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Information plays an important role in decision-making process in oligopoly market. This paper establishes two Cournot duopoly games with information asymmetry based on extrapolative mechanism, and focus on the impacts of information asymmetry from the perspective of stability, complexity and profit. The results show that the extrapolative mechanism plays a different role for heterogeneous expectation firms. In general, proper prediction of rival’s output information is conducive to the improvement of firms’ profit and system stability. However, for firms adopting the adaptive adjustment mechanism, the optimal profit does not occur for the most perfect prediction. Firms who adopt gradient adjustment mechanism are worth to improve prediction accuracy to promote the system stability and the profit. In addition, it is more important for all firms to control their own output adjustment mechanism to gain more profits.

I study score-driven models for modelling high persistence dependence between financial volatility series. I model this persistence dependence with two components, one for the long memory and the other for the short-term process. The addition of components offers a parsimonious solution for modelling high persistence and also allows for a short-term component for the transient shocks. I apply the model to emerging equities in the Americas. The estimates are robust to the advent of the pandemic. In addition, data resampling and marginal alternatives deliver similar parameter estimates. The proposed two-component model improves the in-sample diagnostics and generates more accurate out-of-sample forecasts.

In this paper, we develop a new version of Rao’s score (RS) statistic for testing a non-linear hypothesis under both distributional and local parametric misspecification. Our suggested test statistic is constructed through a size correction approach so that it becomes robust to both types of misspecification. We establish the asymptotic properties of the robust test statistic and provide several examples to illustrate its implementation. We also investigate the finite sample properties of our test along with some other well-known tests through simulations. Our simulation results demonstrate that the new test statistic has good finite sample properties in terms of empirical size and power.

In the context that the tails of security returns obey an asymmetric power-law distribution, this paper constructs two fractal statistical measures based on fractal theory: fractal expectation and fractal variance. Subsequently, a new momentum strategy is constructed by introducing the fractal measures into the momentum strategy as measures of returns and risks to optimize the selection criterion. Finally, the empirical results show that the new momentum strategy outperforms the traditional momentum strategy and the risk-adjusted momentum strategy, confirming the effectiveness of fractal expectation and fractal variance.

Gold may have a hedge, safe haven, or diversifier property when added to stock portfolios. Motivated by the favorable statistical properties and out-of-sample performance of score-driven models, we investigate for equity-gold portfolios whether score-driven mean, volatility, and copula models can improve the performances of DCC (dynamic conditional correlation) portfolios, the naïve portfolio strategy, and the Standard & Poor’s 500 (S&P 500) index. We consider 2880 score-driven portfolio strategies. We use score-driven Clayton, rotated Clayton, Frank, Gaussian, Gumbel, rotated Gumbel, Plackett, and Student’s t copulas. We use several classical and score-driven models of marginal distribution. We use weekly, monthly, quarterly, semi-annual, and annual updates of portfolio weights. We use minimum-variance, maximum Sharpe ratio, and maximum utility function strategies. We use rolling data-windows for portfolio optimization for the COVID-19 investment period of February 2020 to September 2021. We classify competing portfolios by using a new robust multi-step model confidence set (MCS) test approach and provide evidence of the superiority of score-driven portfolios.

We propose a new state space model to estimate the Integrated Variance (IV) in the presence of microstructure noise. Applying the pre-averaging sampling scheme to the irregularly spaced high-frequency data, we derive equidistant efficient price approximations to calculate the noise-contaminated realised variance (NCRV), which is used as an IV estimator. The theoretical properties of the new volatility estimator are illustrated and compared with those of the realised volatility. We highlight the robustness of the new estimator to market microstructure noise (MMN). The pre-averaging sampling effectively eliminates the influence of the MMN component on the NCRV series. The empirical illustration features the EUR/USD exchange rate and provides evidence of a superior performance in volatility forecasting at very high sampling frequencies.