Operations Research
http://lists.repec.orgmailman/listinfo/nep-ore
Operations Research
2016-06-25
Semiparametric Efficient Adaptive Estimation of the PTTGARCH model
http://d.repec.org/n?u=RePEc:pra:mprapa:72021&r=ore
Financial data sets exhibit conditional heteroskedasticity and asymmetric volatility. In this paper we derive a semiparametric efficient adaptive estimator of a conditional heteroskedasticity and asymmetric volatility GARCH-type model (i.e., the PTTGARCH(1,1) model). Via kernel density estimation of the unknown density function of the innovation and via the Newton-Raphson technique applied on the root-n-consistent quasi-maximum likelihood estimator, we construct a more efficient estimator than the quasi-maximum likelihood estimator. Through Monte Carlo simulations, we show that the semiparametric estimator is adaptive for parameters in- cluded in the conditional variance of the model with respect to the unknown distribution of the innovation.
Ciccarelli, Nicola
Semiparametric adaptive estimation; Power-transformed and threshold GARCH.
2016
On the use of high frequency measures of volatility in MIDAS regressions
http://d.repec.org/n?u=RePEc:cpr:ceprdp:11307&r=ore
Many empirical studies link mixed data frequency variables such as low frequency macroeconomic or Â…nancial variables with high frequency Â…financial indicatorsÂ’ volatilities, especially within a predictive regression model context. The objective of this paper is threefold: First, we relate the standard Least Squares (LS) regression model with high frequency volatility predictors, with the corresponding Mixed Data Sampling Nonlinear LS (MIDAS-NLS) regression model (Ghysels et al., 2005, 2006), and evaluate the properties of the regression estimators of these models. We also consider alternative high frequency volatility measures as well as various continuous time models using their corresponding relevant higher-order moments to further analyze the properties of these estimators. Second, we derive the relative MSE efficiency of the slope estimator in the standard LS and MIDAS regressions, we provide conditions for relative efficiency and present the numerical results for different continuous time models. Third, we extend the analysis of the bias of the slope estimator in standard LS regressions with alternative realized measures of risk such as the Realized Covariance, Realized Beta and the Realized Skewness when the true DGP is a MIDAS model.
Andreou, Elena
bias; efficiency; high-frequency volatility estimators; MIDAS regression model
2016-06
Optimal data collection for randomized control trials
http://d.repec.org/n?u=RePEc:ifs:cemmap:15/16&r=ore
In a randomized control trial, the precision of an average treatment e ffect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected. Our procedure seeks to minimize the resulting average treatment e ect estimator's mean squared error, subject to the researcher's budget constraint. We rely on an orthogonal greedy algorithm that is conceptually simple, easy to implement (even when the number of potential covariates is very large), and does not require any tuning parameters. In two empirical applications, we show that our procedure can lead to substantial gains of up to 58%, either in terms of reductions in data collection costs or in terms of improvements in the precision of the treatment eff ect estimator, respectively. The original version of the working paper, posted on 01 April, 2016, is available here.
Pedro Carneiro
Sokbae Lee
Daniel Wilhelm
randomized control trials, big data, data collection, optimal surveydesign, orthogonal greedy algorithm, survey costs.
2016-04-01
FORECAST COMBINATIONS FOR REALIZED VOLATILITY IN PRESENCE OF STRUCTURAL BREAKS
http://d.repec.org/n?u=RePEc:rtr:wpaper:0208&r=ore
In this paper the problem of instability due to changes in the parameters of some Realized Volatility (RV) models has been addressed. The analysis is based on 5-minute RV of four U.S. stock market indices. Three different representations of the log-RV have been considered and, for each of them, the parameter instability has been detected by using the recursive estimates test. In order to analyse how instabilities in the parameters affect the forecasting performance, an out-of-sample forecasting exercise has been performed. In particular, several forecast combinations, designed to accommodate potential structural breaks, have been considered. All of them are based on different estimation windows, with alternative weighting schemes, and do not take into account explicitly estimated break dates. The model con_dence set has been used to compare the forecasting performances of the proposed approaches. Our analysis gives empirical evidences of the effectiveness of the combinations which make adjustments for accounting the possible most recent break point.
Davide De Gaetano
Forecast combinations, Structural breaks, Realized volatility
2016-06
No Man is an Island : the Impact of Heterogeneity and Local Interactions on Macroeconomic Dynamics
http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/20d1ncsepb9ssq3b3v4s6nbc41&r=ore
We develop an agent-based model in which heterogeneous firms and households interact in labor and good markets according to centralized or decentralized search and matching protocols. As the model has a deterministic backbone and a full-employment equilibrium, it can be directly compared to Dynamic Stochastic General Equilibrium (DSGE) models. We study the effects of negative productivity shocks by way of impulse-response func- tions (IRF). Simulation results show that when search and matching are centralized, the economy is always able to return to the full employment equilibrium and IRFs are similar to those generated by DSGE models. However, when search and matching are local, co- ordination failures emerge and the economy persistently deviates from full employment. Moreover, agents display persistent heterogeneity. Our results suggest that macroeco- nomic models should explicitly account for agents’ heterogeneity and direct interactions
Mattia GUERINI
Mauro Napoletano
Andrea Roventini
Agent-based model; Local interactions; Heterogenous agents; DGSE Model
2016-06