This paper re-examines price relationships in global copper futures markets by distinguishing between market co-movement and pricing dominance. Using a unified empirical framework, it analyzes dynamic correlations and information transmission across major exchanges. The results show that, although the markets exhibit time-varying co-movement, pricing dominance remains asymmetric. International markets continue to lead price formation, while emerging markets, represented by China, primarily act as information receivers. Despite changes in mean transmission scores, SHFE and COMEX retain their respective average net receiver and transmitter roles across the estimated segments. These findings suggest that co-movement and pricing dominance capture different dimensions of market integration, and that changes in the degree of market co-movement do not necessarily imply convergence in pricing dominance.
While the determinants of firms’ financial derivatives usage have been extensively studied, the influence of managerial characteristics on this decision-making process has received relatively little attention. This study explores the role of a critical managerial characteristic, namely managerial ability, in shaping financial derivatives usage. We investigate this question within the context of China, a major emerging market. Using data from listed Chinese firms from 2009 to 2024, we find a positive relationship between managerial ability and financial derivatives usage. Mediation analysis identifies two key mechanisms underlying this relationship: information processing capacity and managerial overconfidence. Notably, the increased use of financial derivatives associated with managerial ability is linked to higher firm risk. Cross-sectional analyses suggest that effective monitoring may curb excessive derivatives use among high-ability managers. Furthermore, our results indicate that this increase in financial derivatives usage corresponds to a decline in firm value. While both information processing capacity and managerial overconfidence contribute to the positive relationship between managerial ability and financial derivatives usage, our analysis suggests that managerial overconfidence acts as the dominant factor, ultimately leading to negative consequences for firm value.
Haug and Haug extend the Margrabe exchange option by adding knock-in and knock-out provisions written on the ratio of two asset prices. This paper applies and develops their framework for stock-for-stock takeover bids with collars. The analytical contribution is contract-specific: it combines the collar-induced exchange-option spread with the existing Haug–Haug ratio-barrier formulas, states the initial-state conditions under which those formulas apply, and extends the barrier representation to the binding lower-collar regime. In the standard collar case, the takeover bid is a capped exchange option, equal to a long lower-strike exchange option and a short higher-strike exchange option. When the walk-away provision is written on the same acquirer-to-target value ratio, each option leg can be valued using the Haug–Haug formula after a simple barrier transformation. When the lower collar binds above the exchange threshold, the payoff contains an additional target-share component contingent on the same barrier event; this component is valued as a closed-form asset-or-nothing ratio-barrier claim. Expanded numerical results document the interaction of the collar with down and up barriers, relative volatility, correlation, and time to completion, and clarify that the correlation effect is conditional on the sign of the spread’s net vega. The model provides a tractable benchmark for valuing collared merger consideration and for measuring the value impact of walk-away covenants.
This paper sits at the intersection of derivative pricing and systemic-risk measurement by studying how system-wide tail dependence and crisis amplification can be incorporated into the valuation of European options. The research is motivated by the robust measurement of conditional downside vulnerability, and the adjustment of option values based on each asset’s individual contribution to systemic distress. We subsequently compare a conventional econometric approach against a more flexible machine-learning alternative for estimating this tail-risk component. Empirically, we find substantial heterogeneity in downside tail exposure across broad asset classes, implying that conditioning on systemic distress provides information beyond marginal (stand-alone) risk. We also show that the learning-based tail estimator produces systematically more conservative tail-risk assessments than the econometric baseline, suggesting that adaptive methods may better capture extreme, time-varying distress dynamics. Incorporating systemic tail risk into option pricing yields economically consistent effects–lower call values and higher put values–reflecting compensation for downside externalities that intensify under crisis regimes. Finally, we show that endogenizing the strength of the systemic adjustment generates plausible cross-asset variation, with pricing impacts primarily explained by differences in the individual assets’ contribution to systemic-risk spillovers.
Modeling the price of underlying asset for a financial derivative, requires careful consideration of various aspects of its stochastic behaviour. This paper focuses on three primary influential factors: stochastic volatility, stochastic liquidity and sudden spikes in the price of stock. Our model incorporates stochastic volatility using a Cox-Ingersoll-Ross stochastic differential equation, in line with the approach adapted in the Heston model. As a relatively underexplored yet significant factor in asset pricing, we model stochastic liquidity using an Ornstein-Uhlenbeck process. Furthermore, the model integrates jump components into the price dynamics to effectively capture substantial upward and downward fluctuotions that occur during periods of extraordinary market conditions. We derive an analytical solution for pricing European options and compare its results with real market observations, utilizing the Black-Scholes model as a benchmark. Finally, we include a brief sensitivity analysis to offer a more comprehensive overview of the introduced model. To ensure the robustness of our findings, we conduct the sensitivity analysis under three distinct regimes: baseline, volatile, and tranquil.
