Option prices for risk‐neutral density estimation using nonparametric methods through big data and large‐scale problems

2021 ◽  
Author(s):  
Ana M. Monteiro ◽  
António A. F. Santos
Keyword(s):  
Big Data ◽  
Density Estimation ◽  
Large Scale ◽  
Nonparametric Methods ◽  
Neutral Density ◽  
Option Prices ◽  
Large Scale Problems ◽  
Risk Neutral ◽  
Risk Neutral Density

Recovering Risk Neutral Densities from Option Prices: A New Approach

2008 ◽  
Vol 43 (4) ◽  
pp. 1037-1053 ◽  
Author(s):  
Leonidas S. Rompolis ◽  
Elias Tzavalis
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Empirical Evidence ◽  
Neutral Density ◽  
Option Prices ◽  
Exponential Form ◽  
New Approach ◽  
Risk Neutral ◽  
Risk Neutral Probabilities ◽  
Risk Neutral Density

AbstractIn this paper we present a new method of approximating the risk neutral density (RND) from option prices based on the C-type Gram-Charlier series expansion (GCSE) of a probability density function. The exponential form of this type of GCSE guarantees that it will always give positive values of the risk neutral probabilities, and it can allow for stronger deviations from normality, which are two drawbacks of the A-type GCSE used in practice. To evaluate the performance of the suggested expansion of the RND, the paper presents simulation and empirical evidence.

scholarly journals Dynamic semiparametric factor models in risk neutral density estimation

2009 ◽  
Vol 93 (4) ◽  
pp. 387-402 ◽  
Author(s):  
Enzo Giacomini ◽  
Wolfgang Härdle ◽  
Volker Krätschmer
Keyword(s):  
Density Estimation ◽  
Factor Models ◽  
Neutral Density ◽  
Risk Neutral ◽  
Risk Neutral Density

scholarly journals A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps

2021 ◽  
Vol 6 ◽  
Author(s):  
Liyuan Jiang ◽  
Shuang Zhou ◽  
Keren Li ◽  
Fangfang Wang ◽  
Jie Yang
Keyword(s):  
Parametric Method ◽  
Neutral Density ◽  
Density Estimator ◽  
Option Prices ◽  
Variance Swaps ◽  
Nonparametric Approach ◽  
Normal Inverse Gaussian Distribution ◽  
Risk Neutral ◽  
Risk Neutral Density ◽  
Normal Inverse Gaussian

Estimates of risk-neutral densities of future asset returns have been commonly used for pricing new financial derivatives, detecting profitable opportunities, and measuring central bank policy impacts. We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem. We evaluate our approach using the S&P 500 market option prices from 1996 to 2015. A comprehensive cross-validation study shows that our approach outperforms the existing nonparametric quartic B-spline and cubic spline methods, as well as the parametric method based on the normal inverse Gaussian distribution. As an application, we use the proposed density estimator to price long-term variance swaps, and the model-implied prices match reasonably well with those of the variance future downloaded from the Chicago Board Options Exchange website.

State price density estimation with an application to the recovery theorem

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anthony Sanford
Keyword(s):  
Stock Prices ◽  
Two Dimensions ◽  
Neutral Density ◽  
Option Prices ◽  
Strike Price ◽  
B Splines ◽  
State Price ◽  
Current Estimation ◽  
Risk Neutral ◽  
Risk Neutral Density

Abstract This article introduces a model to estimate the risk-neutral density of stock prices derived from option prices. To estimate a complete risk-neutral density, current estimation techniques use a single mathematical model to interpolate option prices on two dimensions: strike price and time-to-maturity. Instead, this model uses B-splines with at-the-money knots for the strike price interpolation and a mixed lognormal function that depends on the option expiration horizon for the time-to-maturity interpolation. The results of this “hybrid” methodology are significantly better than other risk-neutral density extrapolation methods when applied to the recovery theorem.

scholarly journals The Implied Risk Neutral Density Dynamics: Evidence from the S&P TSX 60 Index

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Nessim Souissi
Keyword(s):  
Risk Perception ◽  
Financial Markets ◽  
Daily Variation ◽  
Trading Strategies ◽  
Neutral Density ◽  
Option Prices ◽  
Monetary Policies ◽  
Risk Neutral ◽  
Density Dynamics ◽  
Risk Neutral Density

The risk neutral density is an important tool for analyzing the dynamics of financial markets and traders’ attitudes and reactions to already experienced shocks by financial markets as well as the potential ones. In this paper, we present a new method for the extraction information content from option prices. By eliminating bias caused by daily variation of contract maturity through a completely nonparametric technique based on kernel regression, we allow comparing evolution of risk neutral density and extracting from time continuous indicators that detect evolution of traders’ attitudes, risk perception, and belief homogeneity. This method is useful to develop trading strategies and monetary policies.

Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method

2018 ◽  
Vol 54 (2) ◽  
pp. 705-728
Author(s):  
Arindam Kundu ◽  
Sumit Kumar ◽  
Nutan Kumar Tomar
Keyword(s):  
Density Estimation ◽  
Neutral Density ◽  
Risk Neutral ◽  
Risk Neutral Density
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