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on Financial Markets |
| By: | Gabriele Casto |
| Abstract: | We introduce the Historical and Dynamic Volatility Ratios (HVR/DVR) and show that equity and index volatilities are cointegrated at intraday and daily horizons. This allows us to construct a VECM to forecast portfolio volatility by exploiting volatility cointegration. On S&P 500 data, HVR is generally stationary and cointegration with the index is frequent; the VECM implementation yields substantially lower mean absolute percentage error (MAPE) than covariance-based forecasts at short- to medium-term horizons across portfolio sizes. The approach is interpretable and readily implementable, factorizing covariance into market volatility, relative-volatility ratios, and correlations. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.23533 |
| By: | Andreas Fuster; Teodora Paligorova; James Vickery |
| Abstract: | We use bond-level data to study how US banks manage risk in their securities portfolios, focusing on the period of rapidly-rising interest rates in 2022-23, and examine the role of financial and regulatory frictions in shaping bank behavior. Interest rate risk in bank portfolios increased sharply as rates rose, with significant cross-bank heterogeneity depending on ex ante holdings of bonds with embedded options. In response, exposed banks shortened the duration of bond purchases but did not actively sell risky securities or expand “qualified” hedging activity; securities also played a limited role in banks’ responses to deposit outflows. We identify two frictions that can help explain this inertia. First, we find that banks are highly averse to selling underwater bonds at a discount to book value—e.g., banks were 8-9 times more likely to trade bonds with unrealized gains than unrealized losses in 2022-23. This “strategic” trading is more pronounced for banks that do not recognize unrealized losses in regulatory capital and banks facing stock market pressure. Second, frictions in establishing qualified accounting hedges limited hedging activity depending on bond type and accounting classification. Banks did, however, reduce the interest-rate sensitivity of regulatory capital by classifying the riskiest bonds as held-to-maturity. |
| Keywords: | securities; gains trading; bank; capital regulation; bonds |
| JEL: | G11 G21 G23 G28 |
| Date: | 2025–10–17 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:101960 |
| By: | Tian Guo; Emmanuel Hauptmann |
| Abstract: | In quantitative investing, return prediction supports various tasks, including stock selection, portfolio optimization, and risk management. Quantitative factors, such as valuation, quality, and growth, capture various characteristics of stocks. Unstructured financial data, like news and transcripts, has attracted growing attention, driven by recent advances in large language models (LLMs). This paper examines effective methods for leveraging multimodal factors and newsflow in return prediction and stock selection. First, we introduce a fusion learning framework to learn a unified representation from factors and newsflow representations generated by an LLM. Within this framework, we compare three representative methods: representation combination, representation summation, and attentive representations. Next, building on empirical observations from fusion learning, we explore the mixture model that adaptively combines predictions made by single modalities and their fusion. To mitigate the training instability observed in the mixture model, we introduce a decoupled training approach with theoretical insights. Finally, our experiments on real investment universes yield several insights into effective multimodal modeling of factors and news for stock return prediction. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15691 |
| By: | Visca Tri Winarty; Sena Safarina |
| Abstract: | Since the COVID-19 pandemic, the number of investors in the Indonesia Stock Exchange has steadily increased, emphasizing the importance of portfolio optimization in balancing risk and return. The classical mean-variance optimization model, while widely applied, depends on historical return and risk estimates that are uncertain and may result in suboptimal portfolios. To address this limitation, robust optimization incorporates uncertainty sets to improve portfolio reliability under market fluctuations. This study constructs such sets using moving-window and bootstrapping methods and applies them to Indonesian banking stock data with varying risk-aversion parameters. The results show that robust optimization with the moving-window method, particularly with a smaller risk-aversion parameter, provides a better risk-return trade-off compared to the bootstrapping approach. These findings highlight the potential of the moving-window method to generate more effective portfolio strategies for risk-tolerant investors. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15288 |