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on Market Microstructure |
| By: | Tim Bollerslev (Department of Economics, Duke University, and NBER and CREATES); Viktor Todorov (Department of Finance, Kellogg School of Management, Northwestern University) |
| Abstract: | We provide a new framework for estimating the systematic and idiosyncratic jump tail risks in financial asset prices. The theory underlying our estimates are based on in-fill asymptotic arguments for directly identifying the systematic and idiosyncratic jumps, together with conventional long-span asymptotics and Extreme Value Theory (EVT) approximations for consistently estimating the tail decay parameters and asymptotic tail dependencies. On implementing the new estimation procedures with a panel of highfrequency intraday prices for a large cross-section of individual stocks and the aggregate S&P 500 market portfolio, we find that the distributions of the systematic and idiosyncratic jumps are both generally heavy-tailed and not necessarily symmetric. Our estimates also point to the existence of strong dependencies between the market-wide jumps and the corresponding systematic jump tails for all of the stocks in the sample. We also show how the jump tail dependencies deduced from the high-frequency data together with the day-to-day temporal variation in the volatility are able to explain the “extreme” dependencies vis-a-vis the market portfolio. |
| Keywords: | Extreme events, jumps, high-frequency data, jump tails, non-parametric estimation, stochastic volatility, systematic risks, tail dependence. |
| JEL: | C13 C14 G10 G12 |
| Date: | 2010–09–10 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2010-64&r=mst |
| By: | Michael C. M\"unnix; Rudi Sch\"afer; Thomas Guhr |
| Abstract: | We present two statistical causes for the distortion of correlations on high-frequency financial data. We demonstrate that the asynchrony of trades as well as the decimalization of stock prices has a large impact on the decline of the correlation coefficients towards smaller return intervals (Epps effect). These distortions depend on the properties of the time series and are of purely statistical origin. We are able to present parameter-free compensation methods, which we validate in a model setup. Furthermore, the compensation methods are applied to high-frequency empirical data from the NYSE's TAQ database. A major fraction of the Epps effect can be compensated. The contribution of the presented causes is particularly high for stocks that are traded at low prices. |
| Date: | 2010–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1009.6157&r=mst |
| By: | Almut E. D. Veraart (CREATES, School of Economics and Management Aarhus University) |
| Abstract: | This paper studies the impact of jumps on volatility estimation and inference based on various realised variation measures such as realised variance, realised multipower variation and truncated realised multipower variation. We review the asymptotic theory of those realised variation measures and present a new estimator for the asymptotic ‘variance’ of the centered realised variance in the presence of jumps. Next, we compare the finite sample performance of the various estimators by means of detailed Monte Carlo studies where we study the impact of the jump activity, the jump size of the jumps in the price and the presence of additional independent or dependent jumps in the volatility on the finite sample performance of the various estimators. We find that the finite sample performance of realised variance, and in particular of the log–transformed realised variance, is generally good, whereas the jump–robust statistics turn out not to be as jump robust as the asymptotic theory would suggest in the presence of a highly active jump process. In an empirical study on high frequency data from the Standard & Poor’s Depository Receipt (SPY), we investigate the impact of jumps on inference on volatility by realised variance in practice. |
| Keywords: | Realised variance, realised multipower variation, truncated realised variance, inference, stochastic volatility, jumps, priceLength: 48 |
| JEL: | C10 C14 G10 |
| Date: | 2010–09–18 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2010-65&r=mst |
| By: | Nuno Cassola (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); Michael Huetl (University of St. Gallen, Swiss Institute of Banking and Finance, Rosenbergstrasse 52, 9000 St. Gallen, Switzerland.) |
| Abstract: | We analyze the impact of the recent financial market crisis on the Euro Overnight Index Average (EONIA) and interbank market trading and assess the effectiveness of the ECB liquidity policy between 07/2007 - 08/2008. We extend the model of [QM06] by (i) incorporating the microstructure of the EONIA market including the ECB fine-tuning operation on the last day of the maintenance period (MP) and banks’ daily excess liquidity, (ii) giving insight into banks’ trading behavior characterized by an endogenous regime-switch and suggesting an efficient procedure to simulate the entire MP, and (iii) proposing a model for market distortion due to lending constraints which lead to a bid-ask spread for the EONIA rate. The model is calibrated by simulation fitting daily EONIA rates and aggregate liquidity measures observed between March 2004 and September 2008. Besides lending constraints we consider market segmentation and aggregate liquidity shocks as possible market distortions in the crisis period. For a calibration cross-check and for estimating the timing of the endogenous regime-switch we use panel data covering liquidity data of 82 Euro Area commercial banks for the period 03/2003 - 07/2007. With the calibrated model the ECB policy of liquidity frontloading is evaluated and compared with a reserve band system policy similar to the Bank of England’s framework. We find that liquidity frontloading is a small scale central bank intervention which is capable of stabilizing interest rates in both frictionless and distorted markets. Simulations suggest that without frontloading the EONIA would have been, on average, 23 basis points above the policy rate (target); with frontloading, the overnight rate is, on average, on target. JEL Classification: E44, E52, G21. |
| Keywords: | liquidity management, open market operations, simulation, microstructure. |
| Date: | 2010–10 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20101247&r=mst |
| By: | Silvia Muzzioli |
| Abstract: | The aim of this paper is to analyse and empirically test how to unlock volatility information from option prices. The information content of three option based forecasts of volatility: Black-Scholes implied volatility, model-free implied volatility and corridor implied volatility is addressed, with the ultimate plan of proposing a new volatility index for the Italian stock market. As for model-free implied volatility, two different extrapolation techniques are implemented. As for corridor implied volatility, five different corridors are compared. Our results, which point to a better performance of corridor implied volatilities with respect to both Black-Scholes implied volatility and model-free implied volatility, are in favour of narrow corridors. The volatility index proposed is obtained with an overall 50% cut of the risk neutral distribution. The properties of the volatility index are explored by analysing both the contemporaneous relationship between implied volatility changes and market returns and the usefulness of the proposed index in forecasting future market returns. |
| Keywords: | volatility index; Black-Scholes implied volatility; model-free implied volatility; corridor implied volatility; implied binomial trees |
| JEL: | G13 G14 |
| Date: | 2010–09 |
| URL: | http://d.repec.org/n?u=RePEc:mod:wcefin:10091&r=mst |
| By: | Ole E. Barndorff-Nielsen (The T.N. Thiele Centre for Mathematics in Natural Science, Department of Mathematical Sciences, University of Aarhus, and CREATES); David G. Pollard (AHL Research, Man Research Laboratory); Neil Shephard (Oxford-Man Institute, University of Oxford) |
| Abstract: | Motivated by features of low latency data in financial econometrics we study in detail integervalued Lévy processes as the basis of price processes for high frequency econometrics. We propose using models built out of the difference of two subordinators. We apply these models in practice to low latency data for a variety of different types of futures contracts. |
| Keywords: | futures markets, high frequency econometrics, low latency data, negative binomial, Skellam, tempered stable |
| JEL: | C01 C14 C32 |
| Date: | 2010–09–23 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2010-66&r=mst |
| By: | Jayanaka Wijeratne; Shino Takayama (School of Economics, The University of Queensland) |
| Abstract: | We present a model in which there is uncertainty about realization of a risky asset value for an informed trader. Then, we show that the informed trader does not trade in equi- librium if the inside information the informed trader has is not sufficiently accurate. We use the framework presented by Glosten and Milgrom (1985) and extend the assumption that the informed trader knows the terminal value of the risky asset. Finally, we obtain the conditions under which the informed trader would not trade in equilibrium. |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:qld:uq2004:411&r=mst |