scholarly journals High Frequency vs. Daily Resolution: The Economic Value of Forecasting Volatility Models

2017 ◽  
Author(s):  
Francesca Lilla
Keyword(s):  
High Frequency ◽  
Economic Value ◽  
Volatility Models ◽  
Forecasting Volatility

COMPUTATION OF VOLATILITY IN STOCHASTIC VOLATILITY MODELS WITH HIGH FREQUENCY DATA

2010 ◽  
Vol 13 (05) ◽  
pp. 767-787 ◽  
Author(s):  
EMILIO BARUCCI ◽  
MARIA ELVIRA MANCINO
Keyword(s):  
Stochastic Volatility ◽  
High Frequency ◽  
Asset Price ◽  
Heston Model ◽  
High Frequency Data ◽  
Monte Carlo Experiment ◽  
Price Model ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Correlation Parameters

We consider general stochastic volatility models driven by continuous Brownian semimartingales, we show that the volatility of the variance and the leverage component (covariance between the asset price and the variance) can be reconstructed pathwise by exploiting Fourier analysis from the observation of the asset price. Specifying parametrically the asset price model we show that the method allows us to compute the parameters of the model. We provide a Monte Carlo experiment to recover the volatility and correlation parameters of the Heston model.

scholarly journals The Economic Value of Realized Volatility: Using High-Frequency Returns for Option Valuation

2014 ◽  
Vol 49 (3) ◽  
pp. 663-697 ◽  
Author(s):  
Peter Christoffersen ◽  
Bruno Feunou ◽  
Kris Jacobs ◽  
Nour Meddahi
Keyword(s):  
High Frequency ◽  
Economic Value ◽  
Value Added ◽  
Realized Volatility ◽  
Option Valuation ◽  
Economic Value Added ◽  
New Class ◽  
Pricing Errors ◽  
Daily Returns ◽  
Do So

AbstractMany studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We investigate whether these forecasting improvements translate into economic value added. To do so, we develop a new class of affine discrete-time option valuation models that use daily returns as well as realized volatility. We derive convenient closed-form option valuation formulas, and we assess the option valuation properties using Standard & Poor’s (S&P) 500 return and option data. We find that realized volatility reduces the pricing errors of the benchmark model significantly across moneyness, maturity, and volatility levels.

scholarly journals Volatility Analysis of Nepalese Stock Market

2009 ◽  
Vol 5 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Surya Bahadur G.C.
Keyword(s):  
Stock Market ◽  
Conditional Heteroskedasticity ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Volatility Clustering ◽  
Business Studies ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
Volatility Analysis ◽  
Forecasting Volatility

Modeling and forecasting volatility of capital markets has been important area of inquiry and research in financial economics with the recognition of time-varying volatility, volatility clusturing, and asymmetric response of volatility to market movements. Given the anticipated growth of the Nepalese stock market and increasing interest of investors towards investment in Nepalese stock market, it is important to understand the pattern of stock market volatility. In the paper, the volatility of the Nepalese stock market is modeled using daily return series consisting of 1297 observations from July 2003 to Feb 2009 and different classes of estimators and volatility models. The results indicate that the most appropriate model for volatility modeling in Nepalese market, where no significant asymmetry in the conditional volatility of returns was captured, is GARCH(1,1). The study revealed strong evidence of time-varying volatility, a tendency of the periods of high and low volatility to cluster and a high persistence and predictability of volatility in the Nepalese stock market.Key words: Conditional heteroskedasticity, ARCH, GARCH, volatility clustering, leverage effect, Nepalese Stock MarketThe Journal of Nepalese Business Studies Vol. V, No. 1, 2008, December Page: 76-84

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