Operations Research
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Operations Research2014-04-11Walter FrischWavelet improvement in turning point detection using a Hidden Markov Model
http://d.repec.org/n?u=RePEc:hhs:nhhfms:2014_010&r=ore
The Hidden Markov Model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (1989). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. We apply a Monte Carlo experiment to show that the detection accuracy of HMMs is highly improved when combined with the wavelet approach. Further simulations demonstrate the excellent accuracy of this improved HMM method relative to another two change point detection algorithms. Two empirical examples illustrate how the wavelet method can be applied to improve turning point detection in practice.Li, Yushu, Reese, Simon2014-03-25HMM; turning point; wavelet; wavelet power spectrum; outlierAssessing Point Forecast Accuracy by Stochastic Divergence from Zero
http://d.repec.org/n?u=RePEc:pen:papers:14-011&r=ore
We propose and explore several related ways of reducing reliance of point forecast accuracy evaluation on expected loss, E(L(e)), where e is forecast error. Our central approach dispenses with the loss function entirely, instead using a \stochastic error divergence" (SED) accuracy measure based directly on the forecast-error c.d.f., F(e). We explore several variations on the basic theme; interestingly, all point to the primacy of absolute-error loss and its generalizations.Francis X. Diebold, Minchul Shin2014-03-20Forecast accuracy, forecast evaluation, absolute-error loss, quadratic loss, squared-error lossA Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting
http://d.repec.org/n?u=RePEc:hhs:nhhfms:2014_012&r=ore
In this paper, we propose a likelihood ratio and Markov chain based method to evaluate density forecasting. This method can jointly evaluate the unconditional forecasted distribution and dependence of the outcomes. This method is an extension of the widely applied evaluation method for interval forecasting proposed by Christoffersen (1998). It is also a more refined approach than the pure contingency table based density forecasting method in Wallis (2003). We show that our method has very high power against incorrect forecasting distributions and dependence. Moreover, the straightforwardness and ease of application of this joint test provide a high potentiality for further applications in both financial and economical areas.Li, Yushu, Andersson, Jonas2014-03-25Likelihood ratio test; Markov Chain; Density forecastingForecasting Co-Volatilities via Factor Models with
Asymmetry and Long Memory in Realized Covariance
http://d.repec.org/n?u=RePEc:ucm:doicae:1405&r=ore
Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the onditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model. Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSV models outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten-day horizons in the periods before, during and after the global financial crisis.Manabu Asai, Michael McAleer2014-03Dimension reduction; Factor Model; Multivariate Stochastic Volatility; Leverage
Eﬀects; Long Memory; Realized Volatility.Stochastic Stability in Assignment Problems
http://d.repec.org/n?u=RePEc:lau:crdeep:14.02&r=ore
In a dynamic model of assignment problems, small deviations suffice to move between stable outcomes. This result is used to obtain no-selection and almost-no-selection results under the stochastic stability concept for uniform and payoff-dependent errors. There is no-selection of partner or payoff under uniform errors, nor for agents with multiple optimal partners under payoff-dependent errors. There can be selection of payoff for agents with a unique optimal partner under payoff-dependent errors. However, when every agent has a unique optimal partner, almost-no-selection is obtained.Bettina Klaus and, Jonathan Newton2014-04Assignment problem; (core) stability; decentralization; stochastic stabilityFund Managers Fees: Estimation and Sensitivity Analysis Using Monte Carlo Simulation
http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-195&r=ore
Fund managers compensation is a particular problem area in terms of its tax treatment in the United States and some European countries. This problem originates in the difficulty of defining these particular forms of incentive and therefore their estimated fair value. Based on the literature, carried interest, which is one of the most common profit-sharing arrangements observed in practice, may be considered as an option characterized by several constraints. The use of classical option-pricing models is inappropriate to take into account all these constraints. In this paper, we build a model to estimate the expected revenue to managers as a function of their investor contracts and we test how this estimated revenue varies across the characteristics of funds. We used the Monte Carlo simulation model and we introduced the non-marketability discount of the carried interest in order to calculate its fair value. A sensitivity analysis is performed in order to show the change in the fair value of carried interest after the change of each criterion. We find sharp differences between venture capital (VC) and buyout (BO) funds and between “deal by deal funds” and “whole funds”.Dorra Najar2014-02-25private equity; venture capital; managerial compensation; simulations