A two factor long memory stochastic volatility model

In this paper we fit the main features of financial returns by means of a two factor long memory stochastic volatility model (2FLMSV). Volatility, which is not observable, is explained by both a short-run and a long-run factor. The first factor follows a stationary AR(1) process whereas the second one, whose purpose is to fit the persistence of volatility observable in data, is a fractional integrated process as proposed by Breidt et al. (1998) and Harvey (1998). We show formally that this model (1) creates more kurtosis than the long memory stochastic volatility (LMSV) of Breidt et al. (1998) and Harvey (1998), (2) deals with volatility persistence and (3) produces small first order autocorrelations of squared observations. In the empirical analysis, we use the estimation procedure of Gallant and Tauchen (1996), the Efficient Method of Moments (EMM), and we provide evidence that our specification performs better than the LMSV model in capturing the empirical facts of data

Parameter estimation for a subcritical affine two factor model

For an affine two factor model, we study the asymptotic properties of the maximum likelihood and least squares estimators of some appearing parameters in the so-called subcritical (ergodic) case based on continuous time observations. We prove strong consistency and asymptotic normality of the estimators in question.Comment: 31 pages. Title is changed. Extended version: new parameters are estimated and an Appendix is adde

Evidence for the reliability and validity, and some support for the practical utility of the two-factor Consideration of Future Consequences Scale-14

Researchers have proposed 1-factor, 2-factor, and bifactor solutions to the 12-item Consideration of Future Consequences Scale (CFCS-12). In order to overcome some measurement problems and to create a robust and conceptually useful two-factor scale the CFCS-12 was recently modified to include two new items and to become the CFCS-14. Using a University sample, we tested four competing models for the CFCS-14: (a) a 12-item unidimensional model, (b) a model fitted for two uncorrelated factors (CFC-Immediate and CFC-Future), (c) a model fitted for two correlated factors (CFC-I and CFC-F), and (d) a bifactor model. Results suggested that the addition of the two new items has strengthened the viability of a two factor solution of the CFCS-14. Results of linear regression models suggest that the CFC-F factor is redundant. Further studies using alcohol and mental health indicators are required to test this redundancy

Stationarity and ergodicity for an affine two factor model

We study the existence of a unique stationary distribution and ergodicity for a 2-dimensional affine process. The first coordinate is supposed to be a so-called alpha-root process with \alpha\in(1,2]. The existence of a unique stationary distribution for the affine process is proved in case of \alpha\in(1,2]; further, in case of \alpha=2, the ergodicity is also shown.Comment: 28 pages; the title has been changed; a mistake in the proof of Theorem 4.1 has been correcte

The Impact of Stochastic Convenience Yield on Long-term Forestry Investment Decisions

This paper investigates whether convenience yield is an important factor in determining optimal decisions for a forestry investment. The Kalman filter method is used to estimate three different models of lumber prices: a mean reverting model, a simple geometric Brownian motion and the two-factor price model due to Schwartz (1997). In the latter model there are two correlated stochastic factors: spot price and convenience yield. The two-factor model is shown to provide a reasonable fit of the term structure of lumber futures prices. The impact of convenience yield on a forestry investment decision is examined using the Schwartz (1997) long-term model which transforms the two-factor price model into a single factor model with a composite price. Using the long-term model an optimal harvesting problem is analyzed, which requires the numerical solution of an impulse control problem formulated as a Hamilton-Jacobi-Bellman Variational Inequality. We compare the results for the long-term model to those from single-factor mean reverting and geometric Brownian motion models. The inclusion of convenience yield through the long-term model is found to have a significant impact on land value and optimal harvesting decisions.