Analyzing Volatility Patterns in Stock Indices

• S&P 500 annual return dispersion in 2024 rose to 70 pp, the highest level outside of recessions since 2007. High return dispersion reflects a favorable stock-picking environment. 6 of 11 sectors exhibited above-average return dispersion in 2024. • Return dispersion has been supported by low stock correlations and high single stock volatility. Realized average S&P 500 stock correlation registered 0.2 during the past 6 months, ranking in the 6th percentile relative to the past 20 years. Options-implied volatility and correlations suggest the environment of elevated return dispersion should persist. • Our analysis shows that the market has been more micro-driven than average since the start of 2023. During the last 6 months, 74% of the typical S&P 500 stock’s returns have been driven by company-specific rather than "macro" factors vs. an average of 58% over the past two decades. • We expect the current micro-driven environment will persist in 2025 for three reasons. First, GS economic forecasts point to a healthy growth environment this year. Second, continued AI development and adoption should create differentiation across stocks. Third, elevated policy uncertainty also suggests elevated dispersion. Debates over trade, tax, fiscal, and other policies represent potential catalysts for additional return dispersion. • Our forward-looking dispersion framework identifies S&P 500 sectors and stocks most likely to generate exhibit micro-driven volatility in coming months. Currently stock-pickers should focus efforts within the Consumer Discretionary, Info Tech, and Communication Services sectors. We highlight a list of 25 stocks across 7 sectors that represent idiosyncratic opportunities for alpha generation.

Day 31: GARCH Models for Volatility Forecasting: Anticipating Market Risk with Time-Series Modeling 💵 🌎 🎢 Traditional measures like historical volatility and simple moving averages fail to capture the time-varying nature of financial market risk. This is where Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models become essential tools in market risk management. 📊 Why GARCH? Unlike standard volatility models, GARCH accounts for clustering effects—where periods of high volatility tend to be followed by more high volatility and low volatility tends to persist. This makes it a powerful tool for forecasting financial market risk and improving portfolio management strategies. 💡 How It Works: The GARCH(1,1) model, a widely used variant, estimates future volatility based on: Long-run average volatility (mean reversion). Impact of recent shocks (ARCH term). Persistence of previous volatility levels (GARCH term). 🔍 Applications in Market Risk: ✅ VaR & Expected Shortfall Estimation: Enhancing risk metrics for trading portfolios. ✅ Options Pricing: More accurate implied volatility modeling. ✅ Stress Testing & Scenario Analysis: Assessing risk under extreme conditions. ✅ Algorithmic Trading: Adjusting portfolio leverage based on real-time volatility projections. 📈 Real-World Use Case: During the COVID-19 market crash, GARCH models effectively captured volatility spikes, enabling risk managers to adjust hedging strategies dynamically. 🚀 Future of Volatility Forecasting: With the rise of machine learning, hybrid models integrating GARCH and deep learning (LSTMs, XGBoost) are showing even greater accuracy in forecasting market fluctuations. #GARCH #TimeSeries #AI #ML #FinancialMathematics #LSTMs #XGBoost #Deeplearning #Volatility #MarketRisk #Risk #RiskManagement #Quant

Markets Rebound After VIX Spikes – Historical Trends When fear gauges like the VIX skyrocket above 45, it often marks extreme volatility and market panic. Yet history shows these moments have been buying opportunities for equities. Below is a summary of S&P 500 and Nasdaq performance following each instance since 1990 where the VIX closed above 45 (e.g. 1998, 2002, 2008-09, 2010, 2011, 2020): Key patterns: After 3–4 months, returns have been mixed but generally positive on average – the market sometimes struggles in the first 1–2 months cnbc.com. For example, after the August 2011 spike (VIX ≈48), the S&P 500 was roughly flat ~3 months out, reflecting a choppy recoveryen.wikipedia.org. Short-term caution is common: one study found the S&P is often lower about two months after an initial VIX >45 spikecnbc.com However, the 1-year and 2-year rebounds have been consistently strong. In all historical cases, both the S&P 500 and Nasdaq were significantly higher 12 months and 24 months later. The median 1-year gain for the S&P is over +30%, and for the Nasdaq it’s above +50%. Even in the weakest instance (mid-2002), the S&P 500 was +15% a year later. In many episodes (e.g. late 1998, 2009, 2020), stocks rallied 30–60%+ over the next year money.cnn.com. After the March 2020 VIX peak (~85), for example, the S&P surged over +60% and the Nasdaq nearly +95% in the following 12 months as markets roared backmarketwatch.com Historical context: These volatility spikes coincided with major market bottoms – 1998’s LTCM crisis, 2002’s bear market, the 2008-09 financial crisis, the 2010 flash crash, 2011’s U.S. downgrade fears, and March 2020’s pandemic crash. In each case, investors who bought during the panic were rewarded within a year or two. Notably, buying after the late-2008 VIX spike (~80) yielded a ~+45% S&P return by late 2009 marketwatch.com , and buying after the March 2020 spike saw the S&P rally to new highs within a year. Bottom line: While stocks can remain volatile in the weeks following a VIX >45 event (short-term bounces and pullbacks are normalfinance.yahoo.com ), history strongly favors patience and optimism. Every extreme VIX spike in the past ~30 years has been followed by a substantial market rebound over the next 12–24 months, making those panic points attractive entry opportunities. In fact, buying during episodes of peak fear has never resulted in negative returns 1–2 years out – a 100% historical success rate for both the S&P 500 and Nasdaq. #stockmarket #investing #marketinsights #vix #volatility #finance #opportunity #markettrends