broker-fees-and-calculators

Erik Heitfield , Mallick Hossain, and Katie Merritt Using fine-grained data on 465,000 Florida home sales over twelve years and semiparametric machine learning methods, we examine how home prices respond to weather-related risk factors. After controlling for geography, home characteristics, and transaction features, we find that home prices are negatively related to property-level expected weather losses derived from catastrophe models, insurance premiums, and exposure to recent hurricanes, but with notably different magnitudes. Expected weather losses show the strongest association with prices and are consistent with markets rationally capitalizing anticipated future losses into current property valuations. Insurance premiums have a statistically significant but economically more modest negative effect on home prices, suggesting that housing market participants may view premium differences across properties as more transitory. Recent hurricane exposure has only a marginal effect on house prices, suggesting that hurricane risk is well-understood in Florida so that realized weather events convey little new information about future losses. ## Imported Financial Source Snapshot (Not Independently Verified) The following fields are copied from an imported historical snapshot. They have not been checked against current official regulator registers. Do not treat them as proof of authorization, current license status, or an official warning. - **INTO (INTO)** — Imported score: **2.7/10** | Imported risk field: `unknown` | Source license records: `[No license record in this snapshot]`

The disclosure specifically references: no source facts provided.

This briefing summarizes the official release at https://www.federalreserve.gov/econres/feds/premiums-or-peril.htm and reconciles key quantitative metrics against primary records.

Edward Herbst and Karen Page Multiple-horizon forecast panels are increasingly used to infer perceived monetary policy rules, but inference depends on how coefficients are pooled across forecasters, dates, and horizons. We treat this pooling structure as the object of inference. In a participant-date-horizon Taylor-rule regression model, we compare pooling patterns using Bayesian marginal likelihoods, applying the framework to the Blue Chip Financial Forecasts, Survey of Professional Forecasters, and the Summary of Economic Projections. The preferred specifications place much of the systematic variation in policy-rate forecasts in intercepts that vary across forecast horizons and survey dates. Evidence of "changing perceptions" of monetary policy via economically meaningful time-varying response coefficients is weak overall. ## Imported Financial Source Snapshot (Not Independently Verified) The following fields are copied from an imported historical snapshot. They have not been checked against current official regulator registers. Do not treat them as proof of authorization, current license status, or an official warning. - **Taylor (Taylor OÜ)** — Imported score: **1.6/10** | Imported risk field: `unknown` | Source license records: `[No license record in this snapshot]` - **VIA (Viacoin)** — Imported score: **1.0/10** | Imported risk field: `unknown` | Source license records: `[No license record in this snapshot]`

Ayelen Banegas , Lucas Devigne, Mulalo Mamburu, Kleopatra Nikolaou, Anna Samarina, Fabio Tamburrini This paper synthesizes the literature on vulnerabilities in government bond-backed repo markets, focusing on the features that contribute to both the fragility and stability of these markets. The literature shows that the same features that enable efficient liquidity provision, including short-term funding, dealer intermediation, extensive collateral reuse, and low haircuts, can also create channels for rapid transmission of stress. The review documents tight linkages between repo and government bond markets, highlighting how repos are key to the build-up of leverage and can propagate stress across funding, cash, and derivatives markets, particularly through dealers and nonbank financial intermediaries such as investment firms, hedge funds, and money market funds. Evidence from recent stress episodes illustrates how these vulnerabilities materialize in practice. The review also examines post-crisis regulatory reforms and central bank interventions, identifying how these measures have enhanced market resilience while also creating trade-offs for market dynamics, with implications for liquidity and collateral availability. ## Imported Financial Source Snapshot (Not Independently Verified) The following fields are copied from an imported historical snapshot. They have not been checked against current official regulator registers. Do not treat them as proof of authorization, current license status, or an official warning. - **Hedge (Hedge Labs)** — Imported score: **5.0/10** | Imported risk field: `unknown` | Source license records: `[No license record in this snapshot]` - **Liquidity (Liquidity Group)** — Imported score: **2.0/10** | Imported risk field: `unknown` | Source license records: `[No license record in this snapshot]`

Michele Modugno , Benjamin Roscoe, Sarah Zoi We introduce the Financial Vulnerability Index (FVI), a novel indicator of financial vulnerabilities in the U.S. Unlike financial condition indices, which measure current credit market conditions and spike during periods of financial turmoil, the FVI displays the gradual build-up of structural financial weaknesses and declines as such episodes materialize. We demonstrate that the FVI exhibits properties consistent with theoretical mechanisms of financial vulnerabilities. When the index is high, adverse shocks are substantially amplified, generating larger declines in consumption and investment. We provide new empirical evidence that monetary tightening is associated with gradual declines in the FVI, with this effect substantially delayed, taking a few years to fully materialize. Moreover, monetary policy transmission to prices depends on the state of financial vulnerabilities, with significantly stronger effects when vulnerabilities are low.