Operations Research
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Operations Research2015-01-19Walter FrischPoisson qmle of count time series models
http://d.repec.org/n?u=RePEc:pra:mprapa:59804&r=ore
Regularity conditions are given for the consistency of the Poisson quasi-maximum likelihood estimator of the conditional mean parameter of a count time series. The asymptotic distribution of the estimator is studied when the parameter belongs to the interior of the parameter space and when it lies at the boundary. Tests for the significance of the parameters and for constant conditional mean are deduced. Applications to specific INAR and INGARCH models are considered. Numerical illustrations, on Monte Carlo simulations and real data series, are provided.Ahmad, Ali, Francq, Christian2014-11Boundary of the parameter space; Consistency and asymptotic normality; Integer-valued AR and GARCH models; Non-normal asymptotic distribution; Poisson quasi-maximum likelihood estimator; Time series of counts.On Bias in the Estimation of Structural Break Points
http://d.repec.org/n?u=RePEc:siu:wpaper:22-2014&r=ore
Based on the Girsanov theorem, this paper obtains the exact finite sample distribution of the maximum likelihood estimator of structural break points in a continuous time model. The exact finite sample theory suggests that, in empirically realistic situations, there is a strong finite sample bias in the estimator of structural break points. This property is shared by least squares estimator of both the absolute structural break point and the fractional structural break point in discrete time models. A simulation-based method based on the indirect estimation approach is proposed to reduce the bias both in continuous time and discrete time models. Monte Carlo studies show that the indirect estimation method achieves substantial bias reductions. However, since the binding function has a slope less than one, the variance of the indirect estimator is larger than that of the original estimator.Liang Jiang, Xiaohu Wang, Jun Yu2014-12Structural change, Bias reduction, Indirect estimation, Break pointAnalogy Making and the Structure of Implied Volatility Skew
http://d.repec.org/n?u=RePEc:pra:mprapa:60921&r=ore
An analogy based call option pricing model is put forward. The model provides a new explanation for the implied volatility skew puzzle. The analogy model is consistent with empirical findings about returns from well studied option strategies such as covered call writing and zero-beta straddles. The analogy based stochastic volatility and the analogy based jump diffusion models are also put forward. The analogy based stochastic volatility model generates the skew even when there is no correlation between the stock price and volatility processes, whereas, the analogy based jump diffusion model does not require asymmetric jumps for generating the skew.Siddiqi, Hammad2014-10-01Implied Volatility Skew, Implied Volatility Smile, Analogy Making, Stochastic Volatility, Jump Diffusion, Covered Call Writing, Zero-Beta StraddleRange-based Volatility Estimation and Forecasting
http://d.repec.org/n?u=RePEc:fau:wpaper:wp2014_34&r=ore
In this paper, we analyze new possibilities in predicting daily ranges, i.e. differences between daily high and low prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges and explore the use of these more efficient volatility measures as predictors of daily ranges. The array of models used in this paper include the heterogeneous autoregressive model, conditional autoregressive ranges model and a vector error-correction model of daily highs and lows. Contrary to intuition, models based on co-integration of daily highs and lows fail to produce good quality out of sample forecasts of daily ranges. The best one-day-ahead daily ranges forecasts are produced by a realized range based HAR model with a GARCH volatility-of-volatility component.Daniel Bencik2014-12volatility, returns, futures contracts, cointegration, predictionBayesian Estimation for Partially Linear Models with an Application to Household Gasoline Consumption
http://d.repec.org/n?u=RePEc:msh:ebswps:2014-28&r=ore
A partially linear model is often estimated in a two-stage procedure, which involves estimating the nonlinear component conditional on initially estimated linear coefficients. We propose a sampling procedure that aims to simultaneously estimate the linear coefficients and bandwidths involved in the Nadaraya-Watson estimator of the nonlinear component. The performance of this sampling procedure is demonstrated through Monte Carlo simulation studies. The proposed sampling algorithm is applied to partially linear models of gasoline consumption based on the US household survey data. In contrary to implausible price effect reported in the literature, we find negative price effect on household gasoline consumption.Haotian Chen, Xibin Zhang2014backfitting least squares, bandwidth, household income, price elasticity, profile least squares, random-walk Metropolis