Informed Trading in the Stock Market and Option-Price Discovery

2020 ◽  
pp. 1-40 ◽  
Author(s):  
Pierre Collin-Dufresne ◽  
Vyacheslav Fos ◽  
Dmitry Muravyev
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Implied Volatility ◽  
Common Knowledge ◽  
Option Price ◽  
Price Volatility ◽  
Informed Trading ◽  
Flow Dynamics ◽  
Option Prices ◽  
Market Makers

Abstract When activist shareholders file Schedule 13D filings, the average stock-price volatility drops by approximately 10%. Prior to filing days, volatility information is reflected in option prices. Using a comprehensive sample of trades by Schedule 13D filers that reveals on what days and in what markets they trade, we show that on days when activists accumulate shares, option-implied volatility decreases, implied volatility skew increases, and implied volatility time slope increases. The evidence is consistent with a theoretical model where it is common knowledge that informed trading occurs only in the stock market and market makers update option prices based on stock-price and order-flow dynamics.

scholarly journals ANALYSIS OF MARKET VOLATILITY VIA A DYNAMICALLY PURIFIED OPTION PRICE PROCESS

2014 ◽  
Vol 09 (03) ◽  
pp. 1450006 ◽  
Author(s):  
CHUONG LUONG ◽  
NIKOLAI DOKUCHAEV
Keyword(s):  
Stock Price ◽  
Implied Volatility ◽  
Financial Time Series ◽  
Option Price ◽  
Option Prices ◽  
Price Process ◽  
Volatility Index ◽  
Quadratic Interpolation ◽  
Dynamic Estimation ◽  
The Impact

The paper studies methods of dynamic estimation of volatility for financial time series. We suggest to estimate the volatility as the implied volatility inferred from some artificial "dynamically purified" price process that in theory allows to eliminate the impact of the stock price movements. The complete elimination would be possible if the option prices were available for continuous sets of strike prices and expiration times. In practice, we have to use only finite sets of available prices. We discuss the construction of this process from the available option prices using different methods. In order to overcome the incompleteness of the available option prices, we suggests several interpolation approaches, including the first order Taylor series extrapolation and quadratic interpolation. We examine the potential of the implied volatility derived from this proposed process for forecasting of the future volatility, in comparison with the traditional implied volatility process such as the volatility index VIX.

scholarly journals Can the Implied Information of Options Predict the Liquidity of Stock Market? A Data-Driven Research Based on SSE 50ETF Options

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hairong Cui ◽  
Jinfeng Fei ◽  
Xunfa Lu
Keyword(s):  
Stock Market ◽  
Risk Premium ◽  
Implied Volatility ◽  
Option Price ◽  
Price Volatility ◽  
Market Liquidity ◽  
Data Driven ◽  
Management Tool ◽  
Variance Risk Premium ◽  
Variance Risk

Liquidity reflects the quality of the market. When the market is short of liquidity, it often causes investors’ trading difficulties and stock price volatility, expanding the investment risk. As a risk management tool, options attract more informed investors to trade because of their flexible design. To explore whether the implied information based on the formation of option price can predict the liquidity of stock market, we take SSE 50ETF options from February 9, 2015, to December 31, 2020, as the research sample. Based on the idea of data-driven approach, we extract the implied information contained in option price, including implied volatility, implied volatility spread, and variance risk premium. Through the regression analysis method, we examine the ability to predict the liquidity of the stock market. The results show that implied volatility spread has the strongest ability to predict the liquidity of the stock market, and it is more significant within 270 days. Implied volatility contains the information about the short-term (120 days) liquidity of the stock market in the future. It shows that implied volatility and implied volatility spread are good indicators to predict stock market liquidity. In contrast, variance risk premium cannot predict the liquidity of stock market. The research conclusion verifies the role of option-implied information in predicting the stock market’s liquidity. By extracting the information of options price, investors and financial regulators can scientifically participate in the financial market under data guidance.

scholarly journals OPTIONS WRITTEN ON STOCKS WITH KNOWN DIVIDENDS

2004 ◽  
Vol 07 (07) ◽  
pp. 901-907
Author(s):  
ERIK EKSTRÖM ◽  
JOHAN TYSK
Keyword(s):  
Stock Price ◽  
Option Price ◽  
Option Prices ◽  
Strike Price ◽  
Call Options ◽  
Alternative Approach ◽  
Black Scholes ◽  
Market Practice ◽  
The Difference ◽  
Risk Free Rate

There are two common methods for pricing European call options on a stock with known dividends. The market practice is to use the Black–Scholes formula with the stock price reduced by the present value of the dividends. An alternative approach is to increase the strike price with the dividends compounded to expiry at the risk-free rate. These methods correspond to different stock price models and thus in general give different option prices. In the present paper we generalize these methods to time- and level-dependent volatilities and to arbitrary contract functions. We show, for convex contract functions and under very general conditions on the volatility, that the method which is market practice gives the lower option price. For call options and some other common contracts we find bounds for the difference between the two prices in the case of constant volatility.

scholarly journals PENENTUAN HARGA OPSI BELI ATAS SAHAM PT. ANTAM (PERSERO) MENGGUNAKAN MODEL BINOMIAL FUZZY

2018 ◽  
Vol 19 (1) ◽  
pp. 8-24
Author(s):  
Agung Prabowo ◽  
Zulfatul Mukarromah ◽  
Lisnawati Lisnawati ◽  
Pramono Sidi
Keyword(s):  
Stock Price ◽  
Market Price ◽  
Option Price ◽  
Risk Level ◽  
Binomial Model ◽  
Purchase Price ◽  
Option Prices ◽  
Movement Data ◽  
Curve Representation ◽  
Price Option

Option is a financial instrument where price depends on the underlying stock price. The pricing of options, both selling options and purchase options, may use the CRR (Cox-Ross-Rubinstein) binomial model. Only two possible parameters were used that is u if the stock price rises and d when the stock price down. One of the elements that determine option prices is volatility. In the binomial model CRR volatility is constant. In fact, the financial market price of stocks fluctuates so that volatility also fluctuates. This article discusses volatility of fluctuating stock price movements by modeling it using binomial fuzzy with triangular curve representation. The analysis is carried out in relation to the existence of three interpretations of the triangular curve representation resulting in different degrees of membership. In addition to volatility, this study added the size or risk level ρ. As an illustration, this study used stock price movement data from PT. Antam (Persero) from August 2015 until July 2016. The results of one period obtained from the purchase price option for August 2016 with the largest volatility, medium and smallest respectively were Rp.143,43, Rp.95,49, and Rp.79,00. There was calculated at the risk level of  ρ = 90%. The degree of membership for each option price varies depending on the interpretation of the triangle curve representation.   Opsi merupakan instrumen keuangan yang harganya tergantung pada harga saham yang mendasarinya. Penentuan harga opsi, baik opsi jual maupun opsi beli dapat menggunakan model binomial CRR (Cox-Ross-Rubinstein). Dalam model ini hanya dimungkinkan adanya dua parameter yaitu u apabila harga saham naik dan d pada saat harga saham turun. Salah satu unsur yang menentukan harga opsi adalah volatilitas. Dalam model binomial CRR digunakan volatilitas yang bersifat konstan. Padahal, pada pasar keuangan pergerakan harga saham mengalami fluktuasi sehingga volatilitas juga menjadi fluktuatif. Artikel ini membahas volatilitas pergerakan harga saham yang fluktuatif dengan memodelkannya menggunakan binomial fuzzy dengan representasi kurva segitiga. Analisis dilakukan terkait dengan adanya tiga interpretasi terhadap representasi kurva segitiga tersebut yang menghasilkan derajat keanggotaan yang berbeda. Selain volatilitas, dalam penelitian ini ditambahkan ukuran atau tingkat risiko ρ. Sebagai ilustrasi, digunakan data pergerakan harga saham PT. Antam (Persero) dari Agustus 2015 hingga Juli 2016. Hasil penelitian dengan perhitungan satu periode diperoleh hasil harga opsi beli untuk bulan Agustus 2016 dengan volatilitas terbesar, menengah, dan terkecil masing-masing adalah Rp.143,43, Rp.95,49, dan Rp.79,00 yang dihitung pada tingkat risiko ρ = 90%. Derajat keanggotaan untuk masing-masing harga opsi berbeda-beda tergantung pada interpretasi dari representasi kurva segitiga.

ENDOGENOUS PARTICIPATION, RISK, AND LEARNING IN THE STOCK MARKET

2020 ◽  
pp. 1-33 ◽  
Author(s):  
Michael Shin
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Price Volatility ◽  
Participation Rate ◽  
Participation Rates ◽  
Asset Pricing Model ◽  
Excess Volatility ◽  
Subjective Risk ◽  
Cross Country

A simple asset pricing model with both endogenous stock market participation and subjective risk can explain the negative cross-country correlation between participation rates and the volatility of excess returns, along with the time-varying participation rates in the data. Belief-driven learning dynamics can explain the interplay between participation rates, subjective risk, and stock price volatility. When agents adaptively learn about the risk and return, my model generates 25% of the excess volatility observed in US stock prices, while also matching key moments. With learning about risk, excess volatility of stock prices is driven by fluctuations in the participation rate that arise because agents’ risk estimates vary with prices. I find that learning about risk is quantitatively more important than learning about returns.

scholarly journals The Task Force Report: The Reasoning Behind the Recommendations

1988 ◽  
Vol 2 (3) ◽  
pp. 3-23 ◽  
Author(s):  
Bruce Greenwald ◽  
Jeremy Stein
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Task Force ◽  
Price Volatility ◽  
Market Mechanisms ◽  
Task Force Report ◽  
Stock Market Crash ◽  
Stock Price Volatility ◽  
Princeton University

This paper was prepared for the Symposium on the [October 1987] Stock Market Crash, held February 8, 1988, at Princeton University. The article provides a framework for thinking about the recommendations made by the Presidential Task Force on Market Mechanisms. Three conclusions can be drawn from the Task Force's findings: First, the proper focus of analysis of the events of the October crash should be on “market mechanisms” rather than on fundamental imbalances in the economy as a whole. Second, the instability evident in the events of October 1987 was not the inexorable limit of a steadily increasing level of day-to-day stock price volatility. Third, under the sorts of conditions that prevailed on late Monday and Tuesday, an orderly halt to trading (and subsequent orderly reopening) would have been preferable to what actually took place. We describe how the data collected by the Task Force leads us to these three broad conclusions.

scholarly journals Prediction of Option Price using Ensemble of Machine Learning Algorithms for Indian Stock Market

2019 ◽  
Vol 8 (2) ◽  
pp. 3231-3241
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithm ◽  
Option Price ◽  
Machine Learning Algorithms ◽  
Parametric Models ◽  
Accuracy Of Prediction ◽  
Indian Stock Market

The non-deterministic behavior of stock market creates ambiguities for buyers. The situation of ambiguities always finds the loss of user financial assets. The variations of price make a very difficult task to predict the option price. For the prediction of option used various non-parametric models such as artificial neural network, machine learning, and deep neural network. The accuracy of prediction is always a challenging task of for individual model and hybrid model. The variation gap of hypothesis value and predicted value reflects the nature of stock market. In this paper use the bagging method of machine learning for the prediction of option price. The bagging process merge different machine learning algorithm and reduce the variation gap of stock price.

scholarly journals Valuing Coca-Cola And PepsiCo Options Using The Black-Scholes Option Pricing Model And Data Downloads From The Internet

2012 ◽  
Vol 8 (6) ◽  
pp. 559-564
Author(s):  
John C. Gardner ◽  
Carl B. McGowan Jr
Keyword(s):  
Option Pricing ◽  
Stock Price ◽  
Option Price ◽  
Option Prices ◽  
Pricing Model ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Risk Free Rate ◽  
Daily Returns ◽  
Free Rate

In this paper, we demonstrate how to collect the data and compute the actual value of Black-Scholes Option Pricing Model call option prices for Coca-Cola and PepsiCo.The data for the current stock price and option price are taken from Yahoo Finance and the daily returns variance is computed from daily prices.The time to maturity is computed as the number of days remaining for the stock option.The risk-free rate is obtained from the U.S. Treasury website.

scholarly journals Why Do Option Prices Predict Stock Returns? The Role of Price Pressure in the Stock Market

2020 ◽  
Vol 66 (9) ◽  
pp. 3903-3926 ◽  
Author(s):  
Luis Goncalves-Pinto ◽  
Bruce D. Grundy ◽  
Allaudeen Hameed ◽  
Thijs van der Heijden ◽  
Yichao Zhu
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Option Price ◽  
Option Prices ◽  
Options Markets ◽  
Stock Return Predictability ◽  
Price Pressure ◽  
Primary Driver ◽  
Stock Values

Stock and options markets can disagree about a stock’s value because of informed trading in options and/or price pressure in the stock. The predictability of stock returns based on this cross-market discrepancy in values is especially strong when accompanied by stock price pressure, and it does not depend on trading in options. We argue that option-implied prices provide an anchor for fundamental stock values that helps to distinguish stock price movements resulting from pressure versus news. Overall, our results are consistent with stock price pressure being the primary driver of the option price-based stock return predictability. This paper was accepted by Tyler Shumway, finance.

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