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on Econometric Time Series |
| 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=ets |
| 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=ets |
| 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=ets |
| By: | Legerstee, R.; Franses, Ph.H.B.F. |
| Abstract: | Forecasts from various experts are often used in macroeconomic forecasting models. Usually the focus is on the mean or median of the survey data. In the present study we adopt a different perspective on the survey data as we examine the predictive power of disagreement amongst forecasters. The premise is that this variable could signal upcoming structural or temporal changes in an economic process or in the predictive power of the survey forecasts. In our empirical work, we examine a variety of macroeconomic variables, and we use different measurements for the degree of disagreement, together with measures for location of the survey data and autoregressive components. Forecasts from simple linear models and forecasts from Markov regime-switching models with constant and with time-varying transition probabilities are constructed in real-time and compared on forecast accuracy. We find that disagreement has predictive power indeed and that this variable can be used to improve forecasts when used in Markov regime-switching models. |
| Keywords: | model forecasts;expert forecasts;survey forecasts;Markov regime-switching models;disagreement;time series |
| Date: | 2010–09–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureir:1765020744&r=ets |
| By: | Ma, Jun (Department of Economics, Finance and Legal Studies, University of Alabama); Nelson, Charles R. (Department of Economics, University of Washington) |
| Keywords: | ARMA, unobserved components, state space, GARCH, zero-information-limit-condition |
| JEL: | C12 C22 C33 |
| Date: | 2010–09 |
| URL: | http://d.repec.org/n?u=RePEc:ihs:ihsesp:256&r=ets |
| By: | Tsyplakov, Alexander |
| Abstract: | This essay is aimed to provide a straightforward and sufficiently accessible demonstration of some known procedures for stochastic volatility model. It reviews the important related concepts, gives informal derivations of the methods and can be useful as a cookbook for a novice. The exposition is confined to classical (non-Bayesian) framework and discrete-time formulations. |
| Keywords: | stochastic volatility |
| JEL: | C13 C53 C15 C22 |
| Date: | 2010–09–28 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:25511&r=ets |