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on Market Microstructure |
| By: | Alain Chaboud; Benjamin Chiquoine; Erik Hjalmarsson; Clara Vega |
| Abstract: | We study the impact that algorithmic trading, computers directly interfacing at high frequency with trading platforms, has had on price discovery and volatility in the foreign exchange market. Our dataset represents a majority of global interdealer trading in three major currency pairs in 2006 and 2007. Importantly, it contains precise observations of the size and the direction of the computer-generated and human-generated trades each minute. The empirical analysis provides several important insights. First, we find evidence that algorithmic trades tend to be correlated, suggesting that the algorithmic strategies used in the market are not as diverse as those used by non-algorithmic traders. Second, we find that, despite the apparent correlation of algorithmic trades, there is no evident causal relationship between algorithmic trading and increased exchange rate volatility. If anything, the presence of more algorithmic trading is associated with lower volatility. Third, we show that even though some algorithmic traders appear to restrict their activity in the minute following macroeconomic data releases, algorithmic traders increase their provision of liquidity over the hour following each release. Fourth, we find that non-algorithmic order flow accounts for a larger share of the variance in exchange rate returns than does algorithmic order flow. Fifth, we find evidence that supports the recent literature that proposes to depart from the prevalent assumption that liquidity providers in limit order books are passive. |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:980&r=mst |
| By: | Álvaro Cartea; Dimitrios Karyampas (Department of Economics, Mathematics & Statistics, Birkbeck) |
| Abstract: | Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance-covariance matrix of n. assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true effcient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of n assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructre noise affects portfolio decisions and calculations of the equity beta in a CAPM setting. |
| Date: | 2009–10 |
| URL: | http://d.repec.org/n?u=RePEc:bbk:bbkefp:0913&r=mst |
| By: | Dimitrios Vortelinos; Dimitrios Thomakos |
| Abstract: | We test for and model volatility jumps for three major indices of the Athens Stock Exchange (ASE). Using intraday data we first construct several, state-of-the-art realized volatility estimators. We use these estimators to construct the jump components of volatility and perform various tests on their properties. Then we use the class of Heterogeneous Autoregressive (HAR) models for assessing the relevant effects of jumps on volatility. Our results expand and complement the previous literature on the ASE market and, in particular, this is the first time, to the best of our knowledge, that volatility jumps are examined and modeled for the Greek market, using a variety of realized volatility estimators. |
| Keywords: | Athens Stock Exchange , Bipower variation, Heterogeneous autoregressive models, Realized volatility, Volatility jumps. |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:00044&r=mst |
| By: | Dimitrios Vortelinos |
| Abstract: | This paper examines the economic value of various realized volatility and covariance estimators under the strategy of volatility timing. There are used three types of portfolios: Global Minimum Variance, Capital Market Line kai Capital Market Line with only positive weights. The state-of-the-art estimators of volatilities and covariances use 5-min high-frequency intraday data. The dataset concerns the FTSE-20, FTSE-40 and FTSE-80 indices of the Athens Stock Exchange (ASE). As far as I know, this is the rst work of its kind for the ASE equity market. Results concern not only the comparison of various estimators but also the comparison of different types of portfolios, in the strategy of volatility timing. The economic value of the contemporary non-parametric realized volatility estimators is more significant than the covariance of the daily squared returns. Moreover, the economic value of each estimator changes with the volatility timing. |
| Keywords: | portfolio analysis, intraday data, optimal sampling, microstructure, volatility forecasting, covariance, Athens Stock Exchange, volatility timing. |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:00046&r=mst |
| By: | Dimitrios Vortelinos; Dimitrios Thomakos |
| Abstract: | This paper investigates the economic value of dierent non-parametric realized volatility estimates in Efficient Frontier, Global Minimum Variance,Capital Market Line and Capital Market Line with only positive weights portfolio types. The dataset concerns the CAC40 index, the DAX index and the General Index (GD) of the Athens Stock Exchange. We use the unrestricted realized volatility estimator, the realized optimally sampled volatility estimator and their bias-corrections against the benchmark of the daily squared returns. The value of switching from daily to intraday returns in estimating the covariance matrix is substantial. The type of realized volatility estimator used is also important. This is proven true according to the portfolio statistic measures (mean, standard deviation, Sharp Ratio and Cumulative Return), the basis points that a risk averse investor is willing to pay per year in order to gain from the realized covariance estimates instead of the daily squared returns, the proportion of times that the average portfolio return of the realized covariance forecasts is higher than the benchmark and the proportion of combinations of portfolio parameters for which the above proportion measure is higher than or equals to the 50% of the total combinations of portfolio parameters used. |
| Keywords: | CAC40, DAX, ASE, portfolios, covariance, realized volatility, bias-correction, optimal sampling, microstructure noise, forecast, evaluation. |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:00042&r=mst |
| By: | Dimitrios Vortelinos |
| Abstract: | In this paper I test for and model volatility jumps for the General Index (GD) of the Athens Stock Exchange (ASE), expanding the previous literature on the ASE in various ways. Using intraday data I first construct various state-of-the-art realized volatility estimators which I then use in testing and modeling for volatility jumps in the General Index of the ASE. The jump detection scheme allows, beyond testing for jumps, the extraction of both the jump and continuous components of volatility which are then used in modeling realized volatility with the class of Heterogeneous Autoregressive (HAR) models. This is the rst time, to the best of my knowledge, that volatility jumps are examined and modeled for the GD of the ASE, using a variety of realized volatility estimators. |
| Keywords: | Athens Stock Exchange, range-based volatility, optimal sampling, heterogeneous autoregres- sive models, realized volatility, volatility jumps. |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:00043&r=mst |
| By: | Mikhail Golosov; Guido Lorenzoni; Aleh Tsyvinski |
| Abstract: | The paper studies asset pricing in informationally decentralized markets. These markets have two key frictions: trading is decentralized (bilateral), and some agents have private information. We analyze how uninformed agents acquire information over time from their bilateral trades. In particular, we show that uninformed agents can learn all the useful information in the long run and that the long-run allocation is Pareto efficient. We then explore how informed agents can exploit their informational advantage in the short run and provide sufficient conditions for the value of information to be positive. Finally, we provide a numerical analysis of the equilibrium trading dynamics and prices. |
| JEL: | D82 D84 G12 G14 |
| Date: | 2009–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:15513&r=mst |
| By: | Piotr Korczak; Kate Phylaktis |
| Abstract: | In this paper we explore how the composition of a market maker's portfolio and allocation of attention across securities in the portfolio affect pricing. We analyze whether more attention devoted to similar securities enables a market maker to extract information relevant to a stock from order flow to related securities and consequently whether it leads to improved price discovery of the stock. We base on the recent literature on allocation of attention in share trading (Corwin and Coughenour, 2008; Boulatov et al., 2009) and define the prominence of a security as the proportion of its dollar volume in the total volume of the specialist portfolio it belongs to. Our empirical tests are focused on New York Stock Exchange specialists and the U.S. share in price discovery of 64 British and French companies cross-listed on the NYSE. We define related securities as stocks from the same country, the same region or other foreign stocks. We find strong evidence that an increase in the prominence of related stocks in the specialist portfolio leads to a higher U.S. share in price discovery of our sample stocks. We interpret our findings as evidence that concentrating market makers in similar stocks reduces information asymmetries and improves the information environment. To support our argument, we show that an increase in the prominence of other foreign stocks in the specialist portfolio significantly reduces the adverse selection component of the bid-ask spread. |
| Keywords: | NYSE specialists, cross-listing, related stocks, price discovery |
| JEL: | G14 G15 |
| Date: | 2009–10 |
| URL: | http://d.repec.org/n?u=RePEc:bri:uobdis:09/612&r=mst |
| By: | Adriana Korczak; Piotr Korczak; Meziane Lasfer |
| Abstract: | We argue that insiders' decisions to trade in short windows before news announcements are likely to result from a trade-off between the incentives to capitalize on the foreknowledge of the disclosure and the risk of regulatory scrutiny and lost reputation. We provide evidence that insider buying is driven by the trade-off, while selling is primarily influenced by the deterring effect of the regulatory and reputation risks. We show that insiders strategically choose the amount of shares bought ahead of good news announcements. They increase their purchases as the price impact of the news goes up, but we find that the amount of shares purchased levels off as the news becomes extreme. In contrast, we find that the probability of insider selling significantly decreases with the price impact of the forthcoming bad news. To further support our arguments on the importance of incentives and disincentives to trade, we show that the strategic trading is mainly observed in the most price-sensitive groups of news announcements, it is clearly pronounced for best informed executives (CEOs), and that trading patterns change with changes in regulations, and insiders with higher reputation at risk limit their trading ahead of bad news. |
| Keywords: | insider trading, private information, information disclosure, regulation |
| JEL: | G14 G18 K22 |
| Date: | 2009–10 |
| URL: | http://d.repec.org/n?u=RePEc:bri:uobdis:09/613&r=mst |