The authors formulate a risk-based swaption portfolio management framework for a profit-and-loss (P&L) explanation. They analyze the implication of using the right volatility backbone in the pricing model from a hedging perspective and demonstrate the importance of incorporating stability and robustness measure as part of the calibration process for optimal model selection. They also derive a displaced-diffusion stochastic volatility model with a closed-form analytical expression to handle negative interest rates. Finally, they show that their framework is able to identify the optimal pricing model, which leads to a superior P&L explanation and hedging performance. TOPICS:Risk management, portfolio management/multi-asset allocation, derivatives, factor-based models Key Findings • A holistic, risk-based calibration framework allows one to select the optimal pricing model with superior P&L explanation performance. • A displaced-diffusion stochastic volatility model with closed-form expression provides a mean to price swaptions efficiently under both positive and negative interest rate regimes while capturing the volatility backbone. • Using the Herfindahl-index to measure the concentration of hedging performance, we show that the optimal model exhibits stability and robustness of model parameters, along with the economy of the explanatory power of daily P&L movement.

This paper develops a tractable theoretical framework to study how network participation shapes the boom–bust dynamics of non-fungible token (NFT) prices. We model NFT pricing under network effects and heterogeneous consumers, and show that prices and participation are jointly determined in equilibrium. The model implies a critical participation threshold that separates expansion from contraction regimes: above this threshold, positive feedback between participation and valuation generates self-reinforcing growth, while below it, weakening network benefits lead to contraction. We provide empirical evidence using data from the aggregate NFT market and prominent collections including Bored Ape Yacht Club (BAYC) and CryptoPunks. Reduced-form regressions show a positive association between prices and network participation, with stronger effects at the collection level than in the aggregate market. Threshold estimation further provides evidence consistent with regime-dependent dynamics, with clearer tipping behaviour in well-defined NFT communities than in the aggregate market. These findings suggest that NFT valuation is closely tied to network structure and participation dynamics. More broadly, this paper contributes a unified framework that links participation, price formation, and threshold behaviour in NFT markets.

The authors show that the slope of the volatility decile portfolio’s return profile contains valuable information that can be used to time volatility under different market conditions in the United States. During good (bad) market conditions, the high- (low-) volatility portfolio produces the highest return. The authors proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio’s return profile. Volatility timing is achieved by being aggressive during strong growth periods and conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a fivefold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence, the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets and holding a concentrated growth portfolio during bull markets. Key Findings ▪ The return profile of the volatility decile portfolio is time-varying. Its slope contains vital information on market condition—high-volatility portfolio outperforms low-volatility portfolio during good market condition, but underperforms during bad market condition. Since market regime and asset price behaviors are persistent, the slope parameter can be used to time volatility exposure. ▪ Holding the low-volatility portfolio benefits from the higher risk-adjusted return during general market condition. However, when the slope parameter is positive and statistically significant, it is optimal to hold the high-volatility portfolio for the subsequent period. This will ride on the higher return of high-volatility portfolio during strong growth periods. This leads to higher return and increased volatility, but both Sortini ratio and Information ratio exhibit statistically significant improvement. ▪ Stocks in the low-volatility portfolio are less correlated than stocks in the high-volatility portfolio. The outperformance of the volatility timing strategy formulated in this paper can be attributed to holding a concentrated growth portfolio during good market conditions, and holding a diversified portfolio during bad market conditions, thus connecting the literature on low-volatility portfolio with studies on correlation structure and diversification.

Portfolio optimization demands simultaneous consideration of multiple conflicting criteria under uncertainty, yet prevailing approaches either rely on some black-box machine learning (ML) models that sacrifice interpretability or employ classical multi-criteria decision-making (MCDM) methods lacking predictive foresight. This paper proposes a two-stage framework integrating a Transformer encoder for multi-output financial forecasting with the Peer-Prediction Trees for MCDM (PPT-MCDM) method for dynamic asset ranking and portfolio construction. The Transformer generates forward-looking predictions of next-period return, volatility, and maximum drawdown, while PPT-MCDM ranks assets by their excess performance index (EPI), measuring how much each asset’s multi-criteria profile exceeds data-driven peer expectations. The framework is validated on 28 sector and thematic exchange-traded funds (ETFs) over a 51-month out-of-sample period from January 2022 to March 2026. The PPT-MCDM portfolio achieves an annualized return of 11.99% with a Sharpe ratio of 0.589 and maximum drawdown of 18.80%, compared to the S&P 500 benchmark delivering 9.16% return, Sharpe ratio of 0.391, and maximum drawdown of 20.25%. An ablation study confirms that Transformer predictions improve the Sharpe ratio by 39.9% relative to using only observed backward-looking criteria. The main contributions of this work are three-fold: first, the development of a two-stage framework integrating deep learning forecasting with interpretable MCDM-based portfolio ranking; second, the first application of PPT-MCDM method to dynamic portfolio optimization with expanding-window retraining; third, empirical evidence that the framework outperforms the S&P 500 on both return and risk-adjusted metrics during a period encompassing both bear and bull market conditions.

Consumer financial services depend on interconnected institutions, products and regulatory processes, yet complaints about failures within this infrastructure are usually analysed as individual cases or grouped using administrative categories. This study examines whether large-scale complaint data can instead reveal recurring structures of failure. Using 4.7 million complaints submitted to the U.S. Consumer Financial Protection Bureau between 2011 and 2024, we develop a Failure Ecosystem Framework that groups complaint narratives by semantic similarity and traces the institutions and products associated with each group over time. The analysis identifies 16 failure ecosystems, spanning credit reporting, identity theft, debt collection, payment disputes, mortgage servicing and digital finance. These ecosystems differ substantially: some failures concentrate among a few institutions while others are widely distributed, and in many, the same institution–product combinations recur from year to year. Greater concentration is not systematically associated with better or worse complaint outcomes, and the evidence is least conclusive for monetary compensation. Corrective outcomes vary more across failure types than with concentration, indicating that market structure alone cannot explain how consumer failures are remedied. Complaints can therefore reveal persistent structures of failure that case-by-case review does not expose, supporting system-level monitoring of vulnerabilities in consumer finance.