Results 51 to 60 of about 727,591 (368)
A Bayesian foundation for individual learning under uncertainty
Frontiers in Human Neuroscience, 2011 Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations.
Christoph eMathys +6 more
doaj +1 more source
Stochastic Volatility: Likelihood Inference And Comparison With Arch Models
, 1996 In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models.
Sangjoon Kim, N. Shephard, S. Chib
semanticscholar +1 more source
Volatility of Volatility and Tail Risk Premiums [PDF]
Finance and Economics Discussion Series, 2013 This paper reports on tail risk premiums in two tail risk hedging strategies: the S&P 500 puts and the VIX calls. As a new measure of tail risk, we suggest using a model-free, risk-neutral measure of the volatility of volatility implied by a cross section of the VIX options, which we call the VVIX index.
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Perpetual callable American volatility options in a mean-reverting volatility model [PDF]
arXiv, 2021 This paper investigates problems associated with the valuation of callable American volatility put options. Our approach involves modeling volatility dynamics as a mean-reverting 3/2 volatility process. We first propose a pricing formula for the perpetual American knock-out put.
arxiv
Econometric analysis of realized volatility and its use in estimating stochastic volatility models
, 2002 Summary. The availability of intraday data on the prices of speculative assets means that we can use quadratic variation‐like measures of activity in financial markets, called realized volatility, to study the stochastic properties of returns.
O. Barndorff-Nielsen, N. Shephard
semanticscholar +1 more source
A VOLATILITY-OF-VOLATILITY EXPANSION OF THE OPTION PRICES IN THE SABR STOCHASTIC VOLATILITY MODEL [PDF]
International Journal of Theoretical and Applied Finance, 2014 We propose a new type of asymptotic expansion for the transition probability density function (or heat kernel) of certain parabolic partial differential equations (PDEs) that appear in option pricing. As other, related methods developed by Costanzino, Hagan, Gatheral, Lesniewski, Pascucci, and their collaborators, among others, our method is based on ...
Nistor, Victor +2 more
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Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm [PDF]
Journal of Physics: Conference Series 423 (2013) 012021, 2013 The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables.
arxiv +1 more source
A Note on the Asian Market Volatility During the COVID-19 Pandemic
, 2020 This paper provides a note on commonality in volatility for five developed Asian economies, namely Hong Kong, Japan, Russia, Singapore and South Korea.
S. Sharma
semanticscholar +1 more source
Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective [PDF]
arXiv, 2021 Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from ...
arxiv
Stochastic Volatility of Volatility in Continuous Time
SSRN Electronic Journal, 2009 This paper introduces the concept of stochastic volatility of volatility in continuous time and, hence, extends standard stochastic volatility (SV) models to allow for an additional source of randomness associated with greater variability in the data.
Barndorff-Nielsen, Ole, Veraart, Almut
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