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on Market Microstructure |
| By: | Dirk Becherer ; Todor Bilarev ; Peter Frentrup |
| Abstract: | We study a multiplicative limit order book model for an illiquid market, where price impact by large orders is multiplicative in relation to the current price, transient over time, and non-linear in volume (market) impact. Order book shapes are specified by general density functions with respect to relative price perturbations. Market impact is mean reverting with possibly non-linear resilience. We derive optimal execution strategies that maximize expected discounted proceeds for a large trader over an infinite horizon in one- and also in two-sided order book models, where buying as well as selling is admitted at zero bid-ask spread. Such markets are shown to be free of arbitrage. Market impact as well as liquidation proceeds are stable under continuous Wong-Zakai-type approximations of strategies. |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1501.01892&r=mst |
| By: | Efstathios Panayi ; Gareth Peters |
| Abstract: | In this paper we develop a new form of agent-based model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibration of the model parameters on Level 2 limit order book data from Chi-X, based on a combination of indirect inference and multi-objective optimisation. We then demonstrate how such an agent-based modelling framework can be of use in testing exchange regulations, as well as informing brokerage decisions and other trading based scenarios. |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1501.02447&r=mst |
| By: | Filippo Passerini ; Samuel E. Vazquez |
| Abstract: | We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-trading zone when using market orders, and a corresponding market-making zone when using limit orders. We show that, when combining both market and limit orders, the problem is further divided into zones in which we trade more aggressively using market orders. Even tough we do not solve analytically the full optimization problem, we present explicit and simple analytical recipes which approximate the full solution and are easy to implement in practice. We test the algorithms using Monte Carlo simulations and show how they improve our Profit and Losses. |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1501.03756&r=mst |
| By: | Lorenzo Camponovo ; Yukitoshi Matsushita ; Taisuke Otsu |
| Abstract: | We propose a nonparametric likelihood inference method for the integrated volatility under high frequency financial data. The nonparametric likelihood statistic, which contains the conventional statistics such as empirical likelihood and Pearson's chi-square as special cases, is not asymptotically pivotal under the so-called infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. We show that multiplying a correction term recovers the chi-square limiting distribution. Furthermore, we establish Bartlett correction for our modified nonparametric likelihood statistic under the constant and general non-constant volatility cases. In contrast to the existing literature, the empirical likelihood statistic is not Bartlett correctable under the infill asymptotics. However, by choosing adequate tuning constants for the power divergence family, we show that the second order refinement to the order n^2 can be achieved. |
| Keywords: | Nonparametric likelihood, Volatility, High frequency data |
| JEL: | C14 |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:/2015/581&r=mst |
| By: | Hoechle, Daniel ; Ruenzi, Stefan ; Schaub, Nic ; Schmid, Markus |
| Abstract: | We use a proprietary dataset from a large Swiss retail bank to examine the impact of financial advice on individual investors’ stock trading performance and their behavioral biases. Our data allows us to classify each individual trade as either advised or independent and to compare them in a trade-by-trade within-person analysis. Thus, our study is the first not being plagued by the selection and endogeneity problems typically faced by existing studies on the impact of financial advice and investors’ performance. We document that advisors hurt trading performance and there is not much evidence that they help to reduce behavioral biases. |
| Keywords: | financial advice, individual investors, trading performance, behavioral biases |
| JEL: | D14 G11 G21 |
| URL: | http://d.repec.org/n?u=RePEc:usg:sfwpfi:2014:19&r=mst |