Search Results (41)

The literature on derivative pricing in illiquid markets has mostly focused on computing optimal hedging controls, but empirical microstructure studies show that large order flow generates persistent and predictable price effects. Therefore, these controls can themselves induce endogenous market manipulation because traders can internalize the impact of their own trades. We identify the key shortcoming as the absence of a formal separation between a large trader’s informational advantage and the mechanical price impact and temporary cost-of-hedging. To address this gap, we introduce a counterfactual informed observer—an agent who knows the large trader’s strategy but does not face trading frictions—and use this device to isolate informational order-flow effects from mechanical price impact, a distinction explicitly observed in microstructure data. We prove the existence of information-neutral probability measures under which the discounted asset is a martingale for this observer and derive a hedging framework that jointly accounts for transaction costs and permanent market impact. Numerical experiments show that because price pressure and order-flow effects create non-linear execution costs, the optimal hedge for an out-of-the-money call can deviate substantially from the Black–Scholes hedge, with implications for risk management and regulatory monitoring. Full article

As an emerging production factor, data assets are gaining strategic prominence, yet their application in collateralized financing faces persistent challenges, including illiquidity and risk evaluation complexities. This study introduces an innovative $P_{max}$ model to enhance the Collateral Value of data assets through insurance mechanisms, systematically demonstrating the feasibility conditions under which risk transfer optimizes asset valuation and delineating implementation pathways to integrate data insurance with asset-backed financing. Building on the theoretical framework of Value-at-Risk (VaR), this study develops a dynamic valuation model to assess the value of the collateral before and after insurance. Our analysis shows that insurance coverage for potential losses significantly enhances financing viability when premiums satisfy $P_{max}$. Empirical analysis employing Monte Carlo simulations reveals a nonlinear positive correlation between pledgees’ risk tolerance thresholds and the maximum acceptable premium $P_{max}$. This study bridges theoretical gaps in understanding insurance-value relationships for data assets while providing conceptual foundations and operational blueprints to standardize data markets and foster financial innovation. Full article

Global biodiversity decreased by 69% from 1970 to 2022, representing a key risk to economic activity. However, the link between nature, biodiversity and finance has received little attention within the field of sustainable finance. This paper attempts to fill this gap. Nature finance aims to avoid biodiversity loss and promote nature-positive activities, such as the conservation and protection of biodiversity through market-based solutions with the proper measurement of impact. Measuring biodiversity impact remains a challenge for most companies and banks, with a fragmented landscape of nature frameworks. We conduct a bibliometric analysis of the literature on biodiversity finance and analyze a unique market dataset of five global investment funds as well as all corporate bonds issued in Brazil, the country with the largest biodiversity assets. First, we find that the literature on nature finance is recent with a tipping point in 2020, with the three most common concepts being ecosystem services, nature-based solutions and circular economy. Second, we find that sovereigns and two corporate sectors (food production, pulp & paper) represent the vast majority of issuers that currently incorporate biodiversity considerations into funding structures, suggesting an opportunity to expand accountability for biodiversity impacts across a greater number of sectors. Third, we find a disconnect between science and finance. Out of a catalogue of 158 biodiversity metrics proposed by the IFC, just 33 have been used in bond issuances and 32 by fund managers, suggesting an opportunity for technical assistance for companies and to simplify catalogs to create a common language. Lack of consensus around metrics, complexity, and cost explain this gap. Fourth, we identify a distinction between liquid markets and illiquid markets in their application of biodiversity impact management and measurement. Illiquid markets, such as private equity, bilateral lending, voluntary carbon markets or investment funds can develop complex bespoke mechanisms to measure nature, leveraging detailed catalogues of metrics. Liquid markets, including bonds, exhibit a preference for simpler metrics such as preserved areas or forest cover. Full article

In a significant transition from classical theories of efficient markets and perfectly rational investors, the recent literature has increasingly acknowledged the importance of the human element and external market conditions in decision-making. However, the application of adaptive market frameworks in the property sector remains underexplored. This gap is particularly pronounced in the commercial property market, where structural inefficiencies, such as information asymmetry and illiquidity, amplify decision-making complexity. Given that investor rationality tends to diminish as uncertainty and complexity increase, this study explored how private commercial property investors adapt their strategies amid heightened uncertainty. The perspectives of seven experienced property experts were thematically analysed to highlight recurring patterns, which were then integrated into a conceptual mind map. The findings reveal that while economic fundamentals are the constant drivers of capital allocation decisions, investors process these signals through the lens of adaptive behaviour based on intuition, experience, risk perceptions, and herding. This relationship becomes more pronounced under conditions of heightened uncertainty, where investors seek to supplement available information with sentiment due to weaker signals and declining confidence in fundamentals. Sustainable investing and technology integration also emerged as core considerations, but interest among private investors is subdued due to ambiguous value propositions regarding the long-term economic benefits of a green premium. These findings offer practical insights into how external market conditions influence property investment decisions and provide a platform for operational models of investment decision-making that integrate adaptive behaviour. Full article

This study identifies a nonlinear relationship among liquidity, tracking error, and risk-adjusted performance in JETFs. Collecting daily data for 1077 JETFs from January 2008 to April 2022, we find a concave association, whereby both highly liquid and highly illiquid JETFs exhibit lower risk-adjusted returns and higher tracking errors. Employing quantile regression, we further show that smaller, less liquid JETFs tend to deliver superior risk-adjusted performance. When comparing across listing venues—Japan, the U.S., Ireland, and Luxembourg—we find that the impact of liquidity on performance is most pronounced in the Japanese market, which also shows the highest average tracking error. In contrast, U.S.-listed JETFs offer the lowest tracking error. These results suggest that investors may benefit from choosing smaller JETFs listed in Japan. Full article

This paper investigates the use of reinforcement learning (RL) algorithms to learn adaptive hedging strategies for derivatives under realistic market conditions, incorporating permanent market impact, execution slippage, and transaction costs. Market frictions arising from trading have been explored in the optimal trade execution literature; however, their influence on derivative hedging strategies remains comparatively understudied within RL contexts. Traditional hedging methods have typically assumed frictionless markets with only transaction costs. We illustrate that the dynamic decision problem posed by hedging with frictions can be modelled effectively with RL, demonstrating efficacy across various market frictions to minimize hedging losses. The results include a comparative analysis of the performance of three RL models across simulated price paths, demonstrating their varying effectiveness and adaptability in these friction-intensive environments. We find that RL agents, specifically TD3 and SAC, can outperform traditional delta hedging strategies in both simplistic and complex, illiquid environments highlighted by 2/3rd reductions in expected hedging losses and over 50% reductions in 5th percentile conditional value at risk (CVaR). These findings demonstrate that DRL agents can serve as a valuable risk management tool for financial institutions, especially given their adaptability to different market conditions and securities. Full article

This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article

Variational autoencoders (VAEs) have emerged as a promising tool for modeling volatility surfaces, with particular significance for generating synthetic implied volatility scenarios that enhance risk management capabilities. This study evaluates VAE performance using synthetic volatility surfaces, chosen specifically for their arbitrage-free properties and clean data characteristics. Through a comprehensive comparison with traditional methods including thin-plate spline interpolation, parametric models (SABR and SVI), and deterministic autoencoders, we demonstrate that our VAE approach with latent space optimization consistently outperforms existing methods, particularly in scenarios with extreme data sparsity. Our findings show that accurate, arbitrage-free surface reconstruction is achievable using only 5% of the original data points, with errors 7–12 times lower than competing approaches in high-sparsity scenarios. We rigorously validate the preservation of critical no-arbitrage conditions through probability distribution analysis and total variance strip non-intersection tests. The framework we develop overcomes traditional barriers of limited market data by generating over 13,500 synthetic surfaces for training, compared to typical market availability of fewer than 100. These capabilities have important implications for market risk analysis, derivatives pricing, and the development of more robust risk management frameworks, particularly in emerging markets or for newly introduced derivatives where historical data are scarce. Our integration of machine learning with financial theory constraints represents a significant advancement in volatility surface modeling that balances statistical accuracy with financial relevance. Full article

This paper solves the optimal trading problem of carbon options with a deep learning approach. In this setting, a trader wants to sell out the option inventory within a day. Since trading a large-size order in the market will influence the price, the trader needs to design a trading strategy to maximize the profit and loss (PnL). We propose a deep learning strategy for carbon options optimal trading, which can also be extended to stock options. Using the data from the European carbon market, we apply our deep learning strategy to four types of price impact functions: linear, logarithmic, power law, and time-varying. We show that our deep learning strategy performs much better than the naive strategy and the TWAP (time-weighted average price) strategy, which are widely used in the industry, especially when the price impact function is time-varying. Our neural network strategy’s advantage becomes larger when the market is more illiquid. Full article

In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, and growth metrics. For instance, firms showcasing favorable debt-to-equity ratios along with robust revenue growth are identified as high-performing entities. This classification facilitates targeted analyses of market dynamics. To predict market states—categorizing them into bull, bear, or neutral phases—the framework utilizes a Non-Stationary Markov Chain (NMC), BERT, to assess sentiment in financial news articles and Long Short-Term Memory (LSTM) networks to identify temporal patterns. Key inputs like the Sentiment Index (SI) and Illiquidity Index (ILLIQ) play essential roles in dynamically influencing regime predictions within the NMC model; these inputs are supplemented by variables including GARCH volatility and VIX to enhance predictive precision further still. Empirical findings demonstrate that our approach achieves an impressive 97.20% accuracy rate for classifying market states, significantly surpassing traditional methods like Naive Bayes, Logistic Regression, KNN, Decision Tree, ANN, Random Forest, and XGBoost. The state-predicted strategy leverages this framework to dynamically adjust portfolio positions based on projected market conditions. It prioritizes growth-oriented assets during bull markets, defensive assets in bear markets, and maintains balanced portfolios in neutral states. Comparative testing showed that this approach achieved an average cumulative return of 13.67%, outperforming the Buy and Hold method’s return of 8.62%. Specifically, for the S&P 500 index, returns were recorded at 6.36% compared with just a 1.08% gain from Buy and Hold strategies alone. These results underscore the robustness of our framework and its potential advantages for improving decision-making within quantitative trading environments as well as asset selection processes. Full article

This article estimates the weighted average cost of capital (WACC) for the energy sector in Ecuador, a country with a dollarized economy and illiquid stock markets. Thus, reference companies in the region were taken, and at the same time combined with characteristics of national companies, establishing a useful methodology, which makes sense with the acceptable discount rates in the Ecuadorian economy. For the above, four estimation alternatives were used. In method one, the traditional WACC formula was applied using interest rates and risk premiums from the U.S. market, which resulted in an overestimation due to the double penalty of the country risk and the U.S. market premium. Method two adjusted the market risk premium to consider only the Ecuador-specific risk premium, thus avoiding the double penalty. In method three, the credit default swap (CDS) was used to calculate the country risk premium, and the CDS was excluded from the nominal interest rate, avoiding redundancies. Finally, method four combined the U.S. interest rate with the CDS directly to calculate the market risk premium, more accurately reflecting local economic conditions in a dollarized economy. The WACC results range from 12.63% to 29.70%. In addition, a dummy variable was controlled for during the pandemic period. This article highlights the need for methodologies adapted to emerging markets, since traditional approaches would overestimate the WACC. Full article

Based on the typical positions of S&P 500 option market makers, we derive a funding illiquidity measure from quoted prices of S&P 500 derivatives. Our measure significantly affects the returns of leveraged managed portfolios; hedge funds with negative exposure to changes in funding illiquidity earn high returns in normal times and low returns in crisis periods when funding liquidity deteriorates. The results are not driven by existing measures of funding illiquidity, market illiquidity, and proxies for tail risk. Our funding illiquidity measure also affects leveraged closed-end mutual funds and, to an extent, asset classes where leveraged investors are marginal investors. Full article

This study introduces a robust model that captures wheat futures’ volatility dynamics, influenced by seasonality, time to maturity, and storage dynamics, with minimal calibratable parameters. Our approach reduces error-proneness and enhances plausibility checks, offering a reliable alternative to models that are difficult to calibrate. Transferring estimated parameters from liquid to illiquid markets is feasible, which is challenging for models with numerous parameters. This is of practical importance as it improves the modeling of volatility in illiquid markets, where price discovery is less efficient. In liquid markets, on the other hand, where speculative activity is high, we find that implied volatility is usually the best measure. Additionally, the introduced volatility model is suitable for pricing options on wheat futures as a risk-neutral measure. Full article

Recently, liquidity issues in financial markets and portfolio asset management have attracted much attention among investors and scholars, fuelling a stream of research devoted to exploring the role of liquidity in investment decisions. In this paper, we aim to investigate the effects of introducing liquidity in portfolio optimization problems. For this purpose, first we consider three volume-based liquidity measures proposed in the literature and we build a new one particularly suited to portfolio optimization. Secondly, we formulate an extended version of the Markowitz portfolio selection problem, named mean–variance–liquidity, wherein the goal is to minimize the portfolio variance subject to the usual constraint on the expected portfolio return and an additional constraint on the portfolio liquidity. Thirdly, we consider a sensitivity analysis, with the aim to assess the trade-offs between liquidity and return, on the one hand, and between liquidity and risk, on the other hand. In the second part of the paper, the portfolio optimization framework is applied to a dataset of US ETFs comprising both standard and alternative, often illiquid, investments. The analysis is carried out with all the liquidity measures considered, allowing us to shed light on the relationships among risk, return and liquidity. Finally, we study the effects of the introduction of a Bitcoin ETF, as an asset with an extremely high expected return and risk. Full article

Lenders writing loans in the peer-to-peer market carry risk with the anticipation of an expected return. In the current implementation, many lenders do not have an exit strategy beyond holding the loan for the full repayment term. Many would-be lenders are deterred by the risk of being stuck with an illiquid investment without a method for adjusting to overall economic conditions. This risk is a limiting factor for the overall number of loan transactions. This risk prevents funding for many applicants in need, while simultaneously steering capital towards other more liquid and mature markets. The underdeveloped valuation methods used presently in the peer-to-peer lending space present an opportunity for establishing a model for assigning value to loans. We provide a novel application of an established model for pricing peer-to-peer loans based on multiple factors common in all loans. The method can be used to give a value to a peer-to-peer loan which enables transactions. These transactions can potentially encourage participation and overall maturity in the secondary peer-to-peer loan trading market. We apply established valuation algorithms to peer-to-peer loans to provide a method for lenders to employ, enabling note trading in the secondary market. Full article