|
on Market Microstructure |
| By: | Marco Cipriani (Institute for Fiscal Studies); Antonio Guarino (Institute for Fiscal Studies); Andreas Uthemann (Institute for Fiscal Studies) |
| Abstract: | We develop a new methodology to estimate the impact of a financial transaction tax (FTT) on informational efficiency, liquidity and volatility. In our sequential trading model there are price elastic noise traders and traders with private information of heterogeneous quality. We estimate the model without a tax and then quantify the effect of an FTT. In our sample, noise traders are price elastic but less so than informed traders. The introduction of an FTT changes the composition of the market, lowering informational efficiency. Even a small, 5 bps, FTT impedes correct price convergence on a sizeable percentage of days. |
| Keywords: | Financial Transaction Tax, Market Microstructure, Structural Estimation |
| Date: | 2019–02–06 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:cemmap:07/19&r=all |
| By: | Fr\'ed\'eric Bucci; Fabrizio Lillo; Jean-Philippe Bouchaud; Michael Benzaquen |
| Abstract: | We revisit the trading invariance hypothesis recently proposed by Kyle and Obizhaeva by empirically investigating a large dataset of bets, or metaorders, provided by ANcerno. The hypothesis predicts that the quantity $I:=\ri/N^{3/2}$, where $\ri$ is the exchanged risk (volatility $\times$ volume $\times$ price) and $N$ is the number of bets, is invariant. We find that the $3/2$ scaling between $\ri$ and $N$ works well and is robust against changes of year, market capitalisation and economic sector. However our analysis clearly shows that $I$ is not invariant. We find a very high correlation $R^2>0.8$ between $I$ and the total trading cost (spread and market impact) of the bet. We propose new invariants defined as a ratio of $I$ and costs and find a large decrease in variance. We show that the small dispersion of the new invariants is mainly driven by (i) the scaling of the spread with the volatility per transaction, (ii) the near invariance of the distribution of metaorder size and of the volume and number fractions of bets across stocks. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.03457&r=all |
| By: | Jieun Lee (Economic Research Institute, The Bank of Korea); Doojin Ryu (College of Economics, Sungkyunkwan University) |
| Abstract: | This study examines the response of intraday options-implied volatilities to scheduled announcements of major macroeconomic indicators. By analyzing the KOSPI 200 options intraday data, we find that the abnormal implied volatility significantly increases around announcements of macroeconomic news and that the extent of the response is influenced by a variety of factors, including the type of macroeconomic indicators released, option type and economic conditions. Specifically, the increase in implied volatility around these announcements is more pronounced for puts than for calls. These effects are also more pronounced in the crisis and post-crisis periods than in the pre-crisis period. Monetary policy announcements have a more substantial impact on implied volatility than other announcements, even after controlling for news surprise components. Finally, the impact appears to be greater for policy rate hikes than for policy rate cuts. |
| Keywords: | Event study; Intraday volatility; KOSPI 200 options; Macroeconomic news announcement |
| JEL: | E52 G10 G14 |
| Date: | 2019–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:bok:wpaper:1902&r=all |
| By: | Ahmed Bel Hadj Ayed (FiQuant); Emilio Said (FiQuant); Ahmed Bel (FiQuant); Hadj Ayed (FiQuant); Damien Thillou (FiQuant); Jean-Jacques Rabeyrin (FiQuant); Fr\'ed\'eric Abergel (FiQuant) |
| Abstract: | This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders-large orders that are split into smaller pieces before being sent to the market on one of the main Asian markets. In line with our previous work on the equity market [Said et al., 2018], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well understood equity market: Square-root law, Fair Pricing Condition and Market Impact Dynamics. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.05418&r=all |
| By: | Chariton Chalvatzis; Dimitrios Hristu-Varsakelis |
| Abstract: | We present a deep long short-term memory (LSTM)-based neural network for predicting asset prices, together with a successful trading strategy for generating profits based on the model's predictions. Our work is motivated by the fact that the effectiveness of any prediction model is inherently coupled to the trading strategy it is used with, and vise versa. This highlights the difficulty in developing models and strategies which are jointly optimal, but also points to avenues of investigation which are broader than prevailing approaches. Our LSTM model is structurally simple and generates predictions based on price observations over a modest number of past trading days. The model's architecture is tuned to promote profitability, as opposed to accuracy, under a strategy that does not trade simply based on whether the price is predicted to rise or fall, but rather takes advantage of the distribution of predicted returns, and the fact that a prediction's position within that distribution carries useful information about the expected profitability of a trade. The proposed model and trading strategy were tested on the S&P 500, Dow Jones Industrial Average (DJIA), NASDAQ and Russel 2000 stock indices, and achieved cumulative returns of 329%, 241%, 468% and 279%, respectively, over 2010-2018, far outperforming the benchmark buy-and-hold strategy as well as other recent efforts. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.03125&r=all |
| By: | Achraf Bahamou; Maud Doumergue; Philippe Donnat |
| Abstract: | Targeting a better understanding of credit market dynamics, the authors have studied a stochastic model named the Hawkes process. Describing trades arrival times, this kind of model allows for the capture of self-excitement and mutual interactions phenomena. The authors propose here a simple yet conclusive method for fitting multidimensional Hawkes processes with exponential kernels, based on a maximum likelihood non-convex optimization. The method was successfully tested on simulated data, then used on new publicly available real trading data for three European credit indices, thus enabling quantification of self-excitement as well as volume impacts or cross indices influences. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.03714&r=all |