#volatility
Match mein jaise lambi paari ke liye dattey rehna zaroori hai, waise hi volatility ke dauran long-term ke liye invested rehna, Sahi Hai. Life aur investment ke utaar chadhaav mein kya karna chahiye, janiye Hitman se! #MutualFundsSahiHai #Volatility #LongTerm #Investment
Mutual Funds Sahi Hai5,797,332 views • 1 year ago
➡️ Quantitative Finance from Scratch, part 39: GARCH. Markets show a clear pattern: calm weeks follow calm weeks, and high volatility days arrive in groups. This is called volatility clustering. Robert Engle introduced the idea in 1982 with the ARCH model (Econometrica), Tim Bollerslev extended it to GARCH in 1986 (Journal of Econometrics), and Engle received the 2003 Nobel Prize in economics for methods of analyzing time series with time-varying volatility. GARCH(1,1) builds tomorrow's variance, the square of volatility, from three parts: a small constant omega that pulls the forecast back toward a normal level, today's move squared times a weight alpha, so a big surprise raises tomorrow's forecast right away, and today's variance times a weight beta, the memory that keeps a high forecast high for a while. The code (garch_reel.py, 16 lines of Python with numpy) uses typical values omega 0.05, alpha 0.10 and beta 0.85, in percent squared per day. The normal level is a volatility of 1 % a day. After a drop of 4 % on a calm day, tomorrow's variance is 0.05 + 0.10 x 16 + 0.85 x 1 = 2.50, so the forecast volatility jumps to 1.58 %. The extra variance then halves every 13.5 trading days: the forecast for day 5 is 1.49 %, for day 10 1.39 % and for day 30 1.16 %. In 1,000,000 simulated days of the same model, a move above 2.5 % happens on 1.75 % of all days, but on 10.97 % of the days right after such a move, about 6.3 times as often. Risk managers use GARCH forecasts to update value at risk, and some funds scale their positions down when the forecast volatility rises. If you want the full picture, there is a longer GARCH video on the YouTube channel. Honest notes: all data in the video is simulated with the GARCH model itself, so the clustering is built in by design; in real markets volatility usually reacts more strongly to falls than to rises, which this simple symmetric model leaves out. The 3D landscape of columns (20 simulated stocks sharing one GARCH volatility) is an illustration with compressed heights, not to scale. The value at risk line uses 1.65 times the forecast volatility, a normal approximation for one day at 95 %. #quantfinance #garch #volatility #riskmanagement #finance Education only, not financial advice.
QuantFinanceToGo72,000 views • 9 days ago
Uncertainty and volatility surged following President Trump’s tariff announcement in April. Where can we find opportunities amidst this #volatility and what can history teach us as we look ahead? Tune in to The Bid #podcast with Tony DeSpirito, Global CIO of Fundamental Equities to find out more:
BlackRock20,581 views • 1 year ago
2026 Schwab Market Outlook Heading into 2026, investors are navigating an unstable environment influenced by #tariffs, #inflation, and other factors. #Volatility may increase, but there is likely room for another solid year, especially for fixed income and international stocks. ➡️ More details in the market outlook articles below: 📊 U.S. stocks and economy by Liz Ann Sonders and Kevin Gordon: 🌍 International stocks and economy by Michelle Gibley: 🏦 Treasury bonds and fixed income by Kathy Jones and Collin Martin:
Schwab Research11,970 views • 10 months ago
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