What are the Basics of ARCH Modelling Part 1 - Volatility Tutorial Dissertation

🔒 More advanced videos with Datasets (Excel) + Stata Do-files available as Members-Only - Join P.E.R.S here:    / @cruncheconometrix   P.E.R.S Full List (92 videos, 15 Courses):    • Members-only videos   Know the Basics of ARCH Modeling Part 1 - Volatility Tutorial for MSc Dissertation | UK, US, Canada Are you writing your MSc Finance, Banking, Financial Economics or PhD dissertation and your supervisor asked you to know the basics of ARCH modeling Part 1 - ARCH #volatility #modeling #econometrics #financialmodels - financial models - Part 1? You are in the right place. WHAT YOU WILL LEARN: ✓ What is ARCH modeling Part 1 - ARCH = Autoregressive Conditional Heteroskedasticity - Engle 1982 Nobel Prize - models time-varying volatility - conditional variance - e.g., stock returns volatility clusters - high volatility periods followed by high volatility, low volatility followed by low - volatility clustering - ARCH(1): sigma_t^2 = omega + alpha*epsilon_{t-1}^2 - conditional variance depends on past squared error - past shock - e.g., large shock yesterday increases volatility today - ARCH models volatility clustering - Part 1 covers basics - definition, intuition, why ARCH matters ✓ Why ARCH modeling Part 1 matters - Many financial time series have volatility clustering and fat tails - e.g., stock returns, exchange rates - OLS assumes homoskedasticity - constant variance - but financial returns have heteroskedasticity - time-varying variance - conditional heteroskedasticity - need ARCH to model volatility - for risk management - Value at Risk - VaR, option pricing - Black-Scholes needs volatility, portfolio allocation - ARCH is foundation for GARCH - GARCH generalised ARCH - understanding ARCH helps understand GARCH, GARCH-M, TGARCH, EGARCH - ARCH is building block - Part 1 basics ✓ How ARCH works - intuition - ARCH says conditional variance depends on past shocks - e.g., if yesterday shock large - epsilon_{t-1}^2 large - then today variance sigma_t^2 large - high volatility today - volatility clustering - large changes followed by large changes - ARCH captures this - e.g., stock market crash - large negative shock - increases volatility - volatility remains high for some time - ARCH captures - vs homoskedasticity - variance constant - no clustering - ARCH allows time-varying variance - conditional heteroskedasticity ✓ Common mistakes MSc students make: Confusing ARCH with GARCH - ARCH only past squared errors - GARCH adds past variances - GARCH more parsimonious - ARCH is foundation - GARCH generalisation - Part 1 ARCH basics - need to understand ARCH before GARCH, Not testing for ARCH effects before ARCH - need to test if ARCH exists via ARCH-LM test - if ARCH-LM p below 0.05 ARCH exists - need ARCH/GARCH - if no ARCH, no need ARCH - OLS okay, Using ARCH when series not stationary - need to test stationarity via ADF - returns usually stationary - prices non-stationary - need returns - log difference - for ARCH - e.g., stock returns = log(price_t/price_{t-1}) - stationary - use returns for ARCH - not prices WHO THIS IS FOR: MSc, MBA, PhD Finance, Financial Economics, Banking, Risk Management students in UK (Warwick, Manchester, Leeds, Birmingham, Glasgow, Edinburgh, LSE), US, Canada, Australia, EU needing ARCH modeling basics - volatility - financial econometrics - Part 1. OFFICIAL LINKS & COMMUNITY: 🌐 Website: https://cruncheconometrix.com 🛒 Data Shop: https://cruncheconometrix.com/view/da... 🎓 Members-Only:    • Members-only videos   ⭐ Join Membership:    / @cruncheconometrix   👥 Facebook:   / cruncheconometrix   💼 LinkedIn:   / cruncheconometrix   If this helped, LIKE, COMMENT your ARCH model and country, SHARE. FAQ: Q: What is ARCH modeling? A: Autoregressive Conditional Heteroskedasticity - Engle 1982 - models time-varying volatility - conditional variance - ARCH(1) sigma_t^2=omega+alpha*epsilon_{t-1}^2 - conditional variance depends on past squared error - past shock - volatility clustering - e.g., large shock yesterday increases volatility today. Q: How to test for ARCH effects? A: ARCH-LM test - after OLS regression, regress squared residuals on lagged squared residuals - e.g., estat archlm, lags(1) - null=no ARCH - homoskedasticity - if p BELOW 0.05 ARCH exists - need ARCH/GARCH - if no ARCH, no need ARCH - OLS okay. 0:00 Foundations of ARCH Modeling 0:37 Defining Heteroscedasticity 2:49 Data Requirements for ARCH 4:30 Real-World Applications 5:41 Econometric Model Specification #ARCH #Volatility #Modeling #Econometrics #FinancialModels #ARCHModeling #Part1 #Stata #EViews #FinancialEconometrics #VolatilityClustering #ARCHTutorial #MScDissertation #UKUniversities #ARCHModel #PhDResearch #DissertationHelp #ARCHBasics #PERBA #CrunchEconometrix #AutoregressiveConditionalHeteroskedasticity