An Empirical Analysis of Stock and Bond Market Liquidity

Chordia, Tarun; Sarkar, Asani; Subrahmanyam, Avanidhar

doi:10.1093/rfs/hhi010

Cited by 629 publications

(147 citation statements)

References 68 publications

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“…17 Several papers, including Hasbrouck (1991), Hiemstra and Jones (1994), Chordia, Roll, and Subrahmanyam (2001), Chordia, Sarkar, and Subrahmanyam (2005a), and Chordia, Sarkar, and Subrahmanyam (2005b, have studied the time-series relation between several measures of liquidity, trading activity, and returns. However, they do not analyze the causality from liquidity to trading volume.…”

Section: Vector Autoregression Resultsmentioning

confidence: 99%

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Stock Returns and the Volatility of Liquidity

Pereira

Zhang

2010

J. Financ. Quant. Anal.

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This paper offers a rational explanation for the puzzling empirical fact that stock returns decrease in the volatility of liquidity. We model liquidity as a stochastic price impact process and define the liquidity premium as the additional return necessary to compensate a multi-period investor for the adverse price impact of trading. The model demonstrates that a fully rational, utility maximizing, risk averse investor can take advantage of time-varying liquidity by adapting his trades to the state of liquidity. We provide new empirical evidence supportive of the model. * We are grateful for the helpful comments of Dong

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Section: Vector Autoregression Resultsmentioning

confidence: 99%

“…To account for documented seasonal regularities in trading (e.g., Chordia, Sarkar, and Subrahmanyam (2005a)), each row in the matrix C includes a constant plus 4 dummies for day-of-theweek and 11 dummies for calendar month. The matrices A l represent the coefficients to be estimated.…”

Section: Vector Autoregression Resultsmentioning

confidence: 99%

Stock Returns and the Volatility of Liquidity

Pereira

Zhang

2010

J. Financ. Quant. Anal.

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“…New money that flows into international funds goes either to bond funds, equity funds or a combination of both, which could result in interdependence of net flows into the bond and equity investments. 17 Generally, within the mutual fund, investors tend to reallocate their money from equity to bond funds when there is a negative shock on equity markets, and vice versa (e.g., Chordia et al, 2005): the asset reallocation between equity and bond funds means a negative correlation between equity and bond net inflows. Thus, the association between bond and equity net inflows depends on the effect of new money relative to the effect of asset reallocation.…”

Section: Comovements Of International Equity and Bond Investmentmentioning

confidence: 99%

International fund investment and local market returns

Jinjarak

Wongswan

Zheng

2011

Journal of Banking & Finance

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a b s t r a c tInternational fund investment in bonds and equities is characterized by a positive association between current net inflows and contemporaneous and past market returns: positive-feedback trading, while being possibly profitable for international fund investors, could be destabilizing for the underlying markets. Allowing for interactions between equity investment and bond investment, our panel vector autoregression shows that past equity returns contain useful information in forecasting equity and bond flows and that bond flows impact future equity returns positively.

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“…Serial dependence also exists in limit order demand and supply, see Dierker, Kim, Lee and Morck (2014). Chordia, Sarkar and Subrahmanyam (2003) documented the cross-sectional dependence among multiple liquidity measures using a Vector AutoRegressive model for bid-ask spreads, depth, volatility, returns, and order flow in the stock and bond markets, where a liquidity measure not only depends on its own past values, but also those of other measures. Ç etin, Jarrow and Protter (2004) introduced liquidity supply curve for robust arbitrage pricing theory.…”

Section: Vfarrandbidaskcurveplotmentioning

confidence: 99%

Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional Autoregressive Dynamics

2016

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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 * Corresponding author: Wee Song Chua Email: a0054070@u.nus.edu Phone: +65-6516 3470 Fax: +65-6872 3919 This research was supported by the FRC grant at the National University of Singapore. Support from IRTG 1792 "High Dimensional Non Stationary Time Series", Humboldt-Universität zu Berlin, is gratefully acknowledged. 1 traded from 2 Jan 2015 to 6 Mar 2015, 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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An Empirical Analysis of Stock and Bond Market Liquidity

Cited by 629 publications

References 68 publications

Stock Returns and the Volatility of Liquidity

Stock Returns and the Volatility of Liquidity

International fund investment and local market returns

Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional Autoregressive Dynamics

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