North American Journal of Economics and Finance最新文献

This study investigates the tail risk transmissions across a diverse range of US commodity & tech-driven sector ETFs and the underlying US stock market by employing the CAViaR-based TVP-VAR methodology on daily data from January 01, 2019, to August 17, 2023. Findings reveal that Covid-19 triggered a notable surge in the total connectedness, consequently amplifying the tail risk transmissions within the system. Moreover, the S&P 500, AI&Robotics and fintech sector ETFs stand out as the primary risk transmitters, while cybersecurity and blockchain sector ETFs are risk receivers within the system, except for a notable shift during the peak of the pandemic. The pairwise results reveal limited risk transmissions between the S&P 500, AI&Robotics and fintech sector ETFs; however, both sector ETFs stand out as potential risk transmitters for the VIX index. In contrast to energy, agriculture and base metals sector ETFs, which are persistent risk receivers for both stock market indices and tech-driven sector ETFs, precious metals sector ETFs appear somewhat isolated and therefore offer a potential source of diversification among commodity sector ETFs. In sum, our findings offer valuable sectoral insights for effective risk management and portfolio diversification strategies in dynamic market conditions.

This paper studies the pricing of discrete arithmetic Asian options (AAOs) with fixed strikes under the Hull–White interest rate model. For the pricing of AAOs, we first investigate the stochastic dynamics of the price of the underlying asset under the $T$-forward measure, and then study the distribution of the discrete arithmetic average of the underlying asset price. Specifically, we provide the first three moments of the discrete arithmetic average under the $T$-forward measure. Then, we derive approximate pricing formulas for AAOs using the three-moment matching method. Furthermore, we calculate the first three conditional moments of the discrete arithmetic average, given the final value of the underlying asset, under the $T$-forward measure. These conditional moments can be used to improve the accuracy of the approximation of the AAO prices. The numerical results show that our three-moment matching approximations are very accurate. Additionally, the accuracy can be further improved by combining the conditioning approach with the three-moment matching method. Our procedure is also applied to the computation of deltas of AAOs.

The determination of optimal exercise boundaries is a critical aspect of pricing American options, which often requires costly numerical methods. This paper proposes a new approach that employs multi-step rebate options to approximate American option prices. Since the rebate options offer payoffs when the multi-step boundaries are touched, the prices of American options are estimated by maximizing the multi-step rebate option prices, and the optimal multi-step barriers replace the true optimal exercise boundaries. To this end, the closed-form pricing formulas for multi-step rebate options are derived and utilized to approximate several American option prices. Through extensive numerical experiments, we demonstrate the validity and performance of our approach.

This manuscript addresses modeling mispricing risk of defined contribution pension plan (DCPP) with a mean–variance criterion to obtain the optimal investment strategy. Provides a way for the sustainability of pensions by investing in the financial market. The pension manager’s objective is to maximize the expected terminal wealth while simultaneously minimizing the associated risk. We employ the stochastic dynamic programming principle (SDPP) and the Lagrange dual theorem to derive the efficient frontier and strategy, then two special cases are examined. Last, we conduct a numerical analysis to show how different parameters influence the efficient frontier and strategy. This analysis sheds light on the economic implications of our findings.

In this paper, we study the pricing problems of basket options and spread options under the Normal Tempered Stable Lévy model, which is a general model for financial assets and covers many well-known models as special cases such as the Variance Gamma model, Normal Inverse Gaussian model etc. Our approach draws inspiration from the lower bound approximation strategy used in Gaussian models in Bjerksund and Stensland (2014). The approximation formula we derived involves some one-dimensional integrations. We calculate these integrals using the generalized Gauss–Laguerre quadrature rule and Taylor expansion methods. In particular, we derive an analytical approximation formula under the Variance Gamma model for some exchange options. Moreover, we extend the approximation formulas proposed by Kirk (1995) and Carmona and Durrleman (2003b) to the Normal Tempered Stable Lévy model. Numerical tests show that our approximation formulas are highly accurate. Furthermore, we show that our approximation formulas outperform the Fourier inversion method introduced by Caldana et al. (2016) in accuracy, especially for low prices cases.

An increasing number of investors are concerned about how they can diversify risks and profits amid surging geopolitical uncertainties. Using a geopolitical risk timing/hedging model, we investigate whether hedge fund managers can effectively hedge or time geopolitical risks by adopting different trading strategies. We find that excluding those in the global macro category, hedge funds with higher minimum investments and management fees exhibit greater success in hedging geopolitical risks. Meanwhile, global macro hedge funds, which have longer lockup periods, are more adept at timing geopolitical risks by increasing their market exposures. Furthermore, hedge funds which are the top geopolitical risk hedgers and timers demonstrate higher economic value than those in the bottom group over the subsequent one and three months. Our findings provide valuable insights into private investors’ selection of hedge funds during periods of heightened geopolitical risk.

Environmental pollution has had a negative impact on the population’s well-being, impeding the pursuit of a better standard of living. This study seeks to investigate the impact of environmental decentralization in China on the health burden, thereby expanding research on environmental federalism, health, and welfare. Using panel data from 30 Chinese provinces, the empirical findings show that good environmental performance and moderate economic development significantly reduce the environmental health burden. Notably, as environmental decentralization increases, the impact of environmental performance becomes more pronounced, particularly in terms of environmental administration and monitoring. The effect of environmental performance in reducing health burdens is more visible in the northern and western regions with relatively severe pollution, as well as in the subsample with higher health burdens. Overall, this paper emphasizes the importance of political institutional factors in reducing the health burden.

Extreme risk in stock markets poses significant challenges, necessitating greater attention in related research. This study presents an effective machine-learning model for forecasting extreme risks in the American stock market. Specifically, to address the issues of imbalanced data distribution and concept drift, we introduced class weight and time weight parameters to enhance the AdaBoost algorithm. Moreover, we improved the active learning framework by transitioning from manual to algorithmic annotation. Experiments on the S&P 500 index from 2005 to 2022 revealed that our optimal model significantly enhanced the classification performance, particularly for risk instances. Additionally, we validated the efficacy of customized sample weight values, the significance of the density-weight strategy, and the robustness of the overall framework under different risk definition criteria and feature lag periods. Our research is significant for the adoption of appropriate macroeconomic policies to mitigate downside risks and provides a valuable tool for achieving financial stability.

This study analyzes how uncertainty affects the correction process of mispricing. We extract stock market data from the United States spanning from January 1976 to December 2016, and discover that uncertainty exerts a notable impact on traders’ decision-making processes. Various robustness tests have been conducted to validate the credibility of our findings. Notably, the extension of the sample duration until December 2022, encompassing the disruptive COVID-19 pandemic, serves to fortify the cohesion and reliability of our primary analysis, with the findings exhibiting consistency. Additionally, we examine how investor sentiment affects future returns under different uncertainty and overpricing ranks. An inverse relation between investor sentiment and uncertainty is also detected. We contribute to the existing literature by revealing potential features that affect the limits of arbitrage. Our results provide insights in designing arbitrage mechanisms and assist arbitrageurs in strategizing their operations with stocks under different magnitudes of uncertainty.

Using a sample of CSI300 over the 2006–2021 period to establish liquidity spillover networks, we find a significantly negative relationship between liquidity connectedness and stock price crash risk. Further analysis shows that liquidity connectedness depresses stock price crash risk through two potential channels: increased conditional conservatism and decreased stock price synchronicity. Moreover, this effect is more prominent for firms with effective external monitoring, firms with lower risk-taking, and state-owned enterprises (SOEs). Overall, our paper shows that liquidity connectedness is an important factor influencing crash risk and provides useful guidance for corporate management and investor decision-making.