Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl     

CGE assumptions under scrutiny: uncertainty in the ethanol promotion policy in Mexico

Based on a CGE exercise of a subsidy to initiate ethanol production in Mexico, we use Monte Carlo simulations for consumer demand elasticities and ethanol cost estimates. The analysis provides three conclusions: when markets vary smoothly and predictably, Monte Carlo methods can then help to gauge the actual probability that a given program will achieve a desired outcome. Second, secondary markets may display little or no sensitivity to these parameter variations. Finally, a ‘razor’s edge’ outcome with no positive benefits if a critical parameter falls below some critical value, reveals that an economic policy may not be conducive to ‘fine tuning’ by marginal adjustments.     

A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

We show that, for three common SARV models, fitting a minimummean square linear filter is equivalent to fitting a GARCH model.This suggests that GARCH models may be useful for filtering,forecasting, and parameter estimation in stochastic volatilitysettings. To investigate, we use simulations to evaluate howthe three SARV models and their associated GARCH filters performunder controlled conditions and then we use daily currency andequity index returns to evaluate how the models perform in arisk management application. Although the GARCH models produceless precise forecasts than the SARV models in the simulations,it is not clear that the performance differences are large enoughto be economically meaningful. Consistent with this view, wefind that the GARCH and SARV models perform comparably in testsof conditional value-at-risk estimates using the actual data.     

Assessing the Least Squares Monte-Carlo Approach to American Option Valuation

A detailed analysis of the Least Squares Monte-Carlo (LSM) approach to American option valuation suggested in Longstaff and Schwartz (2001) is performed. We compare the specification of the cross-sectional regressions with Laguerre polynomials used in Longstaff and Schwartz (2001) with alternative specifications and show that some of these have numerically better properties. Furthermore, each of these specifications leads to a trade-off between the time used to calculate a price and the precision of that price. Comparing the method-specific trade-offs reveals that a modified specification using ordinary monomials is preferred over the specification based on Laguerre polynomials. Next, we generalize the pricing problem by considering options on multiple assets and we show that the LSM method can be implemented easily for dimensions as high as ten or more. Furthermore, we show that the LSM method is computationally more efficient than existing numerical methods. In particular, when the number of assets is high, say five, Finite Difference methods are infeasible, and we show that our modified LSM method is superior to the Binomial Model.     

On the source of contrarian and momentum strategies in the Italian equity market

This paper investigates the source of momentum profits, while inferring the validity of the assumptions underlying rational and behavioural theories. Using a unique sample of securities listed in the Italian Stock Exchange from 1950 to 1995, we observe that buying better performing stocks in the previous 3-12 months and selling worse performing stocks over the same period yields significant profits in the short term (less than 1 year). Results also hold when conditioned upon different risk specifications. On the other hand, the continuation effect seems to significantly revert over a longer period. More importantly, in contrast with Conrad and Kaul [Rev. Financ. Stud. 11 (1998) 489], bootstrap and Monte Carlo simulations show that momentum profits are more likely to be generated by stock returns time series properties rather than by their cross-sectional differences. While the overall findings cannot reject the market efficiency hypothesis, we argue that behavioural theory may be a possible “story” to interpret the continuation effect.     

Moments of renewal shot-noise processes and their applications

In this paper, we study the family of renewal shot-noise processes. The Feynmann–Kac formula is obtained based on the piecewise deterministic Markov process theory and the martingale methodology. We then derive the Laplace transforms of the conditional moments and asymptotic moments of the processes. In general, by inverting the Laplace transforms, the asymptotic moments and the first conditional moments can be derived explicitly; however, other conditional moments may need to be estimated numerically. As an example, we develop a very efficient and general algorithm of Monte Carlo exact simulation for estimating the second conditional moments. The results can be then easily transformed to the counterparts of discounted aggregate claims for insurance applications, and we apply the first two conditional moments for the actuarial net premium calculation. Similarly, they can also be applied to credit risk and reliability modelling. Numerical examples with four distribution choices for interarrival times are provided to illustrate how the models can be implemented.     

Export Sophistication: A Dynamic Panel Data Approach

In this article, we analyze export sophistication based on a large panel dataset (2001–2015; 101 countries) and using various estimation algorithms. Using Monte Carlo simulations, we evaluate the bias properties of estimators and show that GMM-type estimators outperform instrumental-variable and fixed-effects estimators. Based on our analysis we document that GDP per capita and the size of the economy exhibit significant and positive effects on export sophistication; weak institutional quality exhibits negative effect. We also show that export sophistication is path-dependent and stable even during a major economic crisis, which is especially important for emerging and developing economies.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Enders-Granger方法在协整检验中的应用研究

本文将协整检验由传统的线性协整检验扩展到线性协整检验和阈值协整检验,并在Enders和Granger(1998)方法的基础上提出了一个新的检验协整是否存在的Sup-F和Sup-F*统计量。通过MC仿真研究发现:在线性协整下,ADF方法比Sup-F法具有更高的检验势,但在"持久性"较强时,Sup-F检验比ADF检验法具有更高的检验势;Sup-F统计量在Three-Regime的阈值协整检验中比ADF法有更高的检验势;Sup-F*在检验协整(包括线性协整和阈值协整)时都具有较低的检验势;随着在不同Regime中自回归系数差距的增大(非对称程度增大),sup-F统计量的检验势提高很快,且比ADF法的检验势高。     

Scaledt分布、杠杆效应和上证综指的VaR风险

与正态分布相比,上证指数收益率的经验分布具有尖峰厚尾特征,但用Scaled t-分布比正态分布可以更好地拟合上证指数收益率的经验分布。本文以Scaled t-分布假设下的GJR模型为基础,测量了上证指数收益率波动性的杠杆效应,即信息对波动性的不对称影响：并根据GJR模型应用Monte Carlo模拟方法,测定上证指数日收益率和持有期收益率的风险价值（VaR）。根据GJR模型提供的结果,上证指数30天、60天和90天持有期收益率的风险值分别为12．1％、17．8％、22．0％。用GJR模型比均值-方差模型和历史模拟方法计算的5％显著性水平VaR值更接近实际收益率。