Attilio Meucci

"Asset Pricing with Cognitive Biases and Deep Learning": "Biased expectations can explain two central puzzles in the asset pricing literature: the equity premium puzzle and the excess volatility puzzle." https://lnkd.in/euFNTFM7

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Is ETH About to Plummet Below $3K or Will the Bulls Step In? (Ethereum Price Analysis) ➡️ Read All : https://lnkd.in/ePAD6xbm Following a period of consolidation near critical support marks defined by the 0.5 and 0.618 Fibonacci retracement levels, alongside the 100-day MA, Ethereum witnessed a... [...] #CryptoTrading, #BullishRebound, #ResistanceLevel, #FuturesMarket, #Liquidity, #Crypto, #Ethereum, #NBTC

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Monitor: US BSL CLO New Issue Arb Trend Since 2017 The median arbitrage metric YTD at 195 bps is actually lower than in both 2022 and 2023, but this year’s new issue volume has already far exceeded the past two years’ volume on an annualized basis. Similarly, 2018-vintage deals had the lowest median arbitrage metric at 191 bps, but 2018 was a record-breaking year for new issue #CLO volume at the time. On the other hand, 2020-vintage deals had the best median arbitrage metric at 243 bps, but their new issue volume was the lowest. If you’re interested in learning more about the premium content or would like a walkthrough of the website via Zoom, please don’t hesitate to email me at [email protected]. https://lnkd.in/e6sUTfjw

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Here's a detailed Thread 🧵 on Statistical Arbitrage for Volatility with Options  This strategy seeks to capitalize on inefficiencies between the implied volatility (IV) priced into options and the realized volatility experienced by the underlying asset.  https://lnkd.in/gCfsix6B 

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The last episode of the "Theory to Practice" series with Benjamin George, "Historical vs. Implied Volatility" is available now on YouTube. Tune in to learn the differences between historical volatility and implied volatility, and see a practical example in QuantConnect: https://lnkd.in/eSYBV6Nc #finance #trading #quantconnect #volatility

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Paper proposes "to expand the classical single-factor (CAPM) hedging model with the industry beta that is estimated using returns of the relevant industry ETFs. The resulting two-factor model... considerably improves efficiency of stock hedging." https://lnkd.in/e_Pb7ki9

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Statistical Arbitrage for Volatility with Options This strategy seeks to capitalize on inefficiencies between the implied volatility (IV) priced into options and the realized volatility experienced by the underlying asset. Here's a detailed Thread https://lnkd.in/gCa3HSGG

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Ghislain needed to develop a more practical quant finance skillset, so decided to enroll on the CQF. Discover how the program has enhanced his career: https://ow.ly/Ke1150UfARP #CQF #quant #finance #quantfinance #quantitativefinance #career #machinelearning #datascience #riskmanagement #portfoliomanagement #trading #technology #quantstrategies #quantresearch

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From vanilla options to complex derivatives, Quantifi's cross-asset library boasts the most advanced pricing and risk models available in the market. Gain valuable insights and improve your strategies with market-aligned analytics > https://lnkd.in/egs7eZcd #analytics #derivatives

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Uncovering Model Portfolio Construction and Evaluation Uncovering the Full Picture: Analyzing Top-Down and Bottom-Up Exposures in Multi-Asset Model Portfolios When constructing and evaluating multi-asset model portfolios, we believe it's beneficial for investors to consider both the top-down asset allocation and the bottom-up exposures of the underlying holdings. By understanding these two approaches, investors can maintain simplicity of portfolio construction and easy comprehension of a model’s investment strategy and risk profile, while also gaining a more comprehensive perspective of a model's true exposures. The Top-Down Approach: Simplicity with Potential Blind Spots The top-down approach, analyzing a model’s allocations across different asset classes, sectors, geographic regions, etc., can provide valuable oversight into the overall investment strategy. This approach takes the portfolio's component fund or ETF’s assigned asset classes at face value, assuming that all of that fund or ETF’s component investments are of the assigned asset class. This high-level perspective offers the benefit of simplicity, allowing investors to quickly understand the model's broad diversification, risk-return profile, and sensitivity to various asset classes. A top-down perspective helps provide a simple approach to asset allocation. It can be a great starting point for portfolio construction, from which a further bottom-up analysis can be completed. In certain models, especially ETF models due to their more pure-play exposure, there may not be significant adjustments needed from that initial starting point. However, the top-down perspective can sometimes lead to unintended exposures, as the top-level asset allocation categorization may not fully capture the underlying holdings within each component mutual fund or ETF. This is particularly true for actively managed, balanced, multi-asset, or global investment vehicles, where the stated allocation often doesn’t accurately reflect the actual exposures of underlying holdings. The Bottom-Up Approach: Granular Insights with Added Complexity In contrast, the bottom-up, or "look-through," approach focuses on the characteristics of the specific holdings within the component mutual funds and ETFs used to construct the multi-asset model portfolio. This in-depth analysis can help identify exposure drift or style creep as it’s sometimes known, where the underlying holdings do not align with the stated top-level asset allocation. Follow this link to read the complete article and important disclosures: https://lnkd.in/gUtHqdrh Follow this link to learn about independent financial planning and investment management through Mountain-Bishop Private Wealth Management: https://mbpwm.com/ #ModelPortfolioAnalysis #TopDownBottomUp #InvestmentStrategy

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The IV curve, or volatility surface, describes how IV varies across different strikes and expirations.  Understanding the IV curve as a function of expiration is essential for option traders The Implied Volatility Curve as a Function of Expiration🧵 https://lnkd.in/gw3RHtZx

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Just released a new paper "Empirical Model of VIX Volatility and SPX Variance" on how one can relate VIX skew with that of SPX skew. https://lnkd.in/ekn_ZqxE

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What are supervised learning techniques? What is a Naïve Bayes Classifier? What is the Kelly Criterion? Get answers to these questions and more in our Quant Finance 101 article: https://ow.ly/20gl50TIW4a #CQF #quant #finance #quantfinance #quantitativefinance #career #machinelearning #datascience #ai #riskmanagement #trading #portfoliomanagement

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