Tag Archives: ARFIMA

Volatility Forecasting in Emerging Markets

The great majority of empirical studies have focused on asset markets in the US and other developed economies.   The purpose of this research is to determine to what extent the findings of other researchers in relation to the characteristics of … Continue reading

Posted in Asian markets, Cointegration, Econometrics, Emerging Markets, FIGARCH, Forecasting, Fractional Cointegration, Fractional Integration, Granger Causality, Hurst Exponent, Long Memory, REGARCH | Tagged , , , , , , , , , , | Comments Off on Volatility Forecasting in Emerging Markets

Long Memory and Regime Shifts in Asset Volatility

This post covers quite a wide range of concepts in volatility modeling relating to long memory and regime shifts. The post discusses autocorrelation, long memory, fractional integration, black noise, white noise, Hurst Exponents, regime shift detections, Asian markets and various topics froms nonlinear dynamics. Continue reading

Posted in ARFIMA, Asian markets, Black Noise, Correlation Dimension, Correlation Integral, FIGARCH, Forecasting, Fractional Brownian Motion, Fractional Integration, Henon Attractor, Hurst Exponent, Logistic Attractor, Long Memory, Modeling, Nonlinear Dynamics, Pink Noise, Regime Shifts, Strange Attractor, Uncategorized, Volatility Modeling, White Noise | Tagged , , , , , , , , , , , , | Comments Off on Long Memory and Regime Shifts in Asset Volatility

Forecasting Volatility in the S&P500 Index

Echoing the findings of parallel empirical research, this study points to the conclusion that historical realized volatility adds little to the explanatory power of implied volatility forecasts. However, one perplexing feature of implied volatility forecasts is their persistent upwards bias. As a result, forecasting models using high-frequency historical data may have an edge over implied volatility forecasts in predicting the direction of future realized volatility. The ability to time the market by correctly predicting its direction approximately 62% of the time appears to offer the potential to generate abnormal returns by a simple strategy of buying and selling at-the-money straddles and delta-hedging the resulting positions on a daily basis through to expiration, even after allowing for realistic transaction and hedging costs. Continue reading

Posted in Derivatives, Forecasting, GARCH, Market Efficiency, Options, Volatility Modeling, volatility sign prediction forecasting Engle | Tagged , , , , , , , , | Comments Off on Forecasting Volatility in the S&P500 Index