New Economics Papers
on Market Microstructure
Issue of 2012‒04‒17
four papers chosen by
Thanos Verousis

  1. Price Jump Prediction in Limit Order Book By Ban Zheng; Eric Moulines; Fr\'ed\'eric Abergel
  2. Patience and Impatience of Stock Traders By Peter Lerner
  3. Hedging through a Limit Order Book with Varying Liquidity By Rossella Agliardi; Ramazan Gençay
  4. Realized Wavelet Jump-GARCH model: Can wavelet decomposition of volatility improve its forecasting? By Jozef Barunik; Lukas Vacha

  1. By: Ban Zheng; Eric Moulines; Fr\'ed\'eric Abergel
    Abstract: A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we define price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of different features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we find that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.
    Date: 2012–04
  2. By: Peter Lerner
    Abstract: I derive asymptotic distribution of the bids/offers as a function of proportion between patient and impatient traders using my modification of Foucault, Kadan and Kandel dynamic Limit Order Book (LOB) model. Distribution of patient and impatient traders asymptotically obeys rather simple PDE, which admits numerical solutions. My modification of LOB model allows stylized but sufficiently realistic representation of the trading markets. In particular, dynamic LOB allows simulating the distribution of execution times and spreads from high-frequency quotes. Significant analytic progress is made towards future empirical study of trading as competition for immediacy of execution between traders. The results are qualitatively compared with empirical volumeat- price distribution of liquid stocks.
    Date: 2012–04
  3. By: Rossella Agliardi (Dipartimento di Matematica, Università di Bologna, Italy; IMATI - CNR, Italy); Ramazan Gençay (Department of Economics, Simon Fraser University, Canada; The Rimini Centre for Economic Analysis (RCEA), Italy)
    Abstract: We relax the classical price-taking assumption and study the impact of orders of arbitrary size on price when the availability of liquidity is a concern in hedging. Our paper extends the earlier literature, suggesting that an environment with a permanent impact can be viewed as a special case with zero resilience, whereas an environment with a temporary impact can be viewed as a limit case with infinite resilience speed. Furthermore, our results hold for more general stochastic processes for the underlying asset: for example, for a generic Lévy process.
    Keywords: hedging, large traders, limited liquidity, resilience, limit order book
    JEL: G13 C22
    Date: 2012–04
  4. By: Jozef Barunik; Lukas Vacha
    Abstract: In this paper, we propose a forecasting model for volatility based on its decomposition to several investment horizons and jumps. As a forecasting tool, we utilize Realized GARCH framework of Hansen et al. (2011), which models jointly returns and realized measures of volatility. For the decomposition, we use jump wavelet two scale realized volatility estimator (JWTSRV) of Barunik and Vacha (2012). While the main advantage of our time-frequency estimator is that it provides us with realized volatility measure robust to noise as well as with consistent estimate of jumps, it also allows to decompose volatility into the several investment horizons. On currency futures data covering the period of recent financial crisis, we compare forecasts from Realized GARCH(1,1) model using several measures. Namely, we use the realized volatility, bipower variation, two- scale realized volatility, realized kernel and our jump wavelet two scale realized volatility. We find that in-sample as well as out-of-sample performance of the model significantly differs based on the realized measure used. When JWTSRV estimator is used, model produces significantly best forecasts. We also utilize jumps and build Realized Jump-GARCH model. Utilizing the decomposition obtained by our estimator, we finally build Realized Wavelet-Jump GARCH model, which uses estimated jumps as well as volatility at several investment horizons. Our Realized Wavelet-Jump GARCH model proves to further improve the volatility forecasts. We conclude that realized volatility measurement in the time-frequency domain and inclusion of jumps improves the volatility forecasting considerably.
    Date: 2012–04

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