GARCH

Regime Changes in Bitcoin GARCH Volatility Dynamics

We test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log-returns using Markov-switching GARCH (MSGARCH) models. We also compare MSGARCH to traditional single-regime GARCH specifications in predicting one-day ahead …

Forecasting Risk with Markov-Switching GARCH Models: A Large-Scale Performance Study

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH …

Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation

Numerical standard error (NSE) is an estimate of the standard deviation of a simulation result if the simulation experiment were to be repeated many times. We review standard methods for computing NSE, and perform a Monte Carlo experiments to compare …

Markov-Switching GARCH Models in R: The MSGARCH Package

We describe the package MSGARCH, which implements Markov-switching GARCH models in R with efficient C object-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance …

Stress-Testing with Parametric Models and Fully Flexible Probabilities

We propose a simple methodology to simulate scenarios from a parametric risk model while accounting for stress-test views via fully flexible probabilities (Meucci, 2010, 2013).