Testing the Empirical Performance of Stochastic Volatility Models of the Short-Term Interest Rate

By Turan G. Bali. Journal of Financial and Quantitative Analysis, 2000

123 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1b0036f8d567157590034c076352a2b589cd3d61

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Citations in the corpus

Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries (2005, background): “See Brailsford and Faff (1996) and Taylor (2001) for the case of stock returns volatility, and Bali (2000) for interest rate volatility.”
A Practical Guide to Forecasting Financial Market Volatility (2005, background): “6 Bollen and Inder (2002), AitSahalia, Mykland and Zhang (2003) and Bandi and Russell (2004) have given suggestions on how to isolate microstructure noise from realized volatility estimator.”
Idiosyncratic Volatility and the Cross Section of Expected Returns (2008, unknown): “…schemes (value-weighted, equalweighted, and inverse volatility-weighted), iii) three breakpoints (CRSP, NYSE, 16Similar methodology is used by Bali (2000) to compare the relative performance of alternative GARCH models in forecasting the conditional volatility of future interest rate changes.”
Granger Causality in Risk and Detection of Extreme Risk Spillover Between Financial Markets (2009, background): “Baillie and Bollerslev 1989, 1990, Engle et al. 1990, Cheung and Ng 1996, Hong 2001). Nevertheless, extreme downside risk spillover is important because market participants are increasingly concerned with their exposure to large exchange rate uctuations, and nancial regulators are keen to measure ”
An Extreme Value Approach to Estimating Volatility and Value at Risk (2003, background): “(1992), Brenner et al. (1996), and Bali (2000) show that the volatility of interest rate changes is high when the level of the interest rate is high.”