provide useful metrics for risk managers and traders; scientific benchmarks for evaluating controversial issues related to high frequency trading, Market crashes, and liquidity measurement; and guidelines for designing policies in the aftermath of financial crisis.
Albert S Kyle - One of the best experts on this subject based on the ideXlab platform.
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Market Microstructure invariance a dynamic equilibrium model
2020Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:We derive invariance relationships in a dynamic, infinite-horizon, equilibrium model of adverse selection with risk-neutral informed traders, noise traders, Market makers, and with endogenous information production. Scaling laws for bet size and transaction costs require the assumption that the effort required to generate one bet does not vary across securities and time. Scaling laws for pricing accuracy and Market resiliency require the additional assumption that private information has the same signal-to-noise ratio across Markets. Prices follow a martingale with endogenously derived stochastic volatility. Returns volatility, pricing accuracy, liquidity, and Market resiliency are connected by a specific proportionality relationship. The model solution depends on two state variables: stock price and hard-to-observe pricing accuracy. Invariance makes predictions operational by expressing them in terms of log-linear functions of easily observable variables such as price, volume, and volatility.
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dimensional analysis and Market Microstructure invariance
2017Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:Market Microstructure is the subfield of finance and econophysics1 which studies how prices result from the process of trading securities. Large trades move prices2 and incur trading costs. Here we combine dimensional analysis, leverage neutrality, and a principle of Market Microstructure invariance to derive scaling laws which express transaction costs functions, bid-ask spreads, bet sizes, number of bets, and other financial variables in terms of trading volume and volatility. For example, Market liquidity is proportional to the cube root of the ratio of dollar volume to return variance. We illustrate the scaling by showing that bid-ask spreads in Russian stocks indeed scale with the cube root. In addition to being of interest to risk managers and traders, these scaling laws provide scientific benchmarks for evaluating controversial issues related to high frequency trading, Market crashes, and liquidity measurement as well as guidelines for designing policies in the aftermath of financial crisis.
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a practitioner s guide to Market Microstructure invariance
2016Co-Authors: Albert S Kyle, Anna A Obizhaeva, Mark KritzmanAbstract:The authors present a hypothesis of Market Microstructure invariance, which follows from the assumption that risk transfer and transaction costs are the same for all stocks when trades are converted to bets, calendar time is converted to business time, and return volatility is converted to dollar volatility. This hypothesis generates simple operational formulas for determining the distribution of bet sizes, trading patterns, and transaction costs as nonlinear functions of volume and volatility.
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Market Microstructure invariance empirical hypotheses
2016Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:Using the intuition that financial Markets transfer risks in business time, “Market Microstructure invariance” is defined as the hypotheses that the distributions of risk transfers (“bets”) and transaction costs are constant across assets when measured per unit of business time. The invariance hypotheses imply that bet size and transaction costs have specific, empirically testable relationships to observable dollar volume and volatility. Portfolio transitions can be viewed as natural experiments for measuring transaction costs, and individual orders can be treated as proxies for bets. Empirical tests based on a data set of 400,000+ portfolio transition orders support the invariance hypotheses. The constants calibrated from structural estimation imply specific predictions for the arrival rate of bets (“Market velocity”), the distribution of bet sizes, and transaction costs.
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dimensional analysis and Market Microstructure invariance
2016Co-Authors: Albert S Kyle, Anna A ObizhaevaAbstract:This paper combines dimensional analysis, leverage neutrality, and a principle of Market Microstructure invariance to derive scaling laws expressing transaction costs functions, bid-ask spreads, bet sizes, number of bets, and other financial variables in terms of dollar trading volume and volatility. The scaling laws are illustrated using data on bid-ask spreads and number of trades for Russian stocks. These scaling laws provide useful metrics for risk managers and traders; scientific benchmarks for evaluating controversial issues related to high frequency trading, Market crashes, and liquidity measurement; and guidelines for designing policies in the aftermath of financial crisis.
Kathryn M E Dominguez - One of the best experts on this subject based on the ideXlab platform.
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the Market Microstructure of central bank intervention
2003Co-Authors: Kathryn M E DominguezAbstract:Abstract How quickly do central bank intervention operations impact the foreign exchange Market? And, do intra-daily Market conditions influence the effectiveness of central bank interventions? This paper uses high-frequency intra-daily data to examine the relationship between the efficacy of intervention operations and the “state of the Market” at the moment that the operation is made public. The results indicate that some traders typically know that the Fed is intervening at least 1 h prior to the public release of the information in newswire reports. Also, the evidence suggests that the timing of intervention operations matters—interventions that occur during heavy trading volume, that are closely timed to scheduled macro announcements, and that are coordinated with another central bank are the most likely to have large effects.
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the Market Microstructure of central bank intervention
1997Co-Authors: Kathryn M E DominguezAbstract:One of the great unknowns in international finance is the process by which new information influences exchange rate behavior. Until recently, data constraints have limited our ability to examine this issue. The Olsen and Associates high-frequency spot Market data greatly expand the range of testable hypotheses regarding the influence of information. This paper focuses on one important source of information to the foreign exchange Markets, the intervention operations of the G-3 central banks. Previous studies using daily and weekly foreign exchange rate data suggest that central bank intervention operations can influence both the level and variance of exchange rates, but little is known about how exactly traders learn about these operations and whether intra-daily Market conditions influence their effectiveness. Using high-frequency data, this paper will examine the relationship between the efficacy of intervention operations and the "state of the Market" at the moment that the operation is made public to traders.
Per A Mykland - One of the best experts on this subject based on the ideXlab platform.
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the five trolls under the bridge principal component analysis with asynchronous and noisy high frequency data
2020Co-Authors: Dachuan Chen, Per A Mykland, Lan ZhangAbstract:We develop a principal component analysis (PCA) for high frequency data. As in Northern fairy tales, there are trolls waiting for the explorer. The first three trolls are Market Microstructure nois...
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discerning non stationary Market Microstructure noise and time varying liquidity in high frequency data
2016Co-Authors: Richard Y Chen, Per A MyklandAbstract:In this paper, we investigate the implication of non-stationary Market Microstructure noise to integrated volatility estimation, provide statistical tools to test stationarity and non-stationarity in Market Microstructure noise, and discuss how to measure liquidity risk using high frequency financial data. In particular, we discuss the impact of non-stationary Microstructure noise on TSRV (Two-Scale Realized Variance) estimator, and design three test statistics by exploiting the edge effects and asymptotic approximation. The asymptotic distributions of these test statistics are provided under both stationary and non-stationary noise assumptions respectively, and we empirically measure aggregate liquidity risks by these test statistics from 2006 to 2013. As byproducts, functional dependence and endogenous Market Microstructure noise are briefly discussed. Simulation studies corroborate our theoretical results. Our empirical study indicates the prevalence of non-stationary Market Microstructure noise in the New York Stock Exchange.
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jumps in equilibrium prices and Market Microstructure noise
2012Co-Authors: Suzanne S Lee, Per A MyklandAbstract:Abstract Asset prices observed in financial Markets combine equilibrium prices and Market Microstructure noise. In this paper, we study how to tell apart large shifts in equilibrium prices from noise using high frequency data. We propose a new nonparametric test which allows us to asymptotically remove the noise from observable price data and to discover jumps in fundamental asset values. We provide its asymptotic distribution to decide when such jumps occur. In finite samples, our test offers reasonable power for distinguishing between noise and jumps. Empirical evidence indicates that it is necessary to incorporate the presence of jumps in equilibrium prices.
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how often to sample a continuous time process in the presence of Market Microstructure noise
2005Co-Authors: Yacine Aitsahalia, Per A Mykland, Lan ZhangAbstract:In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When Market Microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derive its closed-form expression. But even with optimal sampling, using say five minute returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. We demonstrate that modelling the noise and using all the data is a better solution, even if one misspecifies the noise distribution. So the answer is: sample as often as possible.
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how often to sample a continuous time process in the presence of Market Microstructure noise
2003Co-Authors: Yacine Aitsahalia, Per A MyklandAbstract:Classical statistics suggest that for inference purposes one should always use as much data as is available. We study how the presence of Market Microstructure noise in high-frequency financial data can change that result. We show that the optimal sampling frequency at which to estimate the parameters of a discretely sampled continuous-time model can be finite when the observations are contaminated by Market Microstructure effects. We then address the question of what to do about the presence of the noise. We show that modelling the noise term explicitly restores the first order statistical effect that sampling as often as possible is optimal. But, more surprisingly, we also demonstrate that this is true even if one misspecifies the assumed distribution of the noise term. Not only is it still optimal to sample as often as possible, but the estimator has the same variance as if the noise distribution had been correctly specified, implying that attempts to incorporate the noise into the analysis cannot do more harm than good. Finally, we study the same questions when the observations are sampled at random time intervals, which are an essential feature of transaction-level data.
Mathias Vetter - One of the best experts on this subject based on the ideXlab platform.
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bias correcting the realized range based variance in the presence of Market Microstructure noise
2008Co-Authors: Kim Christensen, Mark Podolskij, Mathias VetterAbstract:Market Microstructure noise is a challenge to high-frequency based estimation of the integrated variance, because the noise accumulates with the sampling frequency. This has lead to widespread use of constructing the realized variance, a sum of squared intraday returns, from sparsely sampled data, for example 5- or 15-minute returns. In this paper, we analyze the impact of Microstructure noise on the realized range-based variance and propose a bias-correction to the range-statistic. The new estimator is shown to be consistent for the integrated variance and asymptotically mixed Gaussian under simple forms of Microstructure noise. We can select an optimal partition of the high-frequency data in order to minimize its asymptotic conditional variance. The finite sample properties of our estimator are studied with Monte Carlo simulations and we implement it using Microsoft high-frequency data from TAQ. We find that a bias-corrected range-statistic often leads to much smaller confidence intervals for the integrated variance, relative to the realized variance.
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bias correcting the realized range based variance in the presence of Market Microstructure noise
2006Co-Authors: Kim Christensen, Mark Podolskij, Mathias VetterAbstract:Market Microstructure noise is a challenge to high-frequency based estimation of the integrated variance, because the noise accumulates with the sampling frequency. In this paper, we analyze the impact of Microstructure noise on the realized range-based variance and propose a bias-correction to the rangestatistic. The new estimator is shown to be consistent for the integrated variance and asymptotically mixed Gaussian under simple forms of Microstructure noise, and we can select an optimal partition of the high-frequency data in order to minimize its asymptotic conditional variance. The finite sample properties of our estimator are studied with Monte Carlo simulations and we implement it on high-frequency data from TAQ. We find that a bias-corrected range-statistic often has much smaller confidence intervals than the realized variance.