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on Risk Management |
| By: | Siqi Shao; R. A. Serota |
| Abstract: | We analyze distributions of historic S&P500 multi-day returns, for the number of days of accumulation from 20 to 120. With the increase of the number of days of accumulation, we observe clear tempering of power-law tails toward a seemingly finite value. To explain this phenomenon, we employ a model that produces a "capped Inverse Gamma" stationary (steady-state) distribution for stochastic volatility which, in turn, produces a "tempered Student-t" distribution for returns. We then employ Jones-Faddy-like symmetry breaking mechanism that produces a "tempered Skew-t" distribution. This distribution provides rather good fits to the distributions of accumulated multi-day S&P500 returns, which exhibit symmetry breaking between gains and losses -- as reflected by positive mean and negative skew. Tempered Skew-t fits are also consistent with near perfect linear dependence on the number of days of accumulation of the mean values and, even more so, of the variances (mean squared realized volatility) of the distributions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.19318 |
| By: | Eric Cuijpers |
| Abstract: | How do unexpected changes in macroprudential capital buffer requirements impact bank valuation, measured by price-to-book ratios? This study addresses this question by constructing macroprudential capital buffer †surprises†from market reactions to buffer announcements and estimating their effects, using panel local projections, on the price-to-book ratios of a panel of large European banks. The analysis shows that unexpected buffer surprises are associated with a short-run decline in price-to-book ra-tios, followed by a sustained increase in the weeks following the announcement. Such an increase is consistent with market recognition of reduced risk, despite higher buffer requirements that could lower distributable resources, suggesting that the risk channel dominates the payout channel in the valuation of large European banks. |
| Keywords: | Capital regulation; Macroprudential policy; Bank valuation |
| JEL: | G21 G28 G32 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:864 |
| By: | Nicholas Appiah; Ali Jaffri; Dilmi C. W. Hettiachchi-Halpe-Kankanamalage; Svetlozar T. Rachev |
| Abstract: | This paper examines portfolio optimization for commodity exchange-traded funds (ETFs) under heavy-tailed return behavior. Using daily Bloomberg data for 30 U.S.-listed commodity ETFs from 12 December 2018 to 16 December 2024, we study funds spanning agriculture, energy, metals, and broad commodity index exposure. We compare a passive buy-and-hold portfolio with rolling-window optimized portfolios formed under mean--variance and conditional value-at-risk (CVaR) criteria, considering both long-only and restricted long--short strategies. The results showed substantial heterogeneity across commodity sectors, with energy and broad commodity index funds displaying pronounced volatility, skewness, and excess kurtosis. Historical optimization indicated that minimum-risk and CVaR-based portfolios provided more stable cumulative performance than tangent portfolios and generally improved Sharpe, Calmar, and STARR$_{0.95}$ ratios. Extreme-value diagnostics showed that optimized portfolios remained exposed to heavy downside tails, so improved risk-adjusted performance did not eliminate extreme-loss risk. A dynamic extension based on ARMA--GARCH marginal models, Student--$t$ copula dependence, and one-step-ahead predictive scenarios improved performance mainly when combined with minimum-risk or CVaR-based objectives. Dynamic mean--variance tangent portfolios performed less reliably, reflecting sensitivity to expected-return estimation error. Transaction-cost robustness checks further showed that the practical value of dynamic optimization depended on turnover control, with low-turnover dynamic CVaR tangent portfolios remaining more resilient to implementation costs. Overall, the analysis showed that commodity ETF allocation benefited most from conservative and downside-risk-aware optimization, while optimized portfolios continued to require explicit tail-risk and implementation diagnostics. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.26625 |
| By: | Parma Bains; Gabriela E Conde; Nobuyasu Sugimoto; Caroline Wu |
| Abstract: | Large technology firms (BigTech) are increasingly expanding into consumer-facing financial services, particularly payments, credit, insurance, asset management, and financial SuperApps. While their current financial stability implications remain limited in most jurisdictions, rapid growth, especially in emerging market and developing economies, raises new conduct, prudential, and systemic risks. This paper analyzes BigTech business models, key activities, and associated risks, and assesses the adequacy of existing regulatory frameworks. It discusses practical options for supervisors to enhance risk identification, strengthen sector-based and group-wide supervision, expand the regulatory perimeter, improve data protection frameworks, and reinforce domestic and international coordination. No global financial standards apply specifically to BigTech. Given the cross-border nature of BigTech activities, global standards should be developed to facilitate internationally consistent regulation and effective cross-border cooperation. |
| Keywords: | BigTech; BNPL; conglomerate; emerging market and developing economies; financial stability; fintech; systemic risk; insurance; credit; payments; asset management; regulation; supervision |
| Date: | 2026–07–10 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imftnm:2026/009 |
| By: | Mark Whitmeyer |
| Abstract: | I study transformations of returns in the growth-optimal (Kelly) portfolio problem. In the one-safe-one-risky-asset problem, a return transform f universally produces a more conservative portfolio if and only if f is concave and strictly increasing and r/f is convex. As a corollary, I characterize comparative risk aversion for a rationally-inattentive agent: a more risk-averse agent is one who is sufficiently more risk averse in the Pratt (1964) sense. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.19175 |
| By: | Abdulrahman Alswaidan; Cade Jin; Jeffrey D. Varner |
| Abstract: | Synthetic generators of daily equity returns let practitioners stress test, backtest, and design scenarios that a single realized market history cannot supply, but only if the generator reproduces the stylized facts of real returns: heavy tails, negligible linear autocorrelation, and slow decay of the absolute-return autocorrelation. Hidden Markov models with few Gaussian states were long thought unable to reproduce that slow decay, and the standard fix was to abandon them for more complex hidden semi-Markov models. We revisit this issue with a continuous hidden Markov model whose regime chain governs the autocorrelation while per-regime densities govern the marginal, separating the temporal and distributional sides of the original failure. A unified expectation-maximization framework fits Gaussian, Student-t, Laplace, and generalized-error emissions under shared forward-backward recursions and quantile-based initialization, and a spectral identity bounds the number of decay modes by the rank of the centred transition matrix. Across SPY walk-forward folds, a sector-balanced 30-ticker panel, a CRSP cross-decade transfer, and a six-asset basket, that bound was not binding once a few states were used: heavy-tailed marginals, not additional decay modes, closed most of the fit gap, recovering volatility clustering above the i.i.d. baseline and narrowing the kurtosis gap without a tuning hyperparameter. The original failure is therefore distributional, not temporal. On daily US equities, a simple, interpretable Markov model suffices, and unlike a bootstrap or semi-Markov fit that wins only on a single-window fit, the fitted model also yields a regime-conditional Value-at-Risk that passes a joint conditional-coverage test and a copula that reproduces cross-asset correlations: one interpretable generator serving both path simulation and downstream risk and portfolio tasks. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.23492 |