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Regime Switching (regime + switching)
Selected AbstractsOPTIMAL FORECAST COMBINATION UNDER REGIME SWITCHING*INTERNATIONAL ECONOMIC REVIEW, Issue 4 2005Graham Elliott This article proposes a new forecast combination method that lets the combination weights be driven by regime switching in a latent state variable. An empirical application that combines forecasts from survey data and time series models finds that the proposed regime switching combination scheme performs well for a variety of macroeconomic variables. Monte Carlo simulations shed light on the type of data-generating processes for which the proposed combination method can be expected to perform better than a range of alternative combination schemes. Finally, we show how time variations in the combination weights arise when the target variable and the predictors share a common factor structure driven by a hidden Markov process. [source] A Markov regime switching approach for hedging stock indicesTHE JOURNAL OF FUTURES MARKETS, Issue 7 2004Amir Alizadeh In this paper we describe a new approach for determining time-varying minimum variance hedge ratio in stock index futures markets by using Markov Regime Switching (MRS) models. The rationale behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterized by regime shifts, which, in turn, suggests that by allowing the hedge ratio to be dependent upon the "state of the market," one may obtain more efficient hedge ratios and hence, superior hedging performance compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models such as GARCH, Error Correction and OLS in the FTSE 100 and S&P 500 markets. In and out-of-sample tests indicate that MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedging strategies only within sample. Overall, the results indicate that by using MRS models market agents may be able to increase the performance of their hedges, measured in terms of variance reduction and increase in their utility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:649,674, 2004 [source] ECONOMETRIC MODELS OF ASYMMETRIC PRICE TRANSMISSIONJOURNAL OF ECONOMIC SURVEYS, Issue 2 2007Giliola Frey Abstract In this paper, we review the existing empirical literature on price asymmetries in commodities, providing a way to classify and compare different studies that are highly heterogeneous in terms of econometric models, type of asymmetries and empirical findings. Relative to the previous literature, this paper is novel in several respects. First, it presents a detailed and updated survey of the existing empirical contributions on price asymmetries in the transmission mechanism linking input prices to output prices. Second, this paper presents an extension of the traditional distinction between long-run and short-run asymmetries to new categories of asymmetries, such as: contemporaneous impact, distributed lag effect, cumulated impact, reaction time, equilibrium and momentum equilibrium adjustment path, regime effect, regime equilibrium adjustment path. Each empirical study is then critically discussed in the light of this new classification of asymmetries. Third, this paper evaluates the relative merits of the most popular econometric models for price asymmetries, namely autoregressive distributed lags, partial adjustments, error correction models, regime switching and vector autoregressive models. Finally, we use the meta-regression analysis to investigate whether the results of asymmetry tests are not model-invariant and find which additional factors systematically influence the rejection of the null hypothesis of symmetric price adjustment. The main results of our survey can be summarized as follows: (i) each econometric model is specialized to capture a subset of asymmetries; (ii) each asymmetry is better investigated by a subset of econometric models; (iii) the general significance of the F test for asymmetric price transmission depends mainly on characteristics of the data, dynamic specification of the econometric model, and market characteristics. Overall, our empirical findings confirm that asymmetry, in all its forms, is very likely to occur in a wide range of markets and econometric models. [source] Forecast performance of nonlinear error-correction models with multiple regimesJOURNAL OF FORECASTING, Issue 2 2005Zacharias Psaradakis Abstract In this paper we investigate the forecast performance of nonlinear error-correction models with regime switching. In particular, we focus on threshold and Markov switching error-correction models, where adjustment towards long-run equilibrium is nonlinear and discontinuous. Our simulation study reveals that the gains from using a correctly specified nonlinear model can be considerable, especially if disequilibrium adjustment is strong and/or the magnitude of parameter changes is relatively large. Copyright © 2005 John Wiley & Sons, Ltd. [source] Price transmission in the Spanish bovine sector: the BSE effectAGRICULTURAL ECONOMICS, Issue 1 2010Islam Hassouneh Food scare; BSE crisis; Price transmission; Regime-switching Abstract A regime-switching vector error correction model is applied to monthly price data to assess the impact of Bovine Spongiform Encephalopathy (BSE) outbreaks on price relationships and patterns of transmission among farm and retail markets for bovines in Spain. To evaluate the degree to which price transmission is affected by BSE food scares, a BSE food scare index is developed and used to determine regime switching. Results suggest that BSE scares affect beef producers and retailers differently. Consumer prices are found to be weakly exogenous and not found to react to BSE scares, while producer prices are conversely adjusted. The magnitude of the adjustment is found to depend on the magnitude of the BSE scare. [source] Autoregressive processes with data-driven regime switchingJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2009Joseph Tadjuidje Kamgaing Abstract., We develop a switching-regime vector autoregressive model in which changes in regimes are governed by an underlying Markov process. In contrast to the typical hidden Markov approach, we allow the transition probabilities of the underlying Markov process to depend on past values of the time series and exogenous variables. Such processes have potential applications in finance and neuroscience. In the latter, the brain activity at time t (measured by electroencephalograms) will be modelled as a function of both its past values as well as exogenous variables (such as visual or somatosensory stimuli). In this article, we establish stationarity, geometric ergodicity and existence of moments for these processes under suitable conditions on the parameters of the model. Such properties are important for understanding the stability properties of the model as well as for deriving the asymptotic behaviour of various statistics and model parameter estimators. [source] Time Series Concepts for Conditional Distributions*OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2003Clive W. J. Granger Abstract The paper asks the question , as time series analysis moves from consideration of conditional mean values and variances to unconditional distributions, do some of the familiar concepts devised for the first two moments continue to be helpful in the more general area? Most seem to generalize fairly easy, such as the concepts of breaks, seasonality, trends and regime switching. Forecasting is more difficult, as forecasts become distributions, as do forecast errors. Persistence can be defined and also common factors by using the idea of a copula. Aggregation is more difficult but causality and controllability can be defined. The study of the time series of quantiles becomes more relevant. [source] Nonlinear Dynamic Relations Between Equity Return and Equity Fund Flow: Korean Market Empirical Evidence,ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 2 2010Sei-Wan Kim C12; C13; G14; G23 Abstract This research studies the dynamic relationship between equity returns and equity fund flows by incorporating nonlinear properties of the two variables. Nonlinear estimation based on a smooth transition autoregressive model reveals results different from those previously reported based on a linear relationship. Our empirical results find that there is significant mutual Granger causality between equity returns and equity fund flows. In addition, by introducing the dividend yield effect, significant Granger causality is also found between the three variables. This can be interpreted as meaning that demand for equities is downward sloping for both the price pressure effect and the information effect. Relatively fast regime switching and dynamic instability show that stock investment through equity funds is mostly short-horizon-oriented investment. [source] Multivariate Markov Switching Common Factor Models for the UKBULLETIN OF ECONOMIC RESEARCH, Issue 2 2003Terence C. Mills We estimate a model that incorporates two key features of business cycles, comovement among economic variables and switching between regimes of boom and slump, to quarterly UK data for the last four decades. A common factor, interpreted as a composite indicator of coincident variables, and estimates of turning points from one regime to the other, are extracted from the data by using the Kalman filter and maximum likelihood estimation. Both comovement and regime switching are found to be important features of the UK business cycle. The composite indicator produces a sensible representation of the cycle and the estimated turning points agree fairly well with independently determined chronologies. These estimates are sharper than those produced by a univariate Markov switching model of GDP alone. A fairly typical stylized fact of business cycles is confirmed by this model , recessions are steeper and shorter than recoveries. [source] | ||||||||||||||||||||||