S&amp;P 500 Index, an Option-Implied Risk Analysis

Barone‐Adesi, Giovanni; Legnazzi, Chiara; Sala, Carlo

doi:10.2139/ssrn.3162037

Cited by 4 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, there has been much interest also in other functionals of the risk neutral distribution. Barone Adesi (2016) suggested to consider implicit Value at Risk and implicit Expected Shortfall, and extensive empirical analysis on S&P500 Index and WTI crude oil options have been provided in Barone Adesi et al (2016a) and Barone Adesi et al (2016b). Elyasiani et al (2016) introduced a risk-asymmetry index based on the normalized difference of positive and negative corridor implied volatilities and studied its empirical properties on FTSE MIB index options.…”

Section: Introductionmentioning

confidence: 99%

Implicit expectiles and measures of implied volatility

Bellini

Mercuri

Rroji

2018

Quantitative Finance

View full text Add to dashboard Cite

We show how to compute the expectiles of the risk neutral distribution from the prices of European call and put options. Empirical properties of these implicit expectiles are studied on a dataset of closing daily prices of FTSE MIB index options. We introduce the interexpectile difference ∆τ (X) := eτ (X) − e1−τ (X), for τ ∈ (1/2, 1], and suggest that it is a natural measure of the variability of the risk neutral distribution. We investigate its theoretical and empirical properties and compare it with the VIX index computed by CBOE. We also discuss a theoretical comparison with implicit VaR and CVaR introduced in Barone Adesi (2016).

show abstract

Section: Introductionmentioning

confidence: 99%

Implicit expectiles and measures of implied volatility

Bellini

Mercuri

Rroji

2018

Quantitative Finance

View full text Add to dashboard Cite

show abstract

“…Thomas (2016) finds that the power utility family is the only valid utility class and commonly used in practical applications. Furthermore, our CRRA coefficients are among the most employed in the forecasting literature (see Barone‐Adesi et al, 2017; Bliss & Panigirtzoglou, 2004; Brinkmann & Korn, 2018; Meyer & Meyer, 2005; Polkovnichenko & Zhao, 2013). Section 6.7 further expands these analyses with an alternative risk setting where risk aversion is dynamically estimated from option prices.…”

Section: Data and Calibrationmentioning

confidence: 99%

Estimating real‐world probabilities: A forward‐looking behavioral framework

Crisóstomo

2021

Journal of Futures Markets

View full text Add to dashboard Cite

We show that disentangling sentiment-induced biases from fundamental expectations significantly improves the accuracy and consistency of probabilistic forecasts. Using data from 1994 to 2017, we analyze 15 stochastic models and risk-preference combinations and in all possible cases a simple behavioral transformation delivers substantial forecast gains. Our results are robust across different evaluation methods, risk-preference hypotheses, and sentiment calibrations, demonstrating that behavioral effects can be effectively used to forecast asset prices. We also implement a trading strategy that shows how behavioral biases can be exploited to generate trading profits. Further analyses confirm that our real-world densities outperform forecasts recalibrated to avoid past mistakes and improve predictive models where risk aversion is dynamically estimated from option prices.

show abstract

“…Barone‐Adesi () derives both the VaR and CVaR from option market data and Barone‐Adesi, Legnazzi, and Sala () present substantial empirical evidence that the proposed technique works well for the S&P 500 for the period 2005–2015. Assuming a portfolio with limited liability, from Equation and by the relation between the VaR and the first derivative of the put price over the strike price, it follows that

\begin{matrix} \frac{d p_{t}}{d K} & = e^{- r_{t, T} \cdot T} \int_{0}^{K} f false(S_{t, T} false) d S_{t, T} \\ = e^{- r_{t, T} \cdot T} F false(K false) \\ = e^{- r_{t, T} \cdot T} α . \end{matrix}

Hence the option‐implied

{VaR}_{t, T}^{α}

is the difference between today's price of the underlying, S t , and the strike price, K , of a European put option at level α :

{VaR}_{t, T}^{α} = S_{t} - K^{α} .

…”

Section: Var and Cvarmentioning

confidence: 99%

WTI crude oil option implied VaR and CVaR: An empirical application

Barone‐Adesi

Finta

Legnazzi

et al. 2019

Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

Using option market data we derive naturally forward‐looking, nonparametric and model‐free risk estimates, three desired characteristics hardly obtainable using historical returns. The option‐implied measures are only based on the first derivative of the option price with respect to the strike price, bypassing the difficult task of estimating the tail of the return distribution. We estimate and backtest the 1%, 2.5%, and 5% WTI crude oil futures option‐implied value at risk and conditional value at risk for the turbulent years 2011–2016 and for both tails of the distribution. Compared with risk estimations based on the filtered historical simulation methodology, our results show that the option‐implied risk metrics are valid alternatives to the statistically based historical models.

show abstract

S&P 500 Index, an Option-Implied Risk Analysis

Cited by 4 publications

References 48 publications

Implicit expectiles and measures of implied volatility

Implicit expectiles and measures of implied volatility

Estimating real‐world probabilities: A forward‐looking behavioral framework

WTI crude oil option implied VaR and CVaR: An empirical application

Contact Info

Product

Resources

About