Operations Research
http://lists.repec.org/mailman/listinfo/nep-ore
Operations Research2014-08-25Walter FrischModeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts
http://d.repec.org/n?u=RePEc:usg:econwp:2014:25&r=ore
A high frequency stochastic volatility (SV) model is proposed. Price duration and associated absolute price change in event time are modeled contemporaneously to fully capture volatility on the tick level, combining the SV and stochastic conditional duration (SCD) model. Estimation is with IBM stock intraday data 2001/10 (decimalization completed), taking a minimum midprice threshold of a half tick. Persistent information flow is extracted, featuring a positively correlated innovation term and negative cross effects in the AR(1) persistence matrix. Additionally, regime switching in both duration and absolute price change is introduced to increase nonlinear capabilities of the model. Thereby, a separate price jump state is identified. Model selection and predictive tests show superiority of the regime switching extension in- and out-of-sample.Trojan, Sebastian2014-08Stochastic volatility, stochastic conditional duration, non-Gaussian and nonlinear state space model, tick data, event time, generalized gamma distribution, negative binomial distribution, regime switching, Markov chain Monte Carlo, block sampler, particle filter, adaptive MetropolisDirichlet Process Hidden Markov Multiple Change-point Model
http://d.repec.org/n?u=RePEc:pra:mprapa:57871&r=ore
This paper proposes a new Bayesian multiple change-point model which is based on the hidden Markov approach. The Dirichlet process hidden Markov model does not require the specification of the number of change-points a priori. Hence our model is robust to model specification in contrast to the fully parametric Bayesian model. We propose a general Markov chain Monte Carlo algorithm which only needs to sample the states around change-points. Simulations for a normal mean-shift model with known and unknown variance demonstrate advantages of our approach. Two applications, namely the coal-mining disaster data and the real US GDP growth, are provided. We detect a single change-point for both the disaster data and US GDP growth. All the change-point locations and posterior inferences of the two applications are in line with existing methods.Ko, Stanley I. M., Chong, Terence T. L., Ghosh, Pulak2014-08-07Change-point; Dirichlet process; Hidden Markov model; Markov chain; Monte Carlo; Nonparametric Bayesian.A Monte Carlo Method using PDE Expansions for a Diversifed Equity Index Model
http://d.repec.org/n?u=RePEc:uts:rpaper:350&r=ore
This paper considers a new class of Monte Carlo methods that are combined with PDE expansions for the pricing and hedging of derivative securities for multidimensional diffusion models. The proposed method combines the advantages of both PDE and Monte Carlo methods and can be directly applied to models with more than two state variables. The pricing procedure is illustrated using a three-component index model that captures some of the key features of a diversied stock index over long time periods. The method is widely applicable and is demonstrated here in the general setting of the benchmark approach, where spatial boundary limiting conditions for the PDE need to be appropriately chosen and approximated. The PDE expansion is based on a Taylor series approximation for the underlying three-component PDE. A Monte Carlo method with variance reduction is then formulated to approximate the true solution. Almost exact simulation schemes are described for the given state variables in the model. Numerical results are presented that demonstrate the effectiveness and tractability of the proposed pricing and hedging methodology.David Heath, Eckhard Platen2014-08-01Multi-factor diffusion; Monte Carlo methods; diversified equity index, pricing PDE; exact simulation; variance reduction; benchmark approachShrinkage Estimation of Regression Models with Multiple Structural Changes
http://d.repec.org/n?u=RePEc:siu:wpaper:06-2014&r=ore
In this paper we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso (least absolute shrinkage and selection operator ). We show that with probability tending to one our method can correctly determine the unknown number of breaks and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a datadriven method to determine the tuning parameter. Monte Carlo simulations demonstrate that the proposed method works well in finite samples. We illustrate the use of our method with a predictive regression of the equity premium on fundamental information.Junhui Qian, Liangjun Su2014-08Change point; Fused Lasso; Group Lasso; Penalized least squares; Structural changeOutlier Detection in Structural Time Series Models: the Indicator Saturation Approach
http://d.repec.org/n?u=RePEc:aah:create:2014-20&r=ore
Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse–and step–indicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries.Martyna Marczak, Tommaso Proietti2014-11-08Indicator saturation, seasonal adjustment, structural time series model, outliers, structural change, general–to–specific approach, state space modelEstimation of Nonseparable Models with Censored Dependent Variables and Endogenous Regressors.
http://d.repec.org/n?u=RePEc:cep:stiecm:/2014/575&r=ore
In this paper we develop a nonparametric estimator for the local average response of a censored dependent variable to endogenous regressors in a nonseparable model where the unobservable error term is not restricted to be scalar and where the nonseparable function need not be monotone in the unobservables. We formalise the identification argument put forward in Altonji, Ichimura and Otsu (2012), construct the nonparametric estimator, characterise its asymptotic property, and conduct a Monte Carlo investigation to study the small sample properties. Identification is constructive and is achieved through a control function approach. We show that the estimator is consistent and asymptotically normally distributed. The Monte Carlo results are encouraging.Taisuke Otsu, Luke Taylor2014-08Rig services and taxation
http://d.repec.org/n?u=RePEc:hhs:stavef:2014_007&r=ore
A long period of rig scarcity and high rates has led to innovation in the procurement of rig services and in relationships between oil companies and rig contractors. Discussions have been conducted on joint ventures between companies and contractors, for instance. This paper describes and analyses such a solution from a taxation perspective. Could a joint venture pose problems for revenue capture from the petroleum sector? Challenges in taxing drilling services - including recently adopted British restrictions on determining internal charter rates for drilling units - are also analysed. In addition to analysing topical issues related to taxation and rigs, the paper makes a general contribution by highlighting the connection between taxing rig services at oil-company and rig-contractor levels, and by placing rig taxation in a broader resource management perspective.Osmundsen, Petter2014-08-01Climate Projects; Decision Analysis; CO2Finite Element Model of the Innovation Diffusion: An Application to Photovoltaic Systems
http://d.repec.org/n?u=RePEc:hhs:kthind:2014_006&r=ore
This paper presents a Finite Element Model, which has been used for forecasting the diffusion of innovations in time and space. Unlike conventional models used in diffusion literature, the model considers spatial heterogeneity. The implementation steps of the model are explained by applying it to the case of diffusion of photovoltaic systems in a local region in southern Germany. The applied model is based on a parabolic partial differential equation that describes the diffusion ratio of photovoltaic systems in a given region over time. The results of the application suggest that the Finite Element Model could be a powerful tool to improve our understanding on the diffusion of innovations as a simultaneous space-time process. For future research, model limitations and possible extensions are also discussed.Karakaya, Emrah2014-07-24Innovation; Adoption; Spatiotemporal; prediction; solar photovoltaicsA Consistent Framework for Modelling Basis Spreads in Tenor Swaps
http://d.repec.org/n?u=RePEc:uts:rpaper:348&r=ore
The phenomenon of the frequency basis (i.e. a spread applied to one leg of a swap to exchange one
oating interest rate for another of a different tenor in the same currency) contradicts textbook no-arbitrage conditions and has become an important feature of interest rate markets since the beginning of the Global Financial Crisis (GFC) in 2008. Empirically, the basis spread cannot be explained by transaction costs alone, and therefore must be due to a new perception by the market of risks involved in the execution of textbook "arbitrage" strategies. This has led practitioners to adopt a pragmatic "multi-curve" approach to interest rate modelling, which leads to a proliferation of term structures, one for each tenor. We take a more fundamental approach and explicitly model liquidity risk as the driver of basis spreads, reducing the dimensionality of the market for the frequency basis from observed spread term structures for every frequency pair down to term structures of two factors characterising liquidity risk. To this end, we use an intensity model to describe the arrival time of (possibly stochastic) liquidity shocks with a Cox Process. The model parameters are calibrated to quoted market data on basis spreads, and the improving stability of the calibration suggests that the basis swap market has matured since the turmoil of the GFC.Yang Chang, Erik Schlogl2014-05-01tenor swap; basis; frequency basis; liquidity risk; swap marketExponential Smoothing, Long Memory and Volatility Prediction
http://d.repec.org/n?u=RePEc:rtv:ceisrp:319&r=ore
Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing (ES) is a very popular and successful forecasting and signal extraction scheme, but it can be suboptimal for long memory time series. This paper discusses possible long memory extensions of ES and finally implements a generalization based on a fractional equal root integrated moving average (FerIMA) model, proposed originally by Hosking in his seminal 1981 article on fractional differencing. We provide a decomposition of the process into the sum of fractional noise processes with decreasing orders of integration, encompassing simple and double exponential smoothing, and introduce a lowpass real time filter arising in the long memory case. Signal extraction and prediction depend on two parameters: the memory (fractional integration) parameter and a mean reversion parameter. They can be estimated by pseudo maximum likelihood in the frequency domain. We then address the prediction of volatility by a FerIMA model and carry out a recursive forecasting experiment, which proves that the proposed generalized exponential smoothing predictor improves significantly upon commonly used methods for forecasting realized volatility.Tommaso Proietti2014-07-30Realized Volatility. Signal Extraction. Permanent-Transitory Decomposition. Fractional equal-root IMA modelSimple Agents, Intelligent Markets
http://d.repec.org/n?u=RePEc:cwl:cwldpp:1868r&r=ore
Attainment of rational expectations equilibria in asset markets calls for the price system to disseminate agents’ private information to others. Markets populated by human agents are known to be capable of converging to rational expectations equilibria. This paper reports comparable market outcomes when human agents are replaced by boundedly-rational algorithmic agents who use a simple means-end heuristic. These algorithmic agents lack the capability to optimize; yet outcomes of markets populated by them converge near the equilibrium derived from optimization assumptions. These findings point to market structure (rather than cognition or optimization) being an important determinant of efficient aggregate level outcomes.Karim Jamal, Michael Maier, Shyam Sunder2012-07Bounded rationality, Dissemination of asymmetric information, Efficiency of security markets, Minimally-rational agents, Rational expectations, Structural properties of marketsStochastic Intensity Models of Wrong Way Risk: Wrong Way CVA Need Not Exceed Independent CVA
http://d.repec.org/n?u=RePEc:fip:fedgfe:2014-54&r=ore
Wrong way risk can be incorporated in Credit Value Adjustment (CVA) calculations in a reduced form model. Hull and White [2012] introduced a CVA model that captures wrong way risk by expressing the stochastic intensity of a counterparty's default time in terms of the financial institution's credit exposure to the counterparty. We consider a class of reduced form CVA models that includes the formulation of Hull and White and show that wrong way CVA need not exceed independent CVA. This result is based on some general properties of the model calibration scheme and a formula that we derive for intensity models of dependent CVA (wrong or right way). We support our result with a stylized analytical example as well as more realistic numerical examples based on the Hull and White model. We conclude with a discussion of the implications of our findings for Basel III CVA capital charges, which are predicated on the assumption that wrong way risk increases CVA.Ghamami, Samim, Goldberg, Lisa R.2014-07-30Credit value adjustment; stochastic intensity modeling; wrong way and right way risk; Basel III; counterparty credit riskReciprocity Networks and the Participation Problem
http://d.repec.org/n?u=RePEc:hhs:gunwpe:0603&r=ore
Reciprocity can be a powerful motivation for human behaviour. Scholars argue that it is relevant in the context of private provision of public goods. We examine whether reciprocity can resolve the associated coordination problem. The interaction of reciprocity with cost-sharing is critical. Neither cost-sharing nor reciprocity in isolation can solve the problem, but together they have that potential. We introduce new network notions of reciprocity relations to better understand this. Our analysis uncovers an intricate web of nuances that demonstrate the attainable yet elusive nature of a unique outcome.Dufwenberg, Martin, Patel, Amrish2014-08-13discrete public good; participation; reciprocity networks; coordination; cost-sharingReal Estate Markets and Uncertainty Shocks: A Variance Causality Approach
http://d.repec.org/n?u=RePEc:pre:wpaper:201436&r=ore
This paper investigates the impact of macroeconomic effects of uncertainty on the conditional volatility of US-listed Real Estate Investment Trusts (REITs). To this end we employ three widely accepted US REITs indices and the two uncertainty indices constructed by Baker et al. (2013). Our sample is extensive covering the period from 4th of January 1999 to 28th of June, 2013. We employ the recently developed test of causality in variance by Hafner and Herwarz (2006) and then we track the response of REITs conditional volatility to various key events as marked by the evolution of the uncertainty indices. Our results provide some useful insights for the causal nexus between real estate and macroeconomic environment. We provide evidence in favor of a two-way transmission channel between REITs conditional volatility and macroeconomic uncertainty. Moreover, equity REITs appear rather sensitive to deteriorating investors' sentiment. Our results entail policy implications for investors, regulators and monetary authorities.Ahdi N. Ajmi, Vassilios Babalos, Fotini Economou, Rangan Gupta2014-07Real estate investment trusts, uncertainty shocks, causality, volatility impulse response functionDesign of Supply Chain Networks with Supply Disruptions using Genetic Algorithm
http://d.repec.org/n?u=RePEc:pra:mprapa:58062&r=ore
The design of supply chain networks subject to disruptions is tackled. A genetic algorithm with the objective of minimizing the design cost and regret cost is developed to achieve a reliable supply chain network. The improvement of supply chain network reliability is measured against the supply chain cost.Taha, Raghda, Abdallah, Khaled, Sadek, Yomma, El-Kharbotly, Amin, Afia, Nahid2014-05-10Supply chain, Disruptions, Genetic AlgorithmA Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias
http://d.repec.org/n?u=RePEc:cbt:econwp:14/22&r=ore
This study uses Monte Carlo analysis to investigate the performances of five different meta-analysis (MA) estimators: the Fixed Effects (FE) estimator, the Weighted Least Squares (WLS) estimator, the Random Effects (RE) estimator, the Precision Effect Test (PET) estimator, and the Precision Effect Estimate with Standard Errors (PEESE) estimator. We consider two types of publication bias: publication bias directed against statistically insignificant estimates, and publication bias directed against wrong-signed estimates. Finally, we consider three cases concerning the distribution of the “true effect”: the Fixed Effects case, where there is only estimate per study, and all studies have the same true effect; the Random Effects case, where there is only one estimate per study, and there is heterogeneity in true effects across studies; and the Panel Random Effects case, where studies have multiple estimates, and true effects are random both across and within studies. Our simulations produce a number of findings that challenge results from previous research.W. Robert Reed, Raymond J.G.M. Florax, Jacques Poot2014-08-13Meta-analysis, Random effects, Fixed effects, publication bias, Monte Carlo, Simulations