Volume 29, Issue 2

We propose a multiscale version of the seemingly unrelated regressions model, based on wavelet transform-based time series observations. Each regression equation refers to a different time scale, which enables the use of across-scale error covariances in the feasible GLS estimation procedure for efficiency gains. We demonstrate the advantages of the proposed method over OLS with two studies: an empirical study using stock market returns for the main US industrial sectors and a detailed Monte Carlo simulation study with alternative wavelet filters. We also provide explanations for the suitability of the proposed method for estimating long-term systematic risk.

We investigate the behaviour of the maximum likelihood estimator (MLE) for stochastic volatility jump-diffusion models commonly used in financial risk management. A simulation study shows the practical conditions under which the MLE behaves according to theory. In an extensive empirical study based on nine indices and more than 6000 individual stocks, we nonetheless find that the MLE is unable to replicate key higher moments. We then introduce a moment-targeted MLE – robust to model misspecification – and revisit both simulation and empirical studies. We find it performs better than the MLE, improving the management of financial risk.

We use a score-driven minimum mean-squared error (MSE) signal extraction method and perform inflation smoothing for China and the ASEAN-10 countries. Our focus on China and ASEAN-10 countries is motivated by the significant historical variation in inflation rates, e.g. during the 1997 Asian Financial Crisis, the 2007–2008 Financial Crisis, the COVID-19 Pandemic, and the Russian Invasion of Ukraine. Some advantages of the score-driven signal extraction method are that it uses dynamic mean and volatility filters, it considers stationary or non-stationary mean dynamics, it is computationally fast, it is robust to extreme observations, it uses information-theoretically optimal updating mechanisms for both mean and volatility, it uses closed-form formulas for smoothed signals, and parameters are estimated by using the maximum likelihood (ML) method for which the asymptotic properties of estimates are known. In the empirical application, we present the political and economic conditions for each country and analyze the evolution and determinants of the core inflation rate.

We introduce diversified risk parity embedded with various reward-risk measures and more generic allocation rules for portfolio construction. We empirically test the proposed reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. The reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The diversified reward-risk parity allocations gain superior performance in the Carhart four-factor analysis.

We build the time-varying parameter feature into the (Sims, E., J. C. Wu, and J. Zhang. 2023. “The Four-Equation New Keynesian Model.” The Review of Economics and Statistics 105 (4): 931–47) four-equation Dynamic Stochastic General Equilibrium (DSGE) model in this paper. We find that both parameters and impulse responses of the variables in the four-equation DSGE model exhibit significant variation over time. Allowing model parameters to vary over time also improves the model’s forecasting performance.

A common thread in the literature shows that an oil price shock can have a major impact on global economic conditions. We examine the global dimensions of changes to the global oil price and world economic uncertainty using three model types: ordinary least square (OLS); general additive model (GAM); and non-linear vector autoregression (VAR) model with local projections (LP). Our study highlights a positive and statistically significant effect of oil prices on economic uncertainty during non-expansionary periods, yet the impact is negative on economic uncertainty during periods of economic growth. Using a VAR-LP we analyze the global dimensions of a world oil price shock on global economic conditions and investigate whether there is consistency in how an oil price shock influences economic growth, consumer prices and economic uncertainty based on the state of economic conditions. The empirical evidence shows that during an expansionary (a non-expansionary) period, the impact of an oil price shock lowers (elevates) economic uncertainty. The empirical evidence from the three model types taken together indicate a presence of state dependence on the influence of an oil price shock.