Operations Research
http://lists.repec.org/mailman/listinfo/nep-ore
Operations Research
2018-11-19
Stochastic model specification in Markov switching vector error correction models
http://d.repec.org/n?u=RePEc:ris:sbgwpe:2018_003&r=ore
This paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinkage priors are used. These shrinkage priors enable to assess which coefficients differ across regimes in a flexible manner. In the case of similar coefficients, our model pushes the respective regions of the parameter space towards the common distribution. This allows for selecting a parsimonious model while still maintaining sufficient flexibility to control for sudden shifts in the parameters, if necessary. In the empirical application, we apply our modeling approach to Euro area data and assume that transition probabilities between expansion and recession regimes are driven by the cointegration errors. Our findings suggest that lagged cointegration errors have predictive power for regime shifts and these movements between business cycle stages are mostly driven by differences in error variances.
Huber, Florian
Pfarrhofer, Michael
Zörner, Thomas O.
Non-linear vector error correction model; Markov switching; hierarchical modeling; variable selection; equilibrium credit level; Euro area
2018-11-01
Asymptotic Theory for Rotated Multivariate GARCH Models
http://d.repec.org/n?u=RePEc:ems:eureir:111553&r=ore
In this paper, we derive the statistical properties of a two step approach to estimating multivariate GARCH rotated BEKK (RBEKK) models. By the denition of rotated BEKK, we estimate the unconditional covariance matrix in the rst step in order to rotate observed variables to have the identity matrix for its sample covariance matrix. In the second step, we estimate the remaining parameters via maximizing the quasi-likelihood function. For this two step quasi-maximum likelihood (2sQML) estimator, we show consistency and asymptotic normality under weak conditions. While second-order moments are needed for consistency of the estimated unconditional covariance matrix, the existence of nite sixth-order moments are required for convergence of the second-order derivatives of the quasi-log-likelihood function. We also show the relationship of the asymptotic distributions of the 2sQML estimator for the RBEKK model and the variance targeting (VT) QML estimator for the VT-BEKK model. Monte Carlo experiments show that the bias of the 2sQML estimator is negligible, and that the appropriateness of the diagonal specication depends on the closeness to either of the Diagonal BEKK and the Diagonal RBEKK models.
Asai, M.
Chang, C-L.
McAleer, M.J.
Pauwels, L.
BEKK, Rotated BEKK, Diagonal BEKK, Variance targeting, Multivariate GARCH, Consistency, Asymptotic normality
2018-10-01
Empirical Evaluation of Overspecified Asset Pricing Models
http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2017_1711&r=ore
Asset pricing models with potentially too many risk factors are increasingly common in empirical work. Unfortunately, they can yield misleading statistical inferences. Unlike other studies focusing on the properties of standard estimators and tests, we estimate the sets of SDFs and risk prices compatible with the asset pricing restrictions of a given model. We also propose tests to detect problematic situations with economically meaningless SDFs uncorrelated to the test assets. We confirm the empirical relevance of our proposed estimators and tests with Yogo's (2006) linearized version of the consumption CAPM, and provide Monte Carlo evidence on their reliability in finite samples.
Elena Manresa
Francisco Peñaranda
Enrique Sentana
Continuously updated GMM, factor pricing models, set estimation, stochastic discount factor, underidentification tests.
2017-05
Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models
http://d.repec.org/n?u=RePEc:ris:sbgwpe:2018_005&r=ore
We propose a straightforward algorithm to estimate large Bayesian time-varying parameter vector autoregressions with mixture innovation components for each coefficient in the system. The computational burden becomes manageable by approximating the mixture indicators driving the time-variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. Two applications illustrate the merits of our approach. First, we forecast the US term structure of interest rates and demonstrate forecast gains relative to benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time-varying effects of a monetary policy tightening
Huber, Florian
Kastner, Gregor
Feldkircher, Martin
Time-varying parameter vector autoregression with stochastic volatility (TVP-VARSV); Change point model; Structural breaks; Term structure of interest rates; Monetary policy; R package threshtvp
2018-11-07
Value-at-Risk prediction using option-implied risk measures
http://d.repec.org/n?u=RePEc:dnb:dnbwpp:613&r=ore
This paper investigates the prediction of Value-at-Risk (VaR) using option-implied information obtained by the maximum entropy method. The maximum entropy method provides an estimate of the risk-neutral distribution based on option prices. Besides commonly used implied volatility, we obtain implied skewness, kurtosis and quantile from the estimated risk-neutral distribution. We find that using the implied volatility and implied quantile as explanatory variables significantly outperforms considered benchmarks in predicting the VaR, including the commonly used GARCH(1,1)-model. This holds for all considered VaR prediction models and VaR probability levels. Overall, a simple quantile regression model performs best for all considered VaR probability levels and forecast horizons.
Kai Schindelhauer
Chen Zhou
Implied Quantile; GARCH; Quantile Regression; Comparative Backtest
2018-10
Long Run Returns Predictability and Volatility with Moving Averages
http://d.repec.org/n?u=RePEc:ems:eureir:111556&r=ore
The paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affect financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.
Chang, C-L.
Ilomäki, J.
Laurila, H.
McAleer, M.J.
Trading strategies, Risk, Moving average, Market timing, Returns predictability, Volatility, Rolling window, Data frequency
2018-09-01