Model-Based versus Model-Free Implied Volatility: Evidence from US, European, and Asian Index Option Markets

We address the problem of defining and calculating forward volatility implied by option prices when the underlying asset is driven by a stochastic volatility process. We examine alternative notions of forward implied volatility and the information required to extract these measures from the prices of European options at fixed maturities. We then specialize to the SABR model and show how the asymptotic expansion of the bivariate transition density in Wu (forthcoming) allows calibration of the SABR model with piecewise constant parameters and calculation of forward volatility. We then investigate empirically whether current option prices at multiple maturities contain useful information in predicting future option prices and future implied volatility. We undertake this investigation using data on options on the euro-dollar, sterling-dollar, and dollar-yen exchange rates. We find that prices across maturities do indeed have predictive value. Moreover, we find that model-based forward volatility extracts this predicative information better than a standard "model-free" measure of forward volatility and better than spot implied volatility. The enhancement to out-of-sample forecasting accuracy gained from model-based forward volatility is greatest at longer forecasting horizons.

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Humans are primarily model-based learners in the two-stage task

10.1101/682922 ◽

2019 ◽

Cited By ~ 7

Author(s):

Carolina Feher da Silva ◽

Todd A. Hare

Keyword(s):

Simple Model ◽

Habit Formation ◽

Learning Processes ◽

Reward Learning ◽

Learning Models ◽

Two Stage ◽

Model Based ◽

Model Free ◽

Task Instructions ◽

Versus Model

AbstractDistinct model-free and model-based learning processes are thought to drive both typical and dysfunctional behaviours. Data from two-stage decision tasks have seemingly shown that human behaviour is driven by both processes operating in parallel. However, in this study, we show that more detailed task instructions lead participants to make primarily model-based choices that have little, if any, simple model-free influence. We also demonstrate that behaviour in the two-stage task may falsely appear to be driven by a combination of simple model-free and model-based learning if purely model-based agents form inaccurate models of the task because of misconceptions. Furthermore, we report evidence that many participants do misconceive the task in important ways. Overall, we argue that humans formulate a wide variety of learning models. Consequently, the simple dichotomy of model-free versus model-based learning is inadequate to explain behaviour in the two-stage task and connections between reward learning, habit formation, and compulsivity.

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Model-free versus Model-based Volatility Prediction

Journal of Financial Econometrics ◽

10.1093/jjfinec/nbm004 ◽

2007 ◽

Vol 5 (3) ◽

pp. 358-359 ◽

Cited By ~ 16

Author(s):

D. N. Politis

Keyword(s):

Model Based ◽

Model Free ◽

Versus Model

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Acute Stress Effects on Model-Based versus Model-Free Reinforcement Learning

PsycEXTRA Dataset ◽

10.1037/e633262013-263 ◽

2013 ◽

Cited By ~ 1

Author(s):

A. Ross Otto ◽

Candace M. Raio ◽

Elizabeth A. Phelps ◽

Nathaniel Daw

Keyword(s):

Reinforcement Learning ◽

Acute Stress ◽

Stress Effects ◽

Model Based ◽

Model Free ◽

Versus Model

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When will's wont wants wanting

Behavioral and Brain Sciences ◽

10.1017/s0140525x20001508 ◽

2021 ◽

Vol 44 ◽

Author(s):

Peter Dayan

Keyword(s):

Reinforcement Learning ◽

Short Term ◽

Model Based ◽

Model Free ◽

Versus Model ◽

Offline Learning ◽

Model Free Control ◽

And Control ◽

Instrumental Control

Abstract We use neural reinforcement learning concepts including Pavlovian versus instrumental control, liking versus wanting, model-based versus model-free control, online versus offline learning and planning, and internal versus external actions and control to reflect on putative conflicts between short-term temptations and long-term goals.

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Serotonergic Neurotransmission and Its Role in Negative Affect

A New Understanding of Mental Disorders ◽

10.7551/mitpress/9780262036894.003.0005 ◽

2017 ◽

Author(s):

Andreas Heinz

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Mental Disorders ◽

Negative Affect ◽

Dopaminergic Neurotransmission ◽

Model Based ◽

Model Free ◽

Versus Model ◽

Serotonergic Neurotransmission

While dopaminergic neurotransmission has largely been implicated in reinforcement learning and model-based versus model-free decision making, serotonergic neurotransmission has been implicated in encoding aversive outcomes. Accordingly, serotonin dysfunction has been observed in disorders characterized by negative affect including depression, anxiety and addiction. Serotonin dysfunction in these mental disorders is described and its association with negative affect is discussed.

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