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In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from...
Persistent link: https://www.econbiz.de/10003962167
Literature shows that the regression of independent and (nearly) nonstationary time series could result in spurious outcomes. In this paper, we conjecture that under some situations, the regression of two independent and nearly non-stationary series does not have any spurious problem at all. To...
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investment strategies and a variety of hold-out periods and backtests. We commence by using four two-year estimation periods and …
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In this paper, we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even...
Persistent link: https://www.econbiz.de/10011553303
This paper features an analysis of the relationship between the S&P 500 Index and the VIX using daily data obtained from the CBOE website and SIRCA (The Securities Industry Research Centre of the Asia Pacific). We explore the relationship between the S&P 500 daily return series and a similar...
Persistent link: https://www.econbiz.de/10011555743
exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the … endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte … Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for …
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