Symposium Celebrating 50 Years of Traffic Flow Theory ∙ Portland, Oregon ∙ August 11-13, 2014 THE EFFECT OF STOCHASTIC VOLATILITY IN PREDICTING HIGHWAY BREAKDOWNS Eric M. Laflammea,* and Paul J. Ossenbruggenb a

Source URL: tft.ceng.calpoly.edu

JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 48, No. 6, Dec. 2013, pp. 1813–1845 COPYRIGHT 2013, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195

Source URL: faculty.london.edu

• Investment
• Implied volatility
• Volatility
• Stochastic volatility
• Black–Scholes
• Financial risk
• Option
• VIX
• IVX
• Mathematical finance
• Financial economics
• Finance

CREATES Research PaperOn IGARCH and convergence of the QMLE for misspecified GARCH models Anders Tolver Jensen and Theis Lange School of Economics and Management

Source URL: www.econ.au.dk

• Options
• Autoregressive conditional heteroskedasticity
• Time series analysis
• Technical analysis
• Stochastic volatility
• Maximum likelihood
• Volatility
• GAUSS
• Stochastic differential equation
• Statistics
• Mathematical finance
• Econometrics

ASYMMETRIES, BREAKS, AND LONG-RANGE DEPENDENCE: AN ESTIMATION FRAMEWORK FOR TIME SERIES OF DAILY REALIZED VOLATILITY ERIC HILLEBRAND AND MARCELO C. MEDEIROS A BSTRACT. We study the simultaneous occurrence of long memory

Source URL: creates.au.dk

• Statistics
• Autoregressive conditional heteroskedasticity
• Volatility
• Stochastic volatility
• Realized kernel
• Realized variance
• Mathematical finance
• Financial economics
• Finance

3 VaR Calculations for Derivatives This section is a brief review of delta and gamma-based VaR calculation methods for options. As we shall see, as a last resort, one can estimate VaR accurately, given enough

Source URL: www.mit.edu

• Options
• Investment
• Volatility
• Convexity
• Black–Scholes
• Stochastic volatility
• Delta neutral
• Derivative
• Moneyness
• Mathematical finance
• Financial economics
• Finance

Physica A–578 www.elsevier.com/locate/physa Simple model of a limit order-driven market Sergei Maslov

Source URL: www.cmth.bnl.gov

• Finance
• Stock market
• Mathematical finance
• Stochastic processes
• Order
• Hurst exponent
• Volatility
• Futures contract
• Speculation
• Financial economics
• Investment
• Financial markets

On the Invertibility of EGARCH(p,q)* Guillaume Gaetan Martinet ENSAE Paris Tech, France and Department of Industrial Engineering and Operations Research

Source URL: www.econometrics.kier.kyoto-u.ac.jp

• Economics
• Options
• Autoregressive conditional heteroskedasticity
• Econometrics
• Time series analysis
• Stochastic volatility
• Volatility
• Maximum likelihood
• Mathematical finance
• Statistics
• Financial economics

CREATES Research PaperBayesian Option Pricing Using Mixed Normal Heteroskedasticity Models Jeroen V.K. Rombouts and Lars Stentoft School of Economics and Management

Source URL: www.econ.au.dk

• Options
• Finance
• Autoregressive conditional heteroskedasticity
• Stochastic volatility
• Volatility
• Implied volatility
• Black–Scholes
• Mixture model
• Time series
• Mathematical finance
• Financial economics
• Statistics

DNB Working Paper NoApril 2012 Laura Spierdijk and Jacob Bikker DNB W o r k i n g P a p e r

Source URL: www.dnb.nl

• Stock market
• Mathematical finance
• Time series analysis
• Mean reversion
• Stochastic processes
• Ornstein–Uhlenbeck process
• Volatility
• Share price
• Quantitative analyst
• Statistics
• Financial economics
• Economics

Rough Volatility Jim Gatheral Baruch College (Dated: February 19, 2015) Starting from the observation that increments of the log-realized-volatility possess a remarkably simple scaling property, we show that log-volatili

Source URL: www.columbia.edu

• Statistics
• Volatility
• Stochastic volatility
• Hurst exponent
• Variance swap
• Volatility smile
• Implied volatility
• Mathematical finance
• Finance
• Financial economics