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on Market Microstructure |
| By: | Roxana Halbleib (Department of Economics, University of Konstanz, Germany); Valeri Voev (School of Economics and Management, Aarhus University, Denmark) |
| Abstract: | In this paper we introduce a new method of forecasting covariance matrices of large dimensions by exploiting the theoretical and empirical potential of using mixed-frequency sampled data. The idea is to use high-frequency (intraday) data to model and forecast daily realized volatilities combined with low frequency (daily) data as input to the correlation model. The main theoretical contribution of the paper is to derive statistical and economic conditions, which ensure that a mixed-frequency forecast has a smaller mean squared forecast error than a similar pure low-frequency or pure high-frequency specification. The conditions are very general and do not rely on distributional assumptions of the forecasting errors or on a particular model specification. Moreover, we provide empirical evidence that, besides overcoming the computational burden of pure high-frequency specifications, the mixed-frequency forecasts are particularly useful in turbulent financial periods, such as the previous financial crisis and always outperforms the pure low-frequency specifications. |
| Keywords: | Multivariate volatility, Volatility forecasting, High-frequency data, Realized variance, Realized covariance |
| JEL: | C32 C53 |
| Date: | 2012–10–12 |
| URL: | http://d.repec.org/n?u=RePEc:knz:dpteco:1230&r=mst |
| By: | Anton Golub; John Keane; Ser-Huang Poon |
| Abstract: | We analyse all Mini Flash Crashes (or Flash Equity Failures) in the US equity markets in the four most volatile months during 2006-2011. In contrast to previous studies, we find that Mini Flash Crashes are the result of regulation framework and market fragmentation, in particular due to the aggressive use of Intermarket Sweep Orders and Regulation NMS protecting only Top of the Book. We find strong evidence that Mini Flash Crashes have an adverse impact on market liquidity and are associated with Fleeting Liquidity. |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1211.6667&r=mst |
| By: | Linda Ponta; Enrico Scalas; Marco Raberto; Silvano Cincotti |
| Abstract: | We study tick-by-tick financial returns belonging to the FTSE MIB index of the Italian Stock Exchange (Borsa Italiana). We find that non-stationarities detected in other markets in the past are still there. Moreover, scaling properties reported in the previous literature for other high-frequency financial data are approximately valid as well. Finally, we propose a simple method for describing non-stationary returns, based on a non-homogeneous normal compound Poisson process and we test this model against the empirical findings. It turns out that the model can reproduce several stylized facts of high-frequency financial time series. |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1212.0479&r=mst |
| By: | Avino, Davide; Lazar, Emese; Varotto, Simone |
| Abstract: | Credit spreads can be derived from the prices of securities traded in different markets. In this paper we investigate the price discovery process in single-name credit spreads obtained from bonds, credit default swaps, equities and equity options. Using a vector error correction model (VECM) of changes in credit spreads for a sample that includes the 2007-2009 financial crisis, we find that during periods of high volatility, price discovery takes place primarily in the option market, whilst the equity market leads the other markets during tranquil periods. By adding GARCH effects to the VECM specification, we also find strong evidence of volatility spillovers from the option market to the other markets in crisis periods. Finally, we show how time-varying measures of price discovery can be generated using GARCH models. |
| Keywords: | credit spreads; price discovery; volatility spillovers; CDS; information flow |
| JEL: | G14 G01 G20 |
| Date: | 2012–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:42847&r=mst |
| By: | Robert F. Engle (Stern School of Business, New York University); Martin Klint Hansen (Aarhus University and CREATES); Asger Lunde (Aarhus University and CREATES) |
| Abstract: | Starting with the advent of the event study methodology, the puzzle of how public information relates to changes in asset prices has unraveled gradually. Using a sample of 28 large US companies, we investigate how more than 3 million firm specific news items are related to firm specific stock return volatility. We specify a return generating process in conformance with the mixture of distributions hypothesis, where stock return volatility has a public and a private information processing component. Following public information arrival, prices incorporate public information contemporaneously while private processing of public information generates private information that is incorporated sequentially. We refer to this model as the information processing hypothesis of return volatility and test it using time series regression. Our results are evidence that public information arrival is related to increases in volatility and volatility clustering. Even so, clustering in public information does not fully explain volatility clustering. Instead, the presence of significant lagged public information effects suggest private information, generated following the arrival of public information, plays an important role. Including indicators of public information arrival explains an incremental 5 to 20 percent of variation in the changes of firm specific return volatility. Contrary to prior financial information research, our investigation favors the view that return volatility is related to public information arrival. |
| Keywords: | Firm Specific News, Realized Volatility, Public Information Arrival. |
| JEL: | G14 |
| Date: | 2012–12–04 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2012-56&r=mst |