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on Econometric Time Series |
| By: | Laurent Schoeffel (CEA Saclay) |
| Abstract: | The probability distribution of log-returns for financial time series, sampled at high frequency, is the basis for any further developments in quantitative finance. In this letter, we present experimental results based on a large set of time series on futures. We show that the t-distribution with $\nu \simeq 3$ gives a nice description of almost all data series considered for a time scale $\Delta t$ below 1 hour. For $\Delta t \ge 8$ hours, the Gaussian regime is reached. A particular focus has been put on the DAX and Euro futures. This appears to be a quite general result that stays robust on a large set of futures, but not on any data sets. In this sense, this is not universal. A technique using factorial moments defined on a sequence of returns is described and similar results for time scales are obtained. Let us note that from a fundamental point of view, there is no clear reason why DAX and Euro futures should present similar behavior with respect to their return distributions. Both are complex markets where many internal and external factors interact at each instant to determine the transaction price. These factors are certainly different for an index on a change parity (Euro) and an index on stocks (DAX). Thus, this is striking that we can identify universal statistical features in price fluctuations of these markets. This is really the advantage of micro-structure analysis to prompt unified approaches of different kinds of markets. Finally, we examine the relation of power law distribution of returns with another scaling behavior of the data encoded into the Hurst exponent. We have obtained $H=0.54 \pm 0.04$ for DAX and $H=0.51 \pm 0.03$ for Euro futures. |
| Date: | 2011–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1110.1727&r=ets |
| By: | Giacomini, Raffaella; Ragusa, Giuseppe |
| Abstract: | We propose a method for modifying a given density forecast in a way that incorporates the information contained in theory-based moment conditions. An example is "improving" the forecasts from atheoretical econometric models, such as factor models or Bayesian VARs, by ensuring that they satisfy theoretical restrictions given for example by Euler equations or Taylor rules. The method yields a new density (and thus point-) forecast which has a simple and convenient analytical expression and which by construction satisfies the theoretical restrictions. The method is flexible and can be used in the realistic situation in which economic theory does not specify a likelihood for the variables of interest, and thus cannot be readily used for forecasting. |
| Keywords: | Bayesian VAR; Euler conditions; Exponential tilting; Forecast comparisons |
| JEL: | C53 |
| Date: | 2011–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:8604&r=ets |
| By: | Lyócsa, Štefan; Výrost, Tomáš; Baumöhl, Eduard |
| Abstract: | Using weekly returns of S&P 500 constituents, we study the time-varying correlation structure during the period of 2006 to mid-2011. Contrary to most of the previous correlation studies of many assets, we do not use rolling correlations but the DCC MV-GARCH model with the MacGyver strategy proposed by Engle (2009). We find empirical evidence that the correlation structure tends to change significantly during the periods of high volatility and market downturns. |
| Keywords: | correlation structure; dynamic conditional correlations; range-based volatility; conditional volatility; MacGyver strategy |
| JEL: | C32 G1 |
| Date: | 2011–10–17 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:34160&r=ets |
| By: | Julio, Juan Manuel |
| Abstract: | A closed formula for the Hodrick & Prescott, HP, filter subject to linear restrictions is derived. This filter is also known as the HP filter with priors. When the formula is applied to the ordinary HP filter linear restrictions apply only within the sample. However, when this formula is applied to the extended HP filter and extensions that correct for GDP revisions and delays, linear restrictions apply out of sample also. |
| Keywords: | Business Cycles, Hodrick-Prescott Filter |
| JEL: | E32 C22 |
| Date: | 2011–10–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:34202&r=ets |