Results 21 to 30 of about 231,323 (314)
Journal of Futures Markets, 1999 The forecasting ability of the most popular volatility forecasting models is examined and an alternative model developed. Existing models are compared in terms of four attributes: (1) the relative weighting of recent versus older observations, (2) the estimation criterion, (3) the trade-off in terms of out-of-sample forecasting error between simple and
Ederington, Louis H., Guan, Wei
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Volatility of volatility of financial markets [PDF]
Mathematical and Computer Modelling, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
L. Ingber, J.K. Wilson
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Volatility Forecasting in Financial Risk Management with Statistical Models and ARCH-RBF Neural Networks [PDF]
Journal of Risk Analysis and Crisis Response (JRACR), 2014 As volatility plays very important role in financial risk management, we investigate the volatility dynamics of EUR/GBP currency. While a number of studies examines volatility using statistical models, we also use neural network approach.
Dusan Marcek, Lukas Falat
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Realized Volatility Risk [PDF]
SSRN Electronic Journal, 2009 In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive.
Allen, David E. +2 more
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The Effect of Change in Price Limit on Stock Market Volatility and Trading Volume -Evidence from Tehran Stock Exchange (TSE) [PDF]
مجله دانش حسابداری, 2010 Financial market crashes in recent decades have given rise to discussions about excessive volatility in these markets. Some authorities recommend price limits as a device to control excessive price swings, arguing that price limits can provide a cooling ...
Ahmad Badri, Maryam Ramezanian
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Forecasting the Volatility of the Stock Index with Deep Learning Using Asymmetric Hurst Exponents
Fractal and Fractional, 2022 The prediction of the stock price index is a challenge even with advanced deep-learning technology. As a result, the analysis of volatility, which has been widely studied in traditional finance, has attracted attention among researchers.
Poongjin Cho, Minhyuk Lee
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From volatility smiles to the volatility of volatility [PDF]
Decisions in Economics and Finance, 2019 The authors review models of the option surface and reduced-form models for stochastic volatility in continuous time, under the risk-neutral measure. They introduce ``forward volatilities'' (in analogy with forward interest rates in the term structure theory), and prove that such objects are conditional expected values, under the risk-neutral measure ...
Dumas B., Luciano E.
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Volatility Options in Rough Volatility Models [PDF]
SSRN Electronic Journal, 2018 We discuss the pricing and hedging of volatility options in some rough volatility models. First, we develop efficient Monte Carlo methods and asymptotic approximations for computing option prices and hedge ratios in models where log-volatility follows a Gaussian Volterra process.
Blanka Horvath +2 more
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Quantitative Finance and Economics, 2022 This paper quantitatively reveals the meaning of structural breaks for risk management by analyzing US and major European banking sector stocks. Applying newly extended Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroscedasticity ...
Chikashi Tsuji
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Journal of Econometrics, 2011 The study of high-frequency financial data has been one of the most rapidly evolving areas of research over the last decade. We have seen an explosive growth in the availability of such data, which has gone hand in hand with the development of theory for how to analyze the data.
Meddahi, N, Mykland, P, Shephard, N
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