scholarly journals Nonparametric statistical methods and the pricing of derivative securities

2002 ◽  
Vol 6 (1) ◽  
pp. 1-22
Author(s):  
Rüdiger Kiesel
Keyword(s):  
Nonparametric Methods ◽  
Derivative Securities ◽  
Short Introduction ◽  
Financial Options ◽  
Consistent Model ◽  
Nonparametric Statistical Methods ◽  
Risk Neutral Probabilities ◽  
Pricing Theory ◽  
Option Pricing Theory ◽  
Nonparametric Statistical

In this review paper we summarise several nonparametric methods recently applied to the pricing of financial options. After a short introduction to martingale-based option pricing theory, we focus on two possible fields of application for nonparametric methods: the estimation of risk-neutral probabilities and the estimation of the dynamics of the underlying instruments in order to construct an internally consistent model.

scholarly journals 999 STATISTICAL ANALYSIS OF DISEASE REACTION DATA USING NONPARAMETRIC METHODS

HortScience ◽  
1994 ◽  
Vol 29 (5) ◽  
pp. 572e-572 ◽  
Author(s):  
Kent M. Eskridge
Keyword(s):  
Statistical Analysis ◽  
Statistical Methods ◽  
Analysis Of Variance ◽  
Plant Disease ◽  
Nonparametric Methods ◽  
Classical Analysis ◽  
Disease Reaction ◽  
Reaction Data ◽  
Nonparametric Statistical Methods ◽  
Nonparametric Statistical

Breeders need powerful and simply understood statistical methods when analyzing disease reaction data. However, many disease reaction experiments result in data which do not adhere to the classical analysis of variance (ANOVA) assumptions of normality, homogeneity variance and a correctly specified model. Nonparametric statistical methods which require fewer assumptions than classical ANOVA, are applied to data from several disease reaction experiments. It is concluded that nonparametric methods are easily understood, can be productively applied to plant disease experiments and many times result in improved chances for detecting differences between treatments.

scholarly journals Fractional Riccati Equation and Its Applications to Rough Heston Model

2020 ◽  
Author(s):  
Siow W. Jeng ◽  
Adem Kilicman
Keyword(s):  
Numerical Methods ◽  
Characteristic Function ◽  
Riccati Equation ◽  
Financial Market ◽  
Heston Model ◽  
Short Introduction ◽  
Volatility Models ◽  
Rough Volatility ◽  
Pricing Theory ◽  
Option Pricing Theory

Rough volatility models are popularized by \cite{gatheral2018volatility}, where they have shown that the empirical volatility in the financial market is extremely consistent with rough volatility. Fractional Riccati equation as a part of computation for the characteristic function of rough Heston model is not known in explicit form as of now and therefore, we must rely on numerical methods to obtain a solution. In this paper, we give a short introduction to option pricing theory and an overview of the current advancements on the rough Heston model.

scholarly journals Methodological Choices In Detecting Divergent Earnings

2011 ◽  
Vol 16 (3) ◽  
Author(s):  
David T. Doran
Keyword(s):  
Market Price ◽  
Test Statistics ◽  
Font Size ◽  
Methodological Choices ◽  
Random Samples ◽  
Nonparametric Statistical Methods ◽  
Expected Earnings ◽  
The Difference ◽  
Reported Earnings ◽  
Nonparametric Statistical

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="letter-spacing: -0.15pt;"><span style="font-size: x-small;"><span style="font-family: Batang;">This paper empirically tests for methodological superiority in detecting divergent earnings (the difference between actual and expected earnings).<span style="mso-spacerun: yes;">&nbsp; </span>Divergent earnings are generated using Value Line forecasted and reported earnings data.<span style="mso-spacerun: yes;">&nbsp; </span>Two hundred random samples of 100 cases each are drawn.<span style="mso-spacerun: yes;">&nbsp;&nbsp;&nbsp; </span>One hundred independent two sample tests are performed with 0%, 1%, 3%, 5%, 7%, and 10 % positive earnings introduced.<span style="mso-spacerun: yes;">&nbsp; </span>The two sample tests are performed using both parametric (t test), and nonparametric (Mann Whitney test) statistics.<span style="mso-spacerun: yes;">&nbsp; </span>They are performed on the &ldquo;divergent earnings&rdquo; data deflated by: 1) forecasted earnings , and 2) the market price of the stock.<span style="mso-spacerun: yes;">&nbsp; </span>The results indicate that the superior alternative is nonparametric statistical methods based upon ranks, and the deflator choice under these nonparametric methods is of little consequence.</span></span></span></p>

Louis Bachelier: The Father of Modern Option Pricing Theory

1991 ◽  
Vol 22 (2) ◽  
pp. 165-171 ◽  
Author(s):  
Edward J. Sullivan ◽  
Timothy M. Weithers
Keyword(s):  
Option Pricing ◽  
Pricing Theory ◽  
Option Pricing Theory

scholarly journals OPTION PRICING IN MARKETS WITH INFORMED TRADERS

2020 ◽  
Vol 23 (06) ◽  
pp. 2050037 ◽  
Author(s):  
Yuan Hu ◽  
Abootaleb Shirvani ◽  
Stoyan Stoyanov ◽  
Young Shin Kim ◽  
Frank J. Fabozzi ◽  
...  
Keyword(s):  
Option Pricing ◽  
Stock Price ◽  
Stock Return ◽  
Asset Pricing Theory ◽  
Black Scholes ◽  
Informed Traders ◽  
Pricing Theory ◽  
Option Pricing Theory ◽  
Information Intensity ◽  
Dynamic Asset Pricing

The objective of this paper is to introduce the theory of option pricing for markets with informed traders within the framework of dynamic asset pricing theory. We introduce new models for option pricing for informed traders in complete markets, where we consider traders with information on the stock price direction and stock return mean. The Black–Scholes–Merton option pricing theory is extended for markets with informed traders, where price processes are following continuous-diffusions. By doing so, the discontinuity puzzle in option pricing is resolved. Using market option data, we estimate the implied surface of the probability for a stock upturn, the implied mean stock return surface, and implied trader information intensity surface.

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