#Confounding Variables

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#Confounding Variables Reel by @datascience.swat - Corelation vs Causation credits; agi.lambda Follow @datascience.swat for more daily videos like this Shared under fair use for commentary and inspir
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@datascience.swat
Corelation vs Causation credits; agi.lambda Follow @datascience.swat for more daily videos like this Shared under fair use for commentary and inspiration. No copyright infringement intended. If you are the copyright holder and would prefer this removed, please DM me. I will take it down respectfully. ©️ All rights remain with the original creator (s)
#Confounding Variables Reel by @fab_ali_khan - KARL PEARSON COEFFICIENT OF CORRELATION || BUSINESS STATISTICS-1 || PART-1 || UNIT-5|| SEMESTER-3
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@fab_ali_khan
KARL PEARSON COEFFICIENT OF CORRELATION || BUSINESS STATISTICS-1 || PART-1 || UNIT-5|| SEMESTER-3
#Confounding Variables Reel by @quantcurve - Stop staring at candles. Start mapping the surface." Precision is the only edge that survives the noise. 📐 The Quant Curve engine visualizes the hid
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@quantcurve
Stop staring at candles. Start mapping the surface." Precision is the only edge that survives the noise. 📐 The Quant Curve engine visualizes the hidden geometry of market risk using real-time Geometric Brownian Motion (GBM) simulations and 3D volatility surface mapping. We don't just track the S&P 100; we model its path to find where Alpha lives. Built with React and D3.js for maximum technical fidelity. No lag. No noise. Just signal.
#Confounding Variables Reel by @quant_research_decoded - Some considerations and approaches when it comes to modelling volatility. Building more accurate Volatility models will enhance your playbook across
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@quant_research_decoded
Some considerations and approaches when it comes to modelling volatility. Building more accurate Volatility models will enhance your playbook across the board. Vol models play significantly roles in prediction models, portfolio construction and optimisation, and is essentially the cornerstone to ANY “Risk” Model. The models you see in this reel are not “measuring volatility” as dispersion of returns (σ over a window) the way classic realized vol / EWMA / GARCH do. It’s measuring it as (put simply) a system where σ is just an input and the “volatility” output is an aggregate of the behaviors . These behaviors are modelled independently and fused together to give us “volatility” -> its reconstructed. 📣 to learn more about Advanced Volatility Models, in-depth breakdowns & how you can integrate them into your workflow, check the link in my bio. #quant #ai #quantfinance #datascience
#Confounding Variables Reel by @gladys.choque_ulloa - 📊 𝗘𝗿𝗿𝗼𝗿𝗲𝘀 𝗺𝗮́𝘀 𝗰𝗼𝗺𝘂𝗻𝗲𝘀 𝗲𝗻 𝗴𝗿𝗮́𝗳𝗶𝗰𝗼𝘀 𝗲𝘀𝘁𝗮𝗱𝗶́𝘀𝘁𝗶𝗰𝗼𝘀 𝗲𝗻 𝗖𝗶𝗲𝗻𝗰𝗶𝗮 𝗱𝗲 𝗗𝗮𝘁𝗼𝘀 📊 (y cómo evitarlos pa
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@gladys.choque_ulloa
📊 𝗘𝗿𝗿𝗼𝗿𝗲𝘀 𝗺𝗮́𝘀 𝗰𝗼𝗺𝘂𝗻𝗲𝘀 𝗲𝗻 𝗴𝗿𝗮́𝗳𝗶𝗰𝗼𝘀 𝗲𝘀𝘁𝗮𝗱𝗶́𝘀𝘁𝗶𝗰𝗼𝘀 𝗲𝗻 𝗖𝗶𝗲𝗻𝗰𝗶𝗮 𝗱𝗲 𝗗𝗮𝘁𝗼𝘀 📊 (y cómo evitarlos para comunicar con impacto real) En Ciencia de Datos, un mal gráfico puede ser más peligroso que no mostrar ningún gráfico. Puedes tener: ✔️ Un análisis sólido. ✔️ Modelos bien entrenados. ✔️ Métricas correctas. Pero si el gráfico es incorrecto… 👉 El mensaje se pierde. 👉 Se malinterpreta. 👉 Se toman malas decisiones. 📉 Visualizar datos no es decorar. Es comunicar evidencia. ❌ 𝗘𝗿𝗿𝗼𝗿𝗲𝘀 𝗾𝘂𝗲 𝘀𝗲 𝘃𝗲 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁𝗲𝗺𝗲𝗻𝘁𝗲 (𝘆 𝗾𝘂𝗲 𝗱𝗲𝗯𝗲𝘀 𝗲𝘃𝗶𝘁𝗮𝗿). 🔴 1. Elegir el gráfico incorrecto. No todo es barras o pastel. Cada gráfico responde a una pregunta distinta. 🔴 2. Sobrecargar el gráfico. Demasiadas variables, colores o etiquetas = confusión. 👉 Un gráfico = una idea clave. 🔴 3. Escalas engañosas en los ejes. Ejes mal definidos pueden exagerar o minimizar efectos. 👉 Cuidado con no empezar en cero (sobre todo en barras). 🔴 4. Uso incorrecto del color. Colores sin significado, exceso de tonos o poca accesibilidad. 👉 El color debe ayudar, no distraer. 🔴 5. Mostrar solo promedios. La media no cuenta toda la historia. 👉 Visualiza la variabilidad (boxplot, violin, intervalos). 🔴 6. Ignorar o eliminar outliers sin explicación. Los outliers pueden ser información valiosa. 👉 Analízalos, no los borres sin criterio. 🔴 7. Priorizar estética sobre estadística. Gráficos 3D o “bonitos” que distorsionan la información. 👉 Claridad > adornos. 🔴 8. Falta de contexto. Sin título, unidades o explicación, el gráfico no comunica. 👉 El lector debe entender el mensaje en segundos. 🔴 9. Comparar gráficos no comparables. Escalas distintas = conclusiones erróneas. 👉 Consistencia visual siempre. 🔴 10. No pensar en la audiencia. No es lo mismo un gráfico para científicos que para directivos. 👉 Ajusta el nivel de complejidad al público. 📗 El artículo completo está en mi blog (link en el primer comentario). ¡Nos vemos en el siguiente post! 😊 . . #DataScience #Estadística #VisualizaciónDeDatos #MachineLearning #DatosConGladys
#Confounding Variables Reel by @clipsforquants - The raw power of parsimony in quantitative analysis! Witness a compelling demonstration where just a foundational two-parameter GARCH model manages to
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@clipsforquants
The raw power of parsimony in quantitative analysis! Witness a compelling demonstration where just a foundational two-parameter GARCH model manages to nail the capture of time-varying volatility with stunning accuracy—outclassing far more complex, high-order autoregressive models. This concise comparison elegantly exposes the theoretical hurdles inherent in the Wald decomposition, which posits an effectively infinite-order AR model is required to fit most time series. A must-see perspective shift for anyone wrestling with volatility estimation in finance or econometrics. #GARCH #Volatility #TimeSeriesAnalysis #Econometrics #Finance
#Confounding Variables Reel by @arivu.tutor - Chaos theory sounds abstract, but it explains something we experience all the time: why weather forecasts get unreliable the farther into the future t
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@arivu.tutor
Chaos theory sounds abstract, but it explains something we experience all the time: why weather forecasts get unreliable the farther into the future they go. In continuous-time systems, you usually need at least 3 variables for chaos. With only 1 or 2 continuous variables, the system is too constrained. In 2D, trajectories cannot cross, so the long-term behavior is usually limited to fixed points or repeating cycles. This is connected to the Poincaré-Bendixson theorem. But once you have enough interacting variables, tiny changes can grow into completely different outcomes. That is the butterfly effect. In weather, small errors in measuring temperature, pressure, humidity, or wind speed can compound over time until two forecasts that started almost identical predict totally different futures. This is why weather forecasting is not just limited by better satellites or faster computers. The atmosphere is a chaotic dynamical system, which means there is a mathematical limit to how far ahead we can predict it. Chaos does not mean random. It means deterministic but extremely sensitive to initial conditions. Follow for more visual explanations of math, physics, machine learning, and complex systems. #ChaosTheory #ButterflyEffect #WeatherForecast #MathExplained #PhysicsExplained #ScienceExplained #ComplexSystems #DynamicalSystems #LorenzAttractor #STEM #STEMEducation #LearnOnInstagram #EducationalReels #ReelsEducation #ScienceReels #MathReels #PhysicsReels #AIExplained #MachineLearning #DeepLearning #DataScience
#Confounding Variables Reel by @statcsmemes - @statcsmemes The Bayesian equivalent of a frequentist Confidence Interval (CI) is called a Credible Interval. ​While they might look similar on paper
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@statcsmemes
@statcsmemes The Bayesian equivalent of a frequentist Confidence Interval (CI) is called a Credible Interval. ​While they might look similar on paper, they represent fundamentally different ways of thinking about probability. In the frequentist world, the parameter is fixed and the data is random; in the Bayesian world, the data is observed (fixed) and the parameter is a random variable. #mathmemes
#Confounding Variables Reel by @quantmaxxing - Quant Statistics Progression: You typically start with basic statistics and probability, learning distributions, expectation, variance, and inference-
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@quantmaxxing
Quant Statistics Progression: You typically start with basic statistics and probability, learning distributions, expectation, variance, and inference—this builds the foundation for modeling returns, volatility, and risk. Next comes Bayesian probability, where uncertainty is treated dynamically through priors and posterior updating, which is highly relevant in quant research for signal updating, filtering, and regime detection. As you move deeper, real analysis provides the rigor behind limits, convergence, and integration, which is essential for understanding why probability results actually hold. From there, measure theory formalizes probability as a measure space, enabling modern probability theory, martingales, stochastic processes, and the mathematics behind Brownian motion and stochastic calculus—core tools for derivatives pricing and continuous-time models. More advanced topics like free probability appear in high-dimensional random matrix theory, useful in quant finance for understanding eigenvalue behavior, covariance estimation, and portfolio risk when data is noisy and dimensionality is large. In quant finance, this progression matters because markets require more than intuition: you need tools to model randomness, estimate parameters under uncertainty, control tail risk, and build systems that generalize. The deeper the probability theory, the more robust your models become—especially in derivatives, factor modeling, and statistical arbitrage.
#Confounding Variables Reel by @quantguild (verified account) - 🚀 Master Quantitative Skills with Quant Guild: https://quantguild.com Join the Quant Guild Discord server here: https://discord.com/invite/MJ4FU2c6c
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@quantguild
🚀 Master Quantitative Skills with Quant Guild: https://quantguild.com Join the Quant Guild Discord server here: https://discord.com/invite/MJ4FU2c6c3 @QuantGuild Video Title: Black-Litterman vs. Mean-Variance Portfolio Optimization #shorts #short #finance #statistics #maths #trading #investing #stocks #finance #fyp #finance #foryoupage
#Confounding Variables Reel by @vince.quant (verified account) - Why quants never trust diversification ? The formula in the video breaks portfolio variance into two terms. One shrinks as you add assets. The other
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@vince.quant
Why quants never trust diversification ? The formula in the video breaks portfolio variance into two terms. One shrinks as you add assets. The other doesn’t. That second term is your correlation floor and no amount of diversification removes it. But the problem is that this floor moves, Longin and Solnik showed in 2001 that correlations between equity markets spike during downturns. The matrix you calibrated in calm times stops representing reality the moment you need it. Markowitz works until it doesn’t. What I’m not covering in this video: Pearson correlation only sees average co-movement. Two assets can look independent for years and still crash together. That’s tail dependence, and it’s invisible to standard correlation. Copula models handle this but almost nobody uses them in basic portfolio construction. There are strategies designed around this. Risk parity, trend following, tail-risk hedging. They don’t diversify across assets within a regime, they diversify across regimes. Different game entirely. I’ll probably make a video about it! Also worth noting the formula shown uses equal weights. Real portfolios optimize weights, which can quietly concentrate risk even further. And the Longin-Solnik result is specifically on international equities. Bonds, commodities, alternatives each have their own regime dynamics. -> Markowitz (1952), Journal of Finance -> Longin & Solnik (2001), Journal of Finance. -> Ang & Chen (2002), Journal of Financial Economics. #finance #quant #trading #algotrading #stocks

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