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on Financial Markets |
| By: | William Ginn (Labcorp, Coburg University of Applied Sciences); Jamel Saadaoui (University Paris 8); Evangelos Salachas (University of the Aegean) |
| Abstract: | This paper examines how U.S. monetary policy shocks influence asset price bubbles under different inflation regimes. Using data from 1998–2023, we show that the transmission of policy is neither constant nor time-invariant. Standard local projection (LP) estimates suggest modest average effects, but including the COVID-19 period reveals that these relationships weaken. Employing time-varying local projections (TVP-LP), we document sharp shifts in transmission during the Global Financial Crisis and the pandemic, motivating a nonlinear approach. Nonlinear VAR-LP estimates uncover clear asymmetries: in high-inflation states, monetary tightening deflates bubbles by raising financing costs and constraining risk-taking; in low-inflation states, the same shocks amplify bubbles by raising expected inflation, narrowing credit spreads, and boosting equity returns. We interpret this inversion as evidence of a state-contingent speculative signaling channel, whereby tightening is perceived as a signal of stronger demand or implicit accommodation, encouraging further speculation. This mechanism highlights that safeguarding financial stability requires more than interest rate adjustments alone—it demands explicit attention to the inflationary environment and investor perceptions. |
| Keywords: | Financial stability, asset prices, booms and busts, local projections |
| JEL: | E |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:inf:wpaper:2025.17 |
| By: | Carlo Zarattini (Concretum Group); Alberto Pagani (University of Parma); Andrea Barbon (University of St. Gallen; University of St.Gallen) |
| Abstract: | In recent years, cryptocurrencies have attracted significant attention from both retail traders and large institutional investors. As their involvement in digital assets grows, so does their interest in active and risk-aware investment frameworks. This paper applies a well-established trend-following methodology, successfully deployed for decades in traditional asset classes, to Bitcoin, and then extends the analysis to a comprehensive, survivorship bias-free dataset covering all cryptocurrencies traded since 2015, to evaluate whether its robustness persists in the emerging digital asset space. We propose an ensemble approach that aggregates multiple Donchian channel-based trend models, each calibrated with different lookback periods, into a single signal, as well as a volatility-based position sizing method. This model, applied to a rotational portfolio of the top 20 most liquid coins, achieved notable net-of-fees returns, with a Sharpe ratio above 1.5 and an annualized alpha of 10.8% versus Bitcoin. While assessing the impact of transaction costs, we propose a straightforward yet effective portfolio technique to mitigate these expenses. Finally, we investigate correlations between crypto-focused trend-following strategies and those applied to traditional asset classes, concluding with a discussion on how investors can execute the proposed strategy through both on-chain and off-chain implementations. |
| Keywords: | Trading Systems, Algo Trading, Momentum, Trend-Following, Cryptocurrencies, Risk Management, Technical Analysis, Decentralized Exchanges, Blockchain, Decentralized Finance |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2580 |
| By: | Nicola Borri; Yukun Liu; Aleh Tsyvinski; Xi Wu |
| Abstract: | Cryptocurrencies are coming of age. We organize empirical regularities into ten stylized facts and analyze cryptocurrency through the lens of empirical asset pricing. We find important similarities with traditional markets -- risk-adjusted performance is broadly comparable, and the cross-section of returns can be summarized by a small set of factors. However, cryptocurrency also has its own distinct character: jumps are frequent and large, and blockchain information helps drive prices. This common set of facts provides evidence that cryptocurrency is emerging as an investable asset class. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.14435 |
| By: | Dimitrios Anastasiou (Athens University of Economics and Business - Department of Business Administration); Apostolos G. Katsafados (Athens University of Economics and Business - Department of Accounting and Finance; Bank of Greece); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)); Christos Tzomakas (Athens University of Economics and Business) |
| Abstract: | Building on the methodology of Gorodnichenko et al. (2023), we reconstruct and propose a novel measure that quantifies the voice sentiment of the Chair of the Federal Reserve press conference responses and examine its impact on the stock price crash risk of U.S. banks. We find that a more positive vocal sentiment, indicative of happiness, significantly reduces banks' ex-ante crash risk, whereas negative emotions, such as sadness and anger, amplify it. Our findings suggest that, beyond the textual content of monetary policy statements, the emotional delivery of central bank communication plays a critical role in shaping financial stability outcomes. |
| Keywords: | US banks, Stock Price Crash Risk, Voice Sentiment, Financial Stability |
| JEL: | G01 G21 G41 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2572 |
| By: | Giuseppe Gurrado; Donato Masciandaro |
| Abstract: | In the ongoing race between new digital currencies, a crucial factor for success will be their relative ability to gain acceptance as a medium of exchange, shaping the demand for money. Focusing on the emerging competition between stablecoins and CBDCs, this analysis offers quantitative insights into which of the two is gaining more attention among different audiences, exploring both academic and public interest in recent years. The growth rates of academic publications on stablecoins and CBDCs, after alternating periods of predominance, now appear to converge, even though research on CBDCs remains higher in absolute terms. However, beyond academia, public attention confirms the possibility of stablecoins catching up. The observed cycles of attention for the two digital currencies highlight the importance of specific events, as well as the relevance of contextual and country-specific factors. |
| Keywords: | Central Bank Digital Currencies, Cryptocurrencies, Stablecoin, Money Demand, Social Networks |
| JEL: | B22 C91 E41 E42 E52 E58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp25254 |
| By: | Sergio A. Correia; Stephan Luck; Emil Verner |
| Abstract: | This paper studies the role of banking supervision in anticipating, monitoring, and disciplining failing banks. We document that supervisors anticipate most bank failures with a high degree of accuracy. Supervisors play an important role in requiring troubled banks to recognize losses, taking enforcement actions, and ultimately closing failing banks. To establish causality, we exploit exogenous variation in supervisory strictness during the Global Financial Crisis. Stricter supervision leads to more loss recognition, reduced dividend payouts, and an increase in the likelihood and speed of closure. Increased strictness entails a trade-off between a lower resolution cost to the FDIC and reduced credit. |
| JEL: | G0 G01 G21 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34343 |
| By: | Onur Polat (Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | This paper extends the discussion on the predictive role of bond market information over the stock market to a novel context by proposing a new predictor of stock market bubbles for the United States (US), namely the implied skewness of the Treasury yield. Using daily data from January 1988 to April 2025, we first implement the Multi-Scale Log-Period Power Law Confidence Indicator (MS-LPPLS-CI) framework to detect positive and negative bubbles at the short-, medium- and long-term. Next, employing a nonparametric causality-in-quantiles framework, we show that bond market signals inferred from the implied skewness of the Treasury yield carry significant predictive content for US and international stock market bubbles. While the predictive effect of Treasury yield skewness is found to be asymmetric across the short-, medium-, and long-term of the positive and negative bubble indicators, the strongest influence is observed at the lowest conditional quantiles of the bubble indicators, suggesting that bond market information captured by forward-looking skewness of interest rate implied by Treasury options can be used to forecast impending crashes in the stock market. These results hold when considering the remaining G7 and BRICS countries, providing support for the determinant role of interest rate signals by the Fed over risky asset dynamics in global stock markets and can be used by investors and policy authorities to have timely insights on imminent boom-bust cycles. |
| Keywords: | Multi-Scale Positive and Negative Bubbles; Stock Markets, Implied Skewness of the Treasury Yield, Nonparametric Causality-in-Quantiles Test, US, G7, and BRICS |
| JEL: | C22 G10 G12 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202539 |
| By: | Zofia Bracha; Pawe{\l} Sakowski; Jakub Micha\'nk\'ow |
| Abstract: | This paper explores the application of deep Q-learning to hedging at-the-money options on the S\&P~500 index. We develop an agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, trained to simulate hedging decisions without making explicit model assumptions on price dynamics. The agent was trained on historical intraday prices of S\&P~500 call options across years 2004--2024, using a single time series of six predictor variables: option price, underlying asset price, moneyness, time to maturity, realized volatility, and current hedge position. A walk-forward procedure was applied for training, which led to nearly 17~years of out-of-sample evaluation. The performance of the deep reinforcement learning (DRL) agent is benchmarked against the Black--Scholes delta-hedging strategy over the same period. We assess both approaches using metrics such as annualized return, volatility, information ratio, and Sharpe ratio. To test the models' adaptability, we performed simulations across varying market conditions and added constraints such as transaction costs and risk-awareness penalties. Our results show that the DRL agent can outperform traditional hedging methods, particularly in volatile or high-cost environments, highlighting its robustness and flexibility in practical trading contexts. While the agent consistently outperforms delta-hedging, its performance deteriorates when the risk-awareness parameter is higher. We also observed that the longer the time interval used for volatility estimation, the more stable the results. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.09247 |