Operations Research
http://lists.repec.orgmailman/listinfo/nep-ore
Operations Research
2017-01-15
Higher-Order Risk Measure and (Higher-Order) Stochastic Dominance
http://d.repec.org/n?u=RePEc:pra:mprapa:75948&r=ore
This paper extends the theory between Kappa ratio and stochastic dominance (SD) and risk-seeking SD (RSD) by establishing several relationships between first- and higher-order risk measures and (higher-order) SD and RSD. We first show the sufficient relationship between the (n+1)-order SD and the n-order Kappa ratio. We then find that, in general, the necessary relationship between SD/RSD and the Kappa ratio cannot be established. Thereafter, we find that when the variables being compared belong to the same location-scale family or the same linear combination of location-scale families, we can get the necessary relationships between the (n+1)-order SD with the n-order Kappa ratio when we impose some conditions on the means. Our findings enable academics and practitioners to draw better decision in their analysis.
Niu, Cuizhen
Wong, Wing-Keung
Xu, Qunfang
Stochastic Dominance, Kappa ratio, Omega Ratio, Sortino ratio, mean-risk analysis, risk aversion, risk seeking
2017-01-03
Identification-robust moment-based tests for Markov-switching in autoregressive models
http://d.repec.org/n?u=RePEc:cir:cirwor:2016s-63&r=ore
This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison to the optimal tests for Markov-switching parameters of Carrasco et al. (2014) and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of U.S. output growth.
Jean-Marie Dufour
Richard Luger
Mixture distributions; Markov chains; Regime switching; Parametric bootstrap; Monte Carlo tests; Exact inference,
2016-12-31
A Stochastic Multi-stage Trading Cost model in optimal portfolio selection
http://d.repec.org/n?u=RePEc:eei:rpaper:eeri_rp_2016_23&r=ore
We propose a multi-stage stochastic trading cost model in optimal portfolio selection. This strategy captures uncertainty in implicit transaction costs incurred by an investor during initial trading and in subsequent rebalancing of the portfolio. We assume that implicit costs are stochastic as are asset returns. We use mean absolute deviation as our risk and apply the model to securities on the Johannesburg Stock Market. The model generates optimal portfolios by minimizing total implicit transaction costs incurred. It provides least-cost optimal portfolios whose net wealths are better than those gener- ated by the mean-variance, minimax and mean absolute deviation models.
Sabastine Mushori
Delson Chikobvu
implicit transaction costs, stochastic programming.
2016-11-23
Forecasting labour supply and population: an integrated stochastic model
http://d.repec.org/n?u=RePEc:iab:iabdpa:201701&r=ore
"This paper presents a stochastic integrated model to forecast the German population and labour supply until 2060. Within a cohort-component approach, the population forecast applies principal components to birth, mortality, emigration and immigration rates. The labour force participation rates are estimated by means of an econometric time series approach. All time series are forecast by bootstrapping. This allows fully integrated simulations and the possibility to illustrate the uncertainties in the form of confidence intervals. Our new forecast confirms the results from former studies. We conclude that even rising birth rates and high levels of immigration cannot break the basic demographic trend in the long run." (Author's abstract, IAB-Doku) ((en))
Fuchs, Johann
Söhnlein, Doris
Weber, Brigitte
Weber, Enzo
2017-01-03
Truncated sum of squares estimation of fractional time series models with deterministic trends
http://d.repec.org/n?u=RePEc:qed:wpaper:1376&r=ore
We consider truncated (or conditional) sum of squares estimation of a parametric model composed of a fractional time series and an additive generalized polynomial trend. Both the memory parameter, which characterizes the behaviour of the stochastic component of the model, and the exponent parameter, which drives the shape of the deterministic component, are considered not only unknown real numbers, but also lying in arbitrarily large (but finite) intervals. Thus, our model captures different forms of nonstationarity and noninvertibility. As in related settings, the proof of consistency (which is a prerequisite for proving asymptotic normality) is challenging due to non-uniform convergence of the objective function over a large admissible parameter space, but, in addition, our framework is substantially more involved due to the competition between stochastic and deterministic components. We establish consistency and asymptotic normality under quite general circumstances, finding that results differ crucially depending on the relative strength of the deterministic and stochastic components.
Javier Hualde
Morten Ørregaard Nielsen
Asymptotic normality, consistency, deterministic trend, fractional process, generalized polynomial trend, noninvertibility, nonstationarity, truncated sum of squares estimation
2017-01
Changes in Persistence in Outlier Contaminated Time Series
http://d.repec.org/n?u=RePEc:han:dpaper:dp-583&r=ore
Outlying observations in time series influence parameter estimation and testing procedures, leading to biased estimates and spurious test decisions. Further inference based on these results will be misleading. In this paper the effects of outliers on the performance of ratio-based tests for a change in persistence are investigated. We consider two types of outliers, additive outliers and innovative outliers. Our simulation results show that the effect of outliers crucially depends on the outlier type and on the degree of persistence of the underlying process. Additive outliers deteriorate the performance of the tests for high degrees of persistence. In contrast, innovative outliers do not negatively influence the performance of the tests. Since additive outliers lead to severe size distortions when the null hypothesis under consideration is described by a nonstationary process, we apply an outlier detection method designed for unit-root testing. The adjustment of the series results in size improvements and power gains. In an empirical example we apply the tests and the outlier detection method to the G7 inflation rates.
Hirsch, Tristan
Rinke, Saskia
Additive Outliers; Innovative Outliers; Change in Persistence; Outlier Detection; Monte Carlo
2017-01