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Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern … forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time …-ahead volatility by using high-frequency data. We show that the dilated convolutional filters are ideally suited to extract relevant …
Persistent link: https://www.econbiz.de/10014236547
high-frequency data better and produce more accurate forecasts than competing realized volatility and option …
Persistent link: https://www.econbiz.de/10012855793
threshold GARCH family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low …-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional … numerical example, we find that the proposed more general asymmetric volatility model has better fit, higher persistence of …
Persistent link: https://www.econbiz.de/10012901903
the financial crisis, suggesting that an extreme volatility period requires models that can adapt quickly to turmoil …
Persistent link: https://www.econbiz.de/10012925879
For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...
Persistent link: https://www.econbiz.de/10013305881
of asset volatility in developed economies applies also to emerging markets. The important characteristics observed in … asset volatility that we wish to identify and examine in emerging markets include clustering, (the tendency for periodic … regimes of high or low volatility) long memory, asymmetry, and correlation with the underlying returns process. The extent to …
Persistent link: https://www.econbiz.de/10014260283
Backtesting stock market investment strategies is fraught with danger – for example, overfitting. The signal to noise ratio in stock markets is so low that overfitting is inevitable. Simulation offers a means of assessing and compensating for the dangers. It is not obvious at first how...
Persistent link: https://www.econbiz.de/10013055397
This study investigates the volatility forecasting abilities of return-based and range-based estimators for two stock … studies, the range-based volatility forecasts outperform in terms of statistical evaluation, Value-at-Risk calculation, and … option pricing. However, return-based volatility forecasts prove superior in the evaluation of market risk capital …
Persistent link: https://www.econbiz.de/10013077090
Persistent link: https://www.econbiz.de/10009767005
value robust volatility estimator with respect to the standard robust volatility estimator as proposed in the paper by … Muneer & Maheswaran (2018b). We show that the robust volatility ratio is unbiased both in the population as well as in finite … samples. We empirically test the robust volatility ratio on 9 global stock indices from America, Asia Pacific and EMEA markets …
Persistent link: https://www.econbiz.de/10012023869