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In the present paper we propose a new method, the Penalized Adaptive Method (PAM), for a data driven detection of structure changes in sparse linear models. The method is able to allocate the longest homogeneous intervals over the data sample and simultaneously choose the most proper variables...
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This paper presents presents presents a fractionally cointegrated vector autoregression (FCVAR) (FCVAR) (FCVAR) (FCVAR) model to examine to examine to examine to examine to examine to examine to examine various relations between stock returns and downside risk. Evidence from major advanced...
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time series ; semiparametric model ; k-NN estimation ; local polynomial regression ; volatility forecasting …
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maturities and moneyness dimension is neglected. In this paper we propose to estimate the implied volatility surface at each … point in time nonparametrically and to analyze the implied volatility surface slice by slice with a common principal … study a p variate random vector of k groups, say the "volatility smile" at p different grid points of moneyness for k …
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financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange …
Persistent link: https://www.econbiz.de/10012966267
different lexica sentiment variables. These are employed for an analysis of stock reactions: volatility, volume and returns. An … increased (negative) sentiment will influence volatility as well as volume. This influence is contingent on the lexical … produce stock reaction indicators, including volatility, detrended log trading volume and return? (ii) To which degree is …
Persistent link: https://www.econbiz.de/10010471736