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.
This paper develops a unified set of Fourier-integral formulas for Greeks under Bakshi and Madan’s (2000) option pricing framework. The contribution is to collect these sensitivities within one characteristic-function-based representation, extend them to a continuous dividend yield, and show how put Greeks follow from put-call parity. The paper also clarifies their interpretation through benchmark applications, including CEV and affine models, and documents their numerical implementation under jump and stochastic-volatility dynamics. Numerical results show that the formulas are practically implementable and produce internally consistent model-dependent option sensitivities beyond simple closed-form settings.
We study the problem of European option pricing in markets characterized by regime-switching jump-diffusion dynamics and proportional transaction costs. Classical pricing frameworks become analytically intractable in this setting, motivating asymptotic approximation methods that provide arbitrage-regularized pricing under small transaction costs while remaining computationally tractable. We introduce a neural stochastic differential equation (SDE) framework that employs flexible nonparametric parameterizations implemented via neural networks to parameterize drift, diffusion, and jump coefficients, controlled by a latent finite-state Markov chain. Non-smooth transaction cost adjustments are handled within the standard Itô jump-diffusion framework using a jump-adapted Euler–Maruyama scheme. We establish existence and uniqueness of strong càdlàg solutions, L2-stability bounds, and weak convergence of a jump-adapted Euler–Maruyama scheme. Option prices are approximated via a Feynman–Kac representation with a Leland-type compensator adjustment, yielding arbitrage-regularized prices that approximate super-replication bounds to first order in the cost parameter. Numerical experiments validate the theoretical results and demonstrate accurate recovery of regime-dependent volatility surfaces while maintaining robustness under misclassification.
In this paper, we study the evolution of intraday market quality in a broad cross-section of commodity futures markets from 1996 to 2025, a period characterized by significant structural changes. The influx of passive index investors, the transition from floor trading to automated electronic limit order markets, and the increasing presence of informed financial investors fundamentally reshaped trading dynamics. We find an improvement in intraday market quality following these changes between 2004 and 2014. Electronic trading reduced effective spreads by approximately one-third, significantly improving liquidity. Other enhancements in market quality can be attributed to changes in trader composition. While market quality during index roll and after the index addition of soybean meal has increased slightly, the overall improvements in intraday price efficiency appear to be driven by informed financial traders.
Using the TVP-VAR-DY and TVP-VAR-SV approach, this study investigates the spillover relationships among the key edible oil and oil-seed futures in China and worldwide, as well as the factors that influence them. To such end, the volatility of the daily closing prices of 13 commodities’ main continuous contracts has been considered from January 2013 to December 2023. The findings are as follows: there are strong linkages among such futures, exhibiting obvious time-varying characteristics and high sensitivity to major risk events. In addition, while foreign varieties remain the main risk transmitters most of the time, during the Sino-US trade war, the influence of all China’s related futures has generally risen. Finally, since the impact of the global economic policy uncertainty and the investor sentiment on the risk resonance effect are generally positive, the Chinese policymakers should pay more attention to lowering the economic policy uncertainty, enhancing policy transparency, strengthening investor education and promoting rational investment concepts.
This study develops a unified framework to evaluate stablecoin stability and resilience during major market disruptions. It is the first to analyze all four reserve architectures—fiat-backed, crypto-backed, commodity-backed, and algorithmic. We examine 20 stablecoins from January 2020 to October 2022, spanning the COVID-19 pandemic and the Russia–Ukraine conflict, using a three-pronged approach: event-study abnormal returns to capture immediate market reactions, wavelet-based volatility analysis to track multiscale shock persistence, and peg-deviation metrics to assess mechanical peg stability. Our results reveal pronounced differences across stablecoin architectures. Commodity-backed stablecoins, particularly gold-linked tokens, generate strong positive abnormal returns during both crises, along with positive yet larger and persistent peg deviations, reflecting slower structural adjustment. Wavelet analysis confirms that volatility propagates toward lower frequencies, indicating sustained market-driven appreciation and resilience to external shocks rather than mechanical failure. By contrast, fiat-backed stablecoins combine tight peg adherence with limited abnormal returns, and their high-frequency volatility dissipates quickly, illustrating robust operational stability. These patterns highlight the distinction between market-driven performance and mechanical peg stability. Algorithmic stablecoins perform weakest, with higher volatility and weaker returns. Overall, across stablecoins, minimal abnormal returns suggest their greater stability relative to unbacked cryptocurrencies. Taken together, the three analyses—event-study, wavelets, and peg deviations—offer a comprehensive picture of stablecoin stability and resilience, with direct implications for portfolio construction and regulatory design.
By analyzing highly informative account-level trade data, we study how an increase in the option contract multiplier affects investor composition in index options and futures markets. The increase in contract size reduces overall options trading, with heterogeneous effects across investor types: retail investors and high-frequency traders participate less, while (non-HFT) financial investment firms increase their relative activity. In the futures market, which is not directly affected by the regulation, high-frequency traders increase their share of volume, whereas investment firms reduce theirs. Intraday volatility and quoted spreads fall in the options market, while neither change significantly in the futures market. Enhanced market quality benefits institutional investors but makes the market less attractive for high-frequency traders. These shifts suggest that the two derivatives markets function as substitutes for certain investor groups.
This study investigates the influence of financial derivatives on bank credit risk, accounting for heterogeneity across derivative types, risk measures, and banking structures. The analysis is based on a panel of 68 large banks from 21 countries over the period from 2010 to 2024 and employs the two-stage instrumental variable (IV) approach. Both accounting-based (Z-score) and market-based (Distance to Default) indicators are measured to capture different dimensions of credit risk. The findings reveal that the effects of derivatives are specific to each instrument on credit risk. Interest rate, equity, and commodity derivatives consistently mitigate credit risk, reinforcing their role as effective hedging tools. Conversely, foreign exchange and credit derivatives display ambiguous or risk-increasing effects, particularly when assessed using market-based measures. Additional evidence suggests that the use of derivatives contributes to higher credit risk in bank holding companies compared to commercial banks, which reflects differences in institutional complexity and risk-taking behavior. These results underscore that derivatives are not inherently stabilizing or destabilizing, highlighting the necessity for regulation that is specific to each instrument and the implementation of comprehensive risk assessment frameworks.
In this paper, we investigate the pricing of vulnerable Asian options under contagion dynamics. We first model the price dynamics of the underlying asset and the counterparty’s total assets by employing Hawkes jump-diffusion processes, where jump processes exhibit both self-excitation and cross-asset contagion characteristics. After deriving the pricing formula of vulnerable Asian options, we calibrate the model parameters using real data, and investigate the effects of self-excitation and cross-asset contagions on vulnerable Asian option prices. Specially, opposite effects of the parameters in the underlying asset price and the counterparty’s asset prices are observed, and we also explain them by emphasizing the role of the risk compensation term.
We benchmark the performance of widely used long short-term memory (LSTM) models in predicting standardized implied volatility (IV) of equity options against a range of alternative time series models. We forecast option prices over the period 2018-2023 and find universal models that are trained on data pooled across all underlyings to perform the best. The highest forecasting accuracy is achieved by a bidirectional LSTM model with one hidden layer. This provides evidence outside the stock markets that using universal models with pooled data across underlyings to train neural networks significantly improves the forecasting accuracy for equity option prices. Once the machine learning model is trained, one can inexpensively use the trained models to predict option prices up to 7 years into the future.
This paper exploits individual trading records from a large brokerage service to investigate the trading patterns of retail investors who take short positions in stocks using contracts for differences (CFDs). Their risk tolerance, as reported in MiFID II questionnaires, and their leverage usage indicate greater risk-seeking behavior. Short positions, compared to long positions, constitute larger portions of overall portfolios and are more highly leveraged. Yet, short sellers’ research activity does not suggest that they increase the amount of attention paid to stocks before taking short positions. Compared with other CFD traders, short sellers perform worse, and their profit variability is greater.
This paper develops an integrated optimisation model for pricing and hedging oil derivatives in incomplete markets where available market quotes and the trader’s views, inventory and risk aversion may affect the pricing. The model is well suited for practical applications such as the design of optimal cross-hedging strategies and the market-maker problem of pricing derivatives while managing inventory risk in illiquid market conditions. We use numerical experiments to illustrate the model features. First, by computing optimal hedge ratios, we show that the hedge effectiveness of using all available market quotes is significantly higher than that of conventional strategies using only one hedging instrument. Second, we find that the indifference prices of spread derivative contracts are often more competitive than the available market quotes. Third, we study the sensitivities of indifference prices with respect to a market-maker’s risk aversion, views and inventory.
Amidst the ongoing financial turbulence, this study evaluates the performance of well-established long/short and 60/40 portfolios within a network of ETFs representing different investment styles (S P 500, value, growth, ESG, Sharia, high-dividend, momentum, high-beta, low-volatility, VIX futures). For this purpose, we undertake an in-depth analysis of volatility spillovers, utilizing sophisticated methodologies such as the time-varying parameter vector autoregressive (TVP-VAR), the extended joint connectedness, and R-squared measures. The sample period spans from December 1, 2015, to December 31, 2024, allowing for a comprehensive examination of the risk-mitigation contributions of long/short and 60/40 portfolios based on their neutrality within the broader market network. Under the TVP-VAR approach, both the long/short and 60/40 portfolios demonstrate resilience to market shocks, with the former exhibiting a stronger stabilizing effect. In contrast, the advanced methodological frameworks expose both portfolios as net volatility transmitters, thereby restricting their risk-reduction potential. A closer examination of their net dynamic behavior indicates that both portfolios display time-varying patterns in their ability to curtail risk. Thereby, our research shows that the risk-dampening gains of these portfolios are not inherent, but are conditional on specific market dynamics. This provides crucial insights for investors and policymakers, underscoring the importance of employing a multi-faceted, network-based analytical approach to accurately assess systemic risk and refine asset allocation strategies.
Unlike traditional assets, cryptocurrencies lack fundamental information such as dividends, earnings, or cash flows, requiring market participants to rely on alternative sources of information for price discovery and trading decisions. In this study, we analyze the relationship between news sentiment and Bitcoin (BTC) and Ether (ETH) futures returns, as well as net trading positions. We use a dataset of over 9100 BTC and 5400 ETH news articles. The findings reveal that news sentiment is significantly associated with futures price movements and market positioning by professional investors. We extend the traditional dictionary-based approach of Loughran and McDonald (2011) by enabling a more precise identification of crypto-relevant content. Our findings highlight the role of news sentiment as an information channel in cryptocurrency derivatives markets and uncover substantial differences between the BTC and ETH futures markets.
Despite significant advancements in machine learning for derivative pricing, the efficient and accurate valuation of American options remains a persistent challenge due to complex exercise boundaries, near-expiry behavior, and intricate contractual features. This paper extends a semi-analytical approach for pricing American options in time-inhomogeneous models, including pure diffusions, jump-diffusions, and Levy processes. Building on prior work, we derive and solve Volterra integral equations of the second kind to determine the exercise boundary explicitly, offering a computationally superior alternative to traditional finite-difference and Monte Carlo methods. We address key open problems: (1) extending the decomposition method, i.e. splitting the American option price into its European counterpart and an early exercise premium, to general jump-diffusion and Levy models; (2) handling cases where closed-form transition densities are unavailable by leveraging characteristic functions via, e.g., the COS method; and (3) generalizing the framework to multidimensional diffusions. Numerical examples demonstrate the method's efficiency and robustness. Our results underscore the advantages of the integral equation approach for large-scale industrial applications, while resolving some limitations of existing techniques.
This paper investigates the effect of financial derivatives usage on stock price crash risk within the context of the Chinese emerging market. We develop two competing hypotheses: the risk-increasing hypothesis and the risk-reducing hypothesis. Using a sample of Chinese listed firms from 2009 to 2024, we find robust evidence supporting the risk-reducing hypothesis: firms with financial derivatives usage exhibit significantly lower stock price crash risk. Mechanism analyses reveal that derivatives usage mitigates crash risk through three channels: reducing cash flow volatility, inhibiting managerial self-interested behavior, and improving information disclosure quality. Cross-sectional analyses indicate that this risk-reducing effect is less pronounced in state-owned enterprises (SOEs), firms with high-ability managers, CEOs with financial backgrounds, and firms located in regions with higher levels of marketization. This study contributes to the literature on financial derivatives usage and stock price crash risk, providing critical insights for regulators and investors to assess firms’ financial derivatives usage and manage financial risks in emerging markets.
We document substantial heterogeneity in how option availability relates to anomaly-based long-short returns across different anomaly categories. After adjusting for differences in firm size and liquidity between optionable and non-optionable stocks, momentum and value anomalies are more pronounced for non-optionable stocks, consistent with binding short-sale constraints and information frictions. Investment anomalies, by contrast, are stronger on optionable stocks, in line with theories suggesting that option availability relaxes funding constraints and thereby makes investment-based risk signals more informative. When averaging across all anomaly signals, these opposing effects offset each other, and anomalies are equally strong on optionable and non-optionable stocks.
