Dueker M.J. (1997) Markov switching in GARCH processes and mean-reverting stockmarket volatility. Journal of Business and Economic Statistics, 15, 26-34. Home/Search Document Details and Download
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Understanding Interest Rate Volatility through.. - Dellaportas, Denison, .. (2001) (Correct)
....Stylised facts (Lamoureaux and Lastrapes, 1990) demonstrated the need for models that could also capture some form of volatility persistence or clustering. This led to a series of more sophisticated models that incorporated regime switches together with volatility persistence (see Gray (1996) Dueker (1997), Brenner et al. 1996) and Chung and Hung (2000) Regime switches in the volatility process could be interpreted as 2 volatility shocks occurring at unknown times. This approach has also led to the modelling of the volatility process via Levy processes (for a review see Barndor Nielsen and ....
Dueker M.J. (1997) Markov switching in GARCH processes and mean-reverting stockmarket volatility. Journal of Business and Economic Statistics, 15, 26-34.
Markov-Switching and Stochastic Volatility Diffusion Models of.. - Smith (2000) (Correct)
....are inherently path dependant. This is because the conditional variance today depends on the conditional variance yesterday that, in turn, depended on previous conditional variances. Thus the conditional variance today depends explicitly on all previous states. However, both Gray (1996) and Dueker (1997) have since developed two approximations that overcome the path dependence problem. 4 It is also possible to allow for time varying transition probabilities. For example, Durland and McCurdy (1994) allow the transition probabilities to decline as the economy remains in one regime. In other ....
Dueker, Michael J., 1997, Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility, Journal of Business and Economic Statistics 15, 26--35.
The Simulated Likelihood Ratio (SLR) Method - Billio, Monfort, Robert (1998) (Correct)
....60 . As far as the starting point is concerned, it is important to consider reasonable values of the parameters. It is possible to obtain a valuable starting point by considering the estimates of the parameter obtained with the approximation of the likelihood suggested by Kim [28] and Dueker [9]. 4.2. Dynamic Tobit models. We consider the following dynamic Tobit model: y t = ff fiy t Gamma1 oe t ; y t = max(y t ; 0) 41) True Starting value SLR estimate Standard deviation Mean square error ff 0.1 0.2 0.22443462 0.18812571 0.05087526 fi 0.5 0.2 0.59256369 ....
Dueker, M.J., Markov Switching in GARCH Processes and Mean-Reverting Stock Market Volatility, Journal of Business & Economic Statistics, 15/1, 26-34, 1997.
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