Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models

Groß-Klußmann, Axel; Hautsch, Nikolaus

doi:10.1002/for.2267

Cited by 27 publications

(15 citation statements)

References 104 publications

(167 reference statements)

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“…Lux and Kaizoji () and Deo, Hsieh, and Hurvich () also find long memory in trading volumes, and in trade durations, respectively. Recently, Groß‐Klußmann and Hautsch () show that bid‐ask spread is strongly serially dependent.…”

Section: Empirical Methodologymentioning

confidence: 99%

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

2014

Journal of Futures Markets

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This study investigates the limit order book characteristics and the intertwined dynamics between trading patience, order flows, and liquidity in an index futures market. The limit order book displays a hump shape and the steepness of the hump is positively related to the number of large market orders. Bid and ask prices tend to move together in the same direction. Moreover, higher proportion of patient traders and higher order arrival rate lead to higher liquidity, after controlling for volatility and the intraday time effect. Liquidity is found to have a feedback effect on trading patience and the order arrival rate.

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Section: Empirical Methodologymentioning

confidence: 99%

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

2014

Journal of Futures Markets

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“…Spread process. It is well known that spread process is autocorrelated in time [42,4,7,18]. We model the spread s (t) as a stationary Markov(1) [41] process 5 :…”

Section: Markov-switching Modelsmentioning

confidence: 99%

Modeling the coupled return-spread high frequency dynamics of large tick assets

Curato

Lillo

2015

J. Stat. Mech.

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Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We introduce a Markov-switching modeling approach for price change, where the latent Markov process is the transition between spreads. We then use a finite Markov mixture of logit regressions on past squared returns to describe the dependence of the probability of price changes. The model can thus be seen as a Double Chain Markov Model. We show that the model describes the shape of return distribution at different time aggregations, volatility clustering, and the anomalous decrease of kurtosis of returns. We calibrate our models on Nasdaq stocks and we show that this model reproduces remarkably well the statistical properties of real data.

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“…Au-toRegressive models have been employed to describe the dynamics of the liquidity measures. Groß-Klußmann and Hautsch (2013) proposed a long memory AutoRegressive conditional Poisson model for the quoted bid-ask spreads. Huberman and Halka (2001) evidenced the serial dependence of bid-ask spread and depth in the AutoRegressive model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamics

Chen

Chua

Härdle

2019

Quantitative Finance

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Limit order book contains comprehensive information of liquidity on bid and ask sides. We propose a Vector Functional AutoRegressive (VFAR) model to describe the dynamics of the limit order book and demand curves and utilize the fitted model to predict the joint evolution of the liquidity demand and supply curves. In the VFAR framework, we derive a closed-form maximum likelihood estimator under sieves and provide the asymptotic consistency of the estimator. In application to limit order book records of 12 stocks in NASDAQ it shows the VAR model presents a strong predictability in liquidity curves, with R 2 values as high as 98.5 percent for insample estimation and 98.2 percent in out-of-sample forecast experiments. It produces accurate 5−, 25− and 50−minute forecasts, with root mean squared error as low as 0.09 to 0.58 and mean absolute percentage error as low as 0.3 to 4.5 percent.

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Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models

Cited by 27 publications

References 104 publications

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

Modeling the coupled return-spread high frequency dynamics of large tick assets

Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamics

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