Operations Research
http://lists.repec.orgmailman/listinfo/nep-ore
Operations Research
2017-09-17
Construction and visualization of optimal confidence sets for frequentist distributional forecasts
http://d.repec.org/n?u=RePEc:msh:ebswps:2017-9&r=ore
The focus of this paper is on the quantification of sampling variation in frequentist probabilistic forecasts. We propose a method of constructing confidence sets that respects the functional nature of the forecast distribution, and use animated graphics to visualize the impact of parameter uncertainty on the location, dispersion and shape of the distribution. The confidence sets are derived via the inversion of a Wald test and are asymptotically uniformly most accurate and, hence, optimal in this sense. A wide range of linear and non-linear time series models - encompassing long memory, state space and mixture specifications - is used to demonstrate the procedure, based on artificially generated data. An empirical example in which distributional forecasts of both financial returns and its stochastic volatility are produced is then used to illustrate the practical importance of accommodating sampling variation in the manner proposed.
David Harris
Gael M. Martin
Indeewara Perera
Don S. Poskitt
probabilistic forecasts, asymptotically uniformly most accurate confidence regions, time series models, animated graphics, realized volatility, heterogeneous autoregressive model.
2017
Stationarity and Invertibility of a Dynamic Correlation Matrix
http://d.repec.org/n?u=RePEc:tin:wpaper:20170082&r=ore
One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators (QMLE). To date, the statistical properties of the QMLE of the DCC parameters have purportedly been derived under highly restrictive and unverifiable regularity conditions. The paper shows that the DCC model can be obtained from a vector random coefficient moving average process, and derives the stationarity and invertibility conditions of the DCC model. The derivation of DCC from a vector random coefficient moving average process raises three important issues, as follows: (i) demonstrates that DCC is, in fact, a dynamic conditional covariance model of the returns shocks rather than a dynamic conditional correlation model; (ii) provides the motivation, which is presently missing, for standardization of the conditional covariance model to obtain the conditional correlation model; and (iii) shows that the appropriate ARCH or GARCH model for DCC is based on the standardized shocks rather than the returns shocks. The derivation of the regularity conditions, especially stationarity and invertibility, should subsequently lead to a solid statistical foundation for the estimates of the DCC parameters. Several new results are also derived for univariate models, including a novel conditional volatility model expressed in terms of standardized shocks rather than returns shocks, as well as the associated stationarity and invertibility conditions.
Michael mcAleer
Dynamic conditional correlation; dynamic conditional covariance; vector random coefficient moving average; stationarity; invertibility; asymptotic properties.
2017-09-06
Optimal Congestion Pricing with Diverging Long-run and Short-run Scheduling Preferences
http://d.repec.org/n?u=RePEc:tin:wpaper:20170077&r=ore
Recent empirical work has suggested that there is an important distinction between short-run versus long-run scheduling behaviour of commuters, reflected in differences in values of time and schedule delays, as well as in preferred arrival moments, for the short-run versus the long-run problem. Peer et al. (2015) for example find that the average value of time when consumers form their routines in the long-run problem may exceed by a factor 6 the short-run value that governs departure time choice given these routines. For values of schedule delay, in contrast, the short-run value exceeds the long-run value, by a factor 2. And, when forming routines, consumers in fact choose a most preferred arrival time that may deviate from the value they would choose in absence of congestion because a change in routines may mean that shorter delays will be encountered. This paper investigates whether this distinction between short-run and long-run scheduling decisions affect optimal pricing of a congestible facility. Using a stochastic dynamic model of flow congestion for describing short-run equilibria and integrating it with a dynamic model of routine formation, it is found that consistent application of short-run first-best optimal congestion pricing does not optimally decentralize the optimal formation of routines in the long-run problem. A separate instrument, next to road pricing, is therefore needed to optimize routine formation.
Erik (E.T.) Verhoef
Congestion pricing; dynamic congestion; scheduling
2017-09-05
Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction
http://d.repec.org/n?u=RePEc:msh:ebswps:2017-13&r=ore
In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive regression model, in which the predictive variables are stochastically nonstationary. We also establish the estimation theory and asymptotic properties for these models in the short horizon and long horizon case. To evaluate the effectiveness of these models, we investigate their capability of stock return prediction. The empirical results show that all of these models can substantially outperform the traditional linear predictive regression model in terms of both in-sample and out-of-sample performance. In addition, we find that these models can always beat the historical mean model in terms of in-sample fitting, and also for some cases in terms of the out-of-sample forecasting.
Tingting Cheng
Jiti Gao
Oliver Linton
Kernel estimator, locally stationary process, series estimator, stock return prediction.
2017
Defaults, Normative Anchors and the Occurrence of Risky and Cautious Shifts
http://d.repec.org/n?u=RePEc:tin:wpaper:20170083&r=ore
Choice shifts occur when individuals advocate a risky (safe) decision when acting as part of a group even though they prefer a safe (risky) decision when acting as individuals. Even though research in psychology and economics has produced a mass of evidence on this puzzling phenomenon, there is no agreement about which mechanism produces choice shifts. In an experiment, we investigate the performance of two prominent mechanisms that have been proposed to explain the phenomenon; (i) rank-dependent utility and (ii) a desire to conform to the wishes of the majority. The evidence provides clear support for the conformity explanation.
Stephan Jagau
Theo (T.J.S.) Offerman
risky shift; cautious shift; conformity; diffusion of responsibility; rank-dependent utility
2017-09-06
Forecasting Market Risk of Portfolios: Copula-Markov Switching Multifractal Approach
http://d.repec.org/n?u=RePEc:cqe:wpaper:6617&r=ore
This paper proposes a new methodology for modeling and forecasting market risks of portfolios. It is based on a combination of copula functions and Markov switching multifractal (MSM) processes. We assess the performance of the copula-MSM model by computing the value at risk of a portfolio composed of the NASDAQ composite index and the S&P 500. Using the likelihood ratio (LR) test by Christofferrsen (1998), the GMM duration-based test by Candelon et al. (2011) and the superior predictive ability (SPA) test by Hansen (2005) we evaluate the predictive ability of the copula-MSM model and compare it to other common approaches such as historical simulation, variance-covariance, Risk-Metrics, copula-GARCH and constant conditional correlation GARCH (CCCGARCH) models. We find that the copula-MSM model is more robust, provides the best fit and outperforms the other models in terms of forecasting accuracy and VaR prediction.
Mawuli Segnon
Mark Trede
Copula, Multifractal processes, GARCH, VaR, Backtesting, SPA
2017-09