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on Market Microstructure |
| By: | Sandrine Jacob Leal (Cerefige, ICN, Business School,Gredec); Mauro Napoletano (Ofce,Skema Business school,Scuola superiore Sant'Anna); Andrea Roventini (Universita di Verona, Scuola superiore Sant'Anna); Giorgo Fagiolo (Scuola Superiore Sant'Anna Pisa, Italy) |
| Abstract: | We build an agent-based model to study how the interplay between low- and high- frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates ash crashes. In the model, low-frequency agents adopt trading rules based on chrono- logical time and can switch between fundamentalist and chartist strategies. On the contrary, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market in- formation produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore,we find that the presence of high-frequency trading increases market volatility and plays a fundamental role in the generation of ash crashes.The emergence of ash crashes is explained by two salient characteristics of high-frequency traders, i.e. their ability to i) generate high bid-ask spreads and ii) synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of ash crashes but reduce their duration. |
| Keywords: | Agent based models, Limit order book, High frequency trading,low frequency trading, flash crashes, market volatility |
| JEL: | G12 G01 C63 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:fce:doctra:1403&r=mst |
| By: | Kris Boudt (Department of Business, Vrije Universiteit Brussel, Belgium and VU University Amsterdam, Netherlands); Sébastien Laurent (Aix-Marseille University, Aix-Marseille School of Economics, CNRS & EHESS, France); Asger Lunde (Aarhus University and CREATES); Rogier Quaedvlieg (Department of Finance, Maastricht University, Netherlands) |
| Abstract: | An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization on the correlation matrix in order to exploit the heterogeneity in trading intensity to estimate the different parameters sequentially with as many observations as possible. The estimator is guaranteed positive semidefinite. Monte Carlo simulations confirm good finite sample properties. In the application we forecast portfolio Value-at-Risk and sector risk exposures for a portfolio of 52 stocks. We find that forecasts obtained from dynamic models utilizing the proposed high-frequency estimator provide statistically and economically superior forecasts to models using daily returns. |
| Keywords: | Cholesky decomposition, Integrated covariance, Non-synchronous trading, Positive semidefinite, Realized covariance |
| JEL: | C10 C58 |
| Date: | 2014–02–24 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2014-05&r=mst |
| By: | Lyudmila A. Glik; Oleg L. Kritski |
| Abstract: | We propose a mathematical procedure for finding informed traders in ultra-high frequency trading. We wrote it as Vector ARMA and found condition of its stationarity. For the price exposure complied with ARMA(1,2) we proved that underlying asset price difference can be derived as ARMA(1,1) process. For validation of the model, we test an influence of informed traders in EUR/USD, GBP/USD, USD/RUB pairs and futures, in gold and futures prices, in Russian Trade System share index (RTS) and futures trading. We found some evidence of such influence in gold and currency pair USD/RUB pricing, in RTS index in the period from Dec 16 till Dec 20, 2013 and from Jan 28 till Jan 30. |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1402.6583&r=mst |
| By: | Francesco Calvori (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze); Fabrizio Cipollini (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze); Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze) |
| Abstract: | The Volume Weighted Average Price (VWAP) mixes volumes and prices at intra-daily intervals and is a benchmark measure frequently used to evaluate a trader's performance. Under suitable assumptions, splitting a daily order according to ex-ante volume predictions is a good strategy to replicate the VWAP. To bypass possible problems generated by local trends in volumes, we propose a novel Generalized Autoregressive Score (GAS) model for predicting volume shares (relative to the daily total), inspired by the empirical regularities of the observed series (intra-daily periodicity pattern, residual serial dependence). An application to six NYSE tickers confirms the suitability of the model proposed in capturing the features of intra-daily dynamics of volume shares. |
| Keywords: | High Frequency Financial Data, Prediction, Trading Volumes, Volume Shares, VWAP, GAS, Dirichlet Distribution |
| JEL: | C22 C53 C58 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2014_01&r=mst |
| By: | Yang-Yu Liu; Jose C. Nacher; Tomoshiro Ochiai; Mauro Martino; Yaniv Altshuler |
| Abstract: | Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are risk-averse with respect to gains and risk-seeking with respect to losses, a phenomenon called "loss aversion". Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of them have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the loss aversion phenomenon, an essence in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the effect of loss aversion. Moreover, we introduce three risk-adjusted metrics inspired by prospect theory to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading, where traders are allowed to watch and follow the trading activities of others, by predicting potential winners statistically based on their historical trading behavior rather than their trading performance at any given point in time. |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1402.6393&r=mst |