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on Risk Management |
| By: | Zhanyi Jiao; Qiuqi Wang; Yimiao Zhao |
| Abstract: | Backtesting risk measures is a unique and important problem for financial regulators to evaluate risk forecasts reported by financial institutions. As a natural extension to standard (or traditional) backtests, comparative backtests are introduced to evaluate different forecasts against regulatory standard models. Based on recently developed concepts of e-values and e-processes, we focus on how standard and comparative backtests can be manipulated in financial regulation by constructing e-processes. We design a model-free (non-parametric) method for standard backtests of identifiable risk measures and comparative backtests of elicitable risk measures. Our e-backtests are applicable to a wide range of common risk measures including the mean, the variance, the Value-at-Risk, the Expected Shortfall, and the expectile. Our results are illustrated by ample simulation studies and real data analysis. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.05840 |
| By: | Purba Banerjee; Srikanth Iyer; Shashi Jain |
| Abstract: | We consider an investor who wants to hedge a path-dependent option with maturity $T$ using a static hedging portfolio using cash, the underlying, and vanilla put/call options on the same underlying with maturity $ t_1$, where $0 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00781 |
| By: | Tamara Broderick; Ali Jadbabaie; Vanessa Lin; Manuel Quintero; Arnab Sarker; Sean R. Sinclair |
| Abstract: | Surety bonds are financial agreements between a contractor (principal) and obligee (project owner) to complete a project. However, most large-scale projects involve multiple contractors, creating a network and introducing the possibility of incomplete obligations to propagate and result in project failures. Typical models for risk assessment assume independent failure probabilities within each contractor. However, we take a network approach, modeling the contractor network as a directed graph where nodes represent contractors and project owners and edges represent contractual obligations with associated financial records. To understand risk propagation throughout the contractor network, we extend the celebrated Friedkin-Johnsen model and introduce a stochastic process to simulate principal failures across the network. From a theoretical perspective, we show that under natural monotonicity conditions on the contractor network, incorporating network effects leads to increases in both the average risk and the tail probability mass of the loss distribution (i.e. larger right-tail risk) for the surety organization. We further use data from a partnering insurance company to validate our findings, estimating an approximately 2% higher exposure when accounting for network effects. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.05691 |
| By: | Matteo Crosignani; Thomas M. Eisenbach; Fulvia Fringuellotti |
| Abstract: | As in previous years, we provide in this post an update on the vulnerability of the U.S. banking system based on four analytical models that capture different aspects of this vulnerability. We use data through 2025:Q2 for our analysis, and also discuss how the vulnerability measures have changed since our last update one year ago. |
| Keywords: | banking system vulnerability; bank capital; fire sales; liquidity risk; run risk |
| JEL: | G01 G21 |
| Date: | 2025–11–04 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:102060 |
| By: | M. Cadoni; R. Melis; A. Trudda |
| Abstract: | Volatility estimation has become one of the core activities of financial analysts. At present, the majority of buy and sell operations are run by "computer traders" that use algorithms mainly based on volatility levels in the market. Several analyses argue that the recent "flash crash crisis" are the amplified consequence of volatility variations. Among the various methodologies proposed in literature, fractals are playing a major role in modeling financial series and, in particular, in analysing volatility characteristics. Following this line, we propose a stochastic approach using a random variable to represent the Hurst Exponent H. We adopt an iterative procedure to model H with a mixture of n Beta distributions, where the number of components will depend on the required modeling accuracy. We choose several types of financial market indexes and assets to evaluate the model and show that the proposed methodology can provide a deep insight into the volatility characteristics associated to each one of them. |
| Keywords: | volatility;Investment Decisions;multifractional brownian motion |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202515 |
| By: | Martins, Igor F. B. Martins (Örebro University School of Business); Virbickaitè, Audronè (CUNEF University, Madrid, Spain); Nguyen, Hoang (Linköping University); Hedibert, Freitas Lopes (Insper Institute of Education and Research) |
| Abstract: | This paper proposes a mixed-frequency stochastic volatility model for intraday returns that captures fast and slow level shifts in the volatility level induced by news from both low-frequency variables and scheduled announcements. A MIDAS component describes slow-moving changes in volatility driven by daily variables, while an announcement component captures fast eventdriven volatility bursts. Using 5-minute crude oil futures returns, we show that accounting for both fast and slow level shifts significantly improves volatility forecasts at intraday and daily horizons. The superior forecasts also translate into higher Sharpe ratios using the volatilitymanaged portfolio strategy. |
| Keywords: | Intraday volatility; high-frequency; announcements; MIDAS; oil; sparsity. |
| JEL: | C22 C52 C58 G32 |
| Date: | 2025–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2025_012 |
| By: | Cansu Isler |
| Abstract: | Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the Growth-at-Risk (GaR) approach by introducing a novel daily sentiment indicator derived from textual analysis of mandatory corporate disclosures (SEC 10-K and 10-Q reports) to forecast downside risks to economic growth. Using the Loughran--McDonald dictionary and a word-count methodology, I compute firm-level tone growth as the year-over-year difference between positive and negative sentiment expressed in corporate filings. These firm-specific sentiment metrics are aggregated into a weekly tone index, weighted by firms' market capitalizations to capture broader, economy-wide sentiment dynamics. Integrated into a mixed-data sampling (MIDAS) quantile regression framework, this sentiment-based indicator enhances the prediction of GDP growth downturns, outperforming traditional financial market indicators such as the National Financial Conditions Index (NFCI). The findings underscore corporate textual data as a powerful and timely resource for macroeconomic risk assessment and informed policymaking. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.04935 |
| By: | Carlo Zarattini (Concretum Group); Andrew Aziz (Peak Capital Trading; Bear Bull Traders); Antonio Mele (University of Lugano; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)) |
| Abstract: | Volatility isn't just a measure of market fluctuations; it is the underlying asset of a large number of tradable instruments. After a concise overview of the history of volatility trading, this paper shows how individual investors can construct portfolios that aim to capture the volatility risk premium using nothing more than VIX-linked exchange-traded notes (ETNs). We test four rule sets, beginning with a constant short-volatility allocation and ending with a dynamically sized strategy that responds to both the option-market premium and the slope of the VIX term structure. Over 2008-2025, and after realistic costs, the final version compounds at 16.3% per year, delivers a Sharpe ratio of 1, and keeps equity-market correlation near 15%. Blending even a modest slice of this strategy into a passive SPY portfolio can lift the combined Sharpe ratio by 20%. We also outline how the rules can be automated through a standard broker API. In conclusion, volatility trading is no longer the exclusive domain of institutional hedge funds. With the right tools and discipline, individual investors and systematic traders can now access and exploit volatility-based strategies. However, one must always be mindful: volatility itself is volatile-and should be handled with care. |
| Keywords: | Trading Systems, Algo Trading, VIX, Volatility Trading, VXX, VIX ETNs, Automatic Trading |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2591 |
| By: | Beverly Hirtle; Matthew Plosser |
| Abstract: | Bank failures and distress can be costly to the economy, causing losses to creditors and reducing the flow of credit and other financial intermediation services. Thus, there is significant value in being able to identify “at risk” banks in a timely and accurate way. In a previous post, we presented a new solvency metric, Economic Capital, and showed how solvency risks in the U.S. banking industry have evolved over time according to this measure. In this post, we continue to draw on our recent Staff Report to present analysis showing that Economic Capital identifies failing banks earlier and more accurately than more conventional solvency measures. |
| Keywords: | bank capital; economic capital; bank solvency |
| JEL: | G21 G28 G20 |
| Date: | 2025–11–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:102061 |
| By: | McQuade, Peter; Pancaro, Cosimo; Reghezza, Alessio; Avril, Pauline |
| Abstract: | This paper investigates whether geopolitical risk causes a reduction in bank lending. In particular, it focuses on how the increase in geopolitical risk stemming from the Russian invasion of Ukraine affected euro area bank credit supply. Matching granular supervisory and credit register data and using a panel difference-in-difference approach, the results show that banks with larger exposure to the increase in geopolitical risk cut lending significantly more than those with smaller exposure. Banks with greater exposure raised impairments despite exhibiting similar levels of credit distress to their peers, suggesting that the fall in lending was driven by uncertainty. Moreover, firms that were heavily reliant on banks with high exposure to geopolitical risk were unable to fully substitute this shortfall in credit by borrowing more from less affected banks, which significantly constrained firm investment and employment. JEL Classification: G1, G21, E22 |
| Keywords: | banks, financial markets, uncertainty |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253143 |
| By: | Nikolas Anic (Swiss Finance Institute - University of Zurich; Finreon); Andrea Barbon (University of St. Gallen; University of St.Gallen); Ralf Seiz (University of St.Gallen; Finreon); Carlo Zarattini (Concretum Group) |
| Abstract: | This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard longonly momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model's pre-training cutoff. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies. |
| Keywords: | Large Language Models, Momentum Investing, Textual Analysis, News Sentiment, Artificial Intelligence |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2594 |
| By: | Bermin, Hans-Peter (Affiliated with Lund University, Knut Wicksell Centre for Financial Studies); Holm, Magnus (Hilbert Group AB) |
| Abstract: | The maximum drawdown of a stochastic process is the largest peak-to-trough decline observed over a given horizon. Using arguments from extreme value theory, we derive the limiting distribution of the maximum drawdown for a Brownian motion with positive drift. We show that, after suitable centering and scaling, the maximum drawdown converges in distribution to the Gumbel law. |
| Keywords: | maximum drawdown; extreme value theory; asymptotic distribution |
| JEL: | G11 G32 |
| Date: | 2025–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lunewp:2025_009 |
| By: | Hamidreza Maleki Almani |
| Abstract: | Heavy-tailed phenomena appear across diverse domains --from wealth and firm sizes in economics to network traffic, biological systems, and physical processes-- characterized by the disproportionate influence of extreme values. These distributions challenge classical statistical models, as their tails decay too slowly for conventional approximations to hold. Among their key descriptive measures are quantile contributions, which quantify the proportion of a total quantity (such as income, energy, or risk) attributed to observations above a given quantile threshold. This paper presents a theoretical study of the quantile contribution statistic and its relationship with order statistics. We derive a closed-form expression for the joint cumulative distribution function (CDF) of order statistics and, based on it, obtain an explicit CDF for quantile contributions applicable to small samples. We then investigate the asymptotic behavior of these contributions as the sample size increases, establishing the asymptotic normality of the numerator and characterizing the limiting distribution of the quantile contribution. Finally, simulation studies illustrate the convergence properties and empirical accuracy of the theoretical results, providing a foundation for applying quantile contributions in the analysis of heavy-tailed data. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.04784 |