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The aim of this paper is to measure and assess the accuracy of different volatility estimators based on high frequency data in an option pricing context. For this, we use a discrete-time stochastic volatility model based on Auto-Regressive-Gamma (ARG) dynamics for the volatility. First, ARG processes are presented both under historical and risk-neutral measure, in an affine stochastic discount factor framework. The model parameters are estimated exploiting the informative content of historical high frequency data. Secondly, option pricing is performed via Monte Carlo techniques. This framework allows us to measure the quality of different volatility estimators in terms of mispricing with respect to real option data, leaving to the ARG volatility model the role of a tool. Our analysis points out that using high frequency intra-day returns allows to obtain more accurate ex post estimation of the true (unobservable) return variation than do the more traditional sample variances based on daily returns, and this is reflected in the quality of pricing. Moreover, estimators robust to microstructure effects show an improvement over the realized volatility estimator. The empirical analysis is conducted on European options written on S&P500 index.

We investigate the modeling and forecasting of the intra-daily volume time series in Chinese stock market with an application to dynamic Volume Weighted Average Price (VWAP) method. The empirical results show that: (1) This method performs better than the traditional static VWAP strategy; (2) By adjusting time scale (time window) and the composition of the stock portfolio according to the principal component analysis method, we can further improve the forecasting accuracy of the stock turnover series; (3) There is significant long memory characteristic in the special component of the turnover series when using the dynamic VWAP method, however, we find that it can not improve the prediction of turnover series by using ARFIMA model on these series. We also analyze the reasons and provide some explanations.

This paper studies the dynamic implications of preferences for wealth habit in a one-sector optimal growth model. We show that the dynamics may encounter saddle-node bifurcations with respect to the parameters of the preferences: the relative weight of wealth in utility and the degree of wealth habit. We analytically provide the monotone comparative statics and the continuity of the critical capital stock with respect to the discount factor, the relative weight of wealth in utility and the degree of wealth habit.

To investigate time heterogeneity in the effects of fiscal policy in the US, we use a non-recursive, Blanchard and Perotti-like structural VAR with time-varying parameters, estimated through Bayesian simulation over 1965:2–2009:2. Our evidence suggests that fiscal policy has lost some capacity to stimulate output but this trend is more pronounced for taxes net of transfers than for government expenditure, whose effectiveness declines only slightly. Fiscal multipliers keep conventional signs throughout. An investigation of changes in fiscal policy conduct indicates an increase in the countercyclical responsiveness of net taxes over recent decades, which appears to have reached a maximum during the 2008–2009 recession.

We investigate the income convergence hypothesis for 24 OECD countries using Fourier-type unit root tests that can account for structural breaks of unknown number, timing and functional form in a series’ data generating process. Our results indicate that all 24 countries have followed a nonlinear underlying growth path (relative to the US) over the post-war era. These growth paths indicate that as many as half of the countries sampled are systematically catching-up with the US. Only a handful show evidence of being in relative decline, but the fact that this group includes several G7 economies is of concern.

This paper assesses the macroeconomic impact of fiscal policy shocks for four key emerging market economies – Brazil, Russia, India and China (BRICs) – using a fully simultaneous system of equations. We also estimate fiscal policy rules and analyze the importance of nonlinearity using a smooth transition (STR) model. Drawing on quarterly data, we find that government spending shocks have strong Keynesian effects for this group of countries while, in the case of government revenue shocks, a tax hike is harmful for output. This suggests that there is no evidence in favor of “expansionary fiscal contraction” in the context of emerging economies where spending policies are largely pro-cyclical. Our findings also show that considerations about growth (in the case of China), exchange rate and inflation (for Brazil and Russia) and commodity prices (in India) drive the nonlinear response of fiscal policy to the dynamics of the economy.