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on Market Microstructure |
| By: | Alexandru Mandes (University of Gießen) |
| Abstract: | Modeling intraday financial markets by means of agent based models requires an additional building block which reflects the order execution, i.e. the trading process. Current implementations rely only on stochastic placement strategies, ranging from total randomness to adding some budget constraints. This contribution addresses the issue of order placement for low-tech traders, by replacing the zero-intelligence assumption with a microtrading-based approach. The results show that the power-law decaying relative price distribution of off-spread limit orders and the concave shape of the overall market price impact can be replicated when rational order submission strategies are used. |
| Keywords: | agent based modeling, high-frequency financial markets, continuous double auction, order placement, market impact |
| JEL: | C63 N20 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:mar:magkse:201443&r=mst |
| By: | Marcio Garcia (Department of Economics PUC-Rio); Marcelo Medeiros (Department of Economics PUC-Rio); Francisco Eduardo de Luna e Almeida Santos (Department of Economics PUC-Rio) |
| Abstract: | The estimation of the impact of macroeconomic announcements in the Brazilian futuresmarkets is used to uncover the relationship between macroeconomic fundamentals andasset prices. Using intraday data from October 2008 to January 2011, we find thatexternal macroeconomic announcements dominate price changes in the ForeignExchange and Ibovespa futures markets, while the impact of the domestic ones ismainly restricted to Interest Rate futures contracts. We additionally propose aninvestment strategy based on the conditional price reaction of each market that showedpromising results in an out-of-sample study, where we are able to correctly identifyreturns’ signals, conditional on the surprise’s signal, in approximately 70% of the cases.Finally, we provide evidence that price reactions are conditional on the state of theeconomy and document the impact on volume and bid-ask spreads. |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:rio:texdis:623&r=mst |
| By: | Farid Mkaouar; Jean-Luc Prigent |
| Abstract: | In this paper, we examine main properties of the Constant Proportion Portfolio Insurance (CPPI) strategy, when trading in continuous-time is not allowed. We focus instead on stochastic-time rebalancing. We prove that investor's tolerance determines crucially portfolio performance, in particular when taking transaction costs into account. We illustrate this feature in the geometric Brownian case and we provide some numerical insights in this framework. |
| Keywords: | Portfolio insurance; CPPI with transaction cost; Tolerance |
| JEL: | C61 G11 |
| Date: | 2014–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-509&r=mst |
| By: | Götz T.B.; Hecq A.W. (GSBE) |
| Abstract: | In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels 2012, where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a high-frequency one and vice versa. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose two approaches to solve this problem, reduced rank restrictions and a Bayesian mixed-frequency VAR. For the latter, we extend the approach in Banbura et al. 2010 to a mixed-frequency setup, which presents an alternative to classical Bayesian estimation techniques. We compare these methods to a common aggregated low-frequency model as well as to the unrestricted VAR in terms of their Granger non-causality testing behavior using Monte Carlo simulations. The techniques are illustrated in an empirical application involving dailyrealized volatility and monthly business cycle fluctuations. |
| Keywords: | Hypothesis Testing: General; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; |
| JEL: | C12 C32 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:unm:umagsb:2014028&r=mst |