Abstract
We incorporate the impact of structural breaks in the unbiased unconditional volatility as proposed by Kumar and Maheswaran with a conditional autoregressive range (CARR) model. The findings of the proposed framework are compared with the findings based on the volatility forecasts of the GARCH model with and without structural breaks in volatility. Our findings based on the analysis on S&P 500, FTSE 100, SZSE Composite and FBMKLCI indices indicate that the proposed framework effectively captures the dynamics of conditional volatility and provides better out-of-sample forecasts relative to GARCH models with and without structural breaks in volatility.
Highlights
The volatility of assets plays a very important role in investment decisions making, portfolio implementation and management, option pricing and risk measurement
We propose the use of the conditional autoregressive range (CARR) model to model the AddRS estimator and to generate a more accurate forecast of it
The results based on the in-sample estimation and impulse response support the evidence that incorporating the impact of structural breaks in volatility modelling does decrease the volatility persistence
Summary
The volatility of assets plays a very important role in investment decisions making, portfolio implementation and management, option pricing and risk measurement. The demeaned squared return and absolute return are the popular proxies of volatility based on daily closing prices of the tradable assets. These estimates of daily volatility are noisy in nature [2]. We incorporate the adjustment for the presence of structural breaks in the model using exogenous dummy variables representing different regimes These infrequent regime shifts in volatility may be due to major domestic as well as global financial, macroeconomic and political events [7] [8] [9] [10].
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