Valuation Method of Equity Incentives of Listed Companies Based on the Black-Scholes Model

2020 ◽  
Vol 12 (2) ◽  
pp. 36-49
Author(s):  
Zhongwen Liu ◽  
Yifei Chen
Keyword(s):  
Stock Price ◽  
Turnover Rate ◽  
Garch Model ◽  
Listed Companies ◽  
Valuation Method ◽  
Black Scholes Model ◽  
Black Scholes ◽  
S Model ◽  
The Garch Model ◽  
Equity Incentive

This article applies the classic Black-Scholes model (i.e. B-S model) and turnover rate adapted B-S model (revised B-S model) to equity incentive valuation of listed companies. Unlike other studies on equity incentive valuation which generally adopt historical volatility, this article applies the GARCH model to equity incentive valuation. The volatility of stock price is estimated by the GARCH model to improve the accuracy of equity incentive valuation. The turnover rate has an important impact on the equity incentive valuation of listed companies. Considering the turnover rate can improve the accuracy of the equity incentive valuation and reduce the error of equity incentive valuation. Through the case study of the equity incentive valuation of Infinova, the practicality of the equity incentive valuation method is further verified.

scholarly journals Can The IDX Be Hegded ? : Comparison of Black Scholes Option Model And Garch Option Model Using Long Strangle Strategy

2020 ◽  
Vol 20 (3) ◽  
pp. 252
Author(s):  
Riko Hendrawan ◽  
Gede Teguh Laksana ◽  
Wiwin Aminah
Keyword(s):  
Arima Model ◽  
Garch Model ◽  
Call Option ◽  
Average Percentage ◽  
Put Option ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Option Model ◽  
The One ◽  
The Garch Model

The purpose of this research was to compare the accuracy of the Black Scholes option model and the GARCH option model on index options using IDX Composite (IHSG) data from 2009-2018 with the long strangle strategy. The Black Scholes volatility constructed by using historical volatility, while GARCH volatility constructed by using the ARIMA model and the best lag. The accuracy of options analyzed using the average percentage mean square error (AMSE) to find the best model. The results of this study showed that for the one month option, the GARCH model is more accurate for a call option with 0.26%, while the Black Scholes model is more accurate for a put option with 0.18%. For the two month option, the GARCH model is more accurate for a call option with 0.92%, while the Black Scholes model is more accurate for a put option with 0.26%. For the three month option, the Black Scholes model is more accurate for a call option and put option with 2.00% and 0.31%, respectively. The results of this study further sharpen the research conducted by Bhat and Arekar (2016)and Hendrawan(2010) Keywords : Black Scholes Options Model; GARCH Option Model; Long Strangle; ,Index Option.,

scholarly journals The Extended Black-Scholes Model with-LAGS-and “Hedging Errors”

2003 ◽  
Author(s):  
Mondher Bellalah
Keyword(s):  
Stock Price ◽  
Standard Form ◽  
Small Time ◽  
Market Model ◽  
Short Term ◽  
Hedging Strategy ◽  
Underlying Asset ◽  
Elapsed Time ◽  
Black Scholes Model ◽  
Black Scholes

The Black-Scholes model is derived under the assumption that heding is done instantaneously. In practice, there is a “small” time that elapses between buying or selling the option and hedging using the underlying asset. Under the following assumptions used in the standard Black-Scholes analysis, the value of the option will depend only on the price of the underlying asset S, time t and on other Variables assumed constants. These assumptions or “ideal conditions” as expressed by Black-Scholes are the following. The option us European, The short term interest rate is known, The underlying asset follows a random walk with a variance rate proportional to the stock price. It pays no dividends or other distributions. There is no transaction costs and short selling is allowed, i.e. an investment can sell a security that he does not own. Trading takes place continuously and the standard form of the capital market model holds at each instant. The last assumption can be modified because in practice, trading does not take place in-stantaneouly and simultaneously in the option and the underlying asset when implementing the hedging strategy. We will modify this assumption to account for the “lag”. The lag corresponds to the elapsed time between buying or selling the option and buying or selling - delta units of the underlying assets. The main attractions of the Black-Scholes model are that their formula is a function of “observable” variables and that the model can be extended to the pricing of any type of option. All the assumptions are conserved except the last one.

scholarly journals Pricing Exotic Options Using Some Lattice Procedures

2021 ◽  
Vol 41 (1) ◽  
pp. 26-40
Author(s):  
Sadia Anjum Jumana ◽  
ABM Shahadat Hossain
Keyword(s):  
Stock Price ◽  
Lattice Models ◽  
Exotic Options ◽  
Barrier Options ◽  
Tree Model ◽  
Binomial Tree ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Binomial Tree Model ◽  
Trinomial Tree Model

In this work, we discuss some very simple and extremely efficient lattice models, namely, Binomial tree model (BTM) and Trinomial tree model (TTM) for valuing some types of exotic barrier options in details. For both these models, we consider the concept of random walks in the simulation of the path which is followed by the underlying stock price. Our main objective is to estimate the value of barrier options by using BTM and TTM for different time steps and compare these with the exact values obtained by the benchmark Black-Scholes model (BSM). Moreover, we analyze the convergence of these lattice models for these exotic options. All the results have been shown numerically as well as graphically. GANITJ. Bangladesh Math. Soc.41.1 (2021) 26-40

Peramalan Indeks Harga Saham dengan Autoregressive Moving Average Generelized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH)

2021 ◽  
Vol 1 (1) ◽  
pp. 7-12
Author(s):  
Nur Najmi Layla ◽  
Eti Kurniati ◽  
Didi Suhaedi
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Garch Model ◽  
Data Partitioning ◽  
Forecasting Model ◽  
Autoregressive Moving Average ◽  
Conditional Heteroscedasticity ◽  
Eligibility Requirements ◽  
The Garch Model ◽  
Autoregressive Conditional Heteroscedasticity

Abstract. The stock price index is the information the public needs to know the development of stock price movements. Stock price forecasting will provide a better basis for planning and decision making. The forecasting model that is often used to model financial and economic data is the Autoregressive Moving Average (ARMA). However, this model can only be used for data with the assumption of stationarity to variance (homoscedasticity), therefore an additional model is needed that can model data with heteroscedasticity conditions, namely the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. This study uses data partitioning in pre-pandemic conditions and during the pandemic, Insample data with pre-pandemic conditions and insample data during pandemic conditions. Based on the research results, the GARCH model (1,1) was obtained with the conditions before the pandemic and GARCH (1,2) during the pandemic condition. The forecasting model obtained has met the eligibility requirements of the GARCH model. If the forecasting model fulfills the eligibility requirements, then MAPE calculations are performed to see the accuracy of the forecasting model. And obtained MAPE in the conditions before the pandemic and during the pandemic in the very good category. Abstrak. Indeks harga saham merupakan informasi yang diperlukan masyarakat untuk mengetahui perkembangan pergerakan harga saham. Peramalan harga saham akan memberikan dasar yang lebih baik bagi perencanaan dan pengambilan keputusan. Model peramalan yang sering digunakan untuk memodelkan data keuangan dan ekonomi adalah Autoregrresive Moving Average (ARMA). Namun model tersebut hanya dapat digunakan untuk data dengan asumsi stasioneritas terhadap varian (homoskedastisitas), oleh karena itu diperlukan suatu model tambahan yang bisa memodelkan data dengan kondisi heteroskedastisitas, yaitu model Generalized Autoregressive Conditional Heteroscedastisity (GARCH). Penelitian ini menggunakan partisi data pada kondisi sebelum pandemi dan saat pandemi berlangsung data Insample dengan kondisi sebelum pandemi dan insample pada kondisi pandemi. Berdasarkan hasil penelitian, maka didapat model GARCH (1,1) dengan kondisi sebelum pandemi dan GARCH (1,2) saat kondisi pandemi. Model peramalan yang didapat sudah memenuhi syarat kelayakan model GARCH. Apabila model peramalan terpenuhi syarat kelayakannya maka dilakukan perhitungan MAPE untuk melihat keakuratan model peramalannya. Dan diperoleh MAPE pada kondisi sebelum pandemi dan saat pandemi dengan kategori sangat baik. 

scholarly journals Does Chinese Financial Market Information Promote Listed Manufacturing Firms’ Productivity?

2019 ◽  
Vol 11 (2) ◽  
pp. 329
Author(s):  
Fujun Lai ◽  
Qian Wang ◽  
Qingxiang Feng
Keyword(s):  
Economic Growth ◽  
Total Factor Productivity ◽  
Financial Market ◽  
Stock Price ◽  
Turnover Rate ◽  
Listed Companies ◽  
Market Information ◽  
Factor Productivity ◽  
Price Informativeness ◽  
Stock Price Informativeness

There have been many research studies that have examined the impact of financial development on economic growth, but few of them have explored this problem from the perspective of financial market information. In this paper, we investigate whether the stock price informativeness affect the listed firms’ sustainable growth by using the Chinese manufacturing listed companies’ data from 2007 to 2017. Specifically, we use the stock price nonsynchronicity and turnover rate to measure stock price informativeness, and the economic growth sustainability is proxied by the listed companies’ total factor productivity, which is the driving force of firms’ sustainable and steady growth. We find that higher stock price informativeness is associated with higher total factor productivity, no matter whether the stock price informativeness is proxied by the stock price nonsynchronicity or turnover rate. This finding is robust when we mitigate for endogeneity issues, and when we use the return on assets (ROA) as an alternative proxy for economic growth. Our results show that the stock price informativeness can significantly improve the total factor productivity of the listed companies, and play an important role in the sustainable development of listed manufacturing enterprises.

scholarly journals THE GARCH MODEL VOLATILITY OF SHARIA STOCKS ASSOCIATED CAUSALITY WITH MARKET INDEX

2020 ◽  
Vol 1 (1) ◽  
pp. 18-28
Author(s):  
Endang Soeryana Hasbullah ◽  
Endang Rusyaman ◽  
Alit Kartiwa
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Garch Model ◽  
Test Method ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Market Index ◽  
Causality Relationship ◽  
The Garch Model ◽  
The Relationship

The purpose of this paper is to examine the volatility of Islamic stocks related to the causality of the composite stock price index (CSPI). The aim is to investigate the causality of several levels of stock returns with the movement of the CSPI, and determine its volatility as a measure of risk. To determine the causality relationship is done by using the granger causality test method, with Vector Autoregressive (VAR) modeling. Whereas to determine the volatility is done using the Generalized Autoregressive Conditional Heteroscedastisiy (GARCH) model approach. The results of the causality test show that there is a direct relationship that affects and is influenced by the CSPI, and the relationship that affects each other between the company's stock market and the movement of the CSPI. While the volatility follows the GARCH model (1, 1). Based on the results of this study are expected to be used as consideration in making investment decisions in the analyzed stocks.

scholarly journals Stock price volatility during the COVID-19 pandemic: The GARCH model

2021 ◽  
Vol 18 (4) ◽  
pp. 12-20
Author(s):  
Endri Endri ◽  
Widya Aipama ◽  
A. Razak ◽  
Laynita Sari ◽  
Renil Septiano
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Practical Implication ◽  
Abnormal Returns ◽  
Garch Model ◽  
Price Volatility ◽  
Efficient Market Hypothesis ◽  
Empirical Literature ◽  
Stock Price Volatility ◽  
The Garch Model

This study examined the response of stock prices on the Indonesia Stock Exchange (IDX) to COVID-19 using an event study approach and the GARCH model. The research sample is the closing price of the Composite Stock Price Index (JCI) and companies that are members of LQ-45 in the 40-day period before the COVID-19 incident, 1 day during the COVID-19 incident (March 2, 2020) and 10 days after, January 6, 2020 – March 16, 2020. Empirical findings prove that abnormal returns react negatively to COVID-19, JCI volatility fluctuates widely during the COVID-19 event, and the GARCH(1,2) model can be used to assess volatility and predict stock abnormal returns in IDX in market conditions infected with COVID-19. The practical implication of the study’s findings for investors is that the COVID-19 event caused stock price volatility, which affects abnormal returns. Therefore, to face the conditions of uncertainty and increased volatility in the future, several lines of risk management are needed in managing a stock portfolio. In addition, it also opens up opportunities for speculators to profit in an inefficient market environment. This study is based on the empirical literature currently being developed to investigate the phenomenon of stock price volatility behavior during COVID-19 on the IDX. The GARCH model used proves that during the COVID-19 pandemic, stock price volatility increases and leads to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.

Option Pricing using Black Scholes Model (Case-Tata Motors and Reliance)

2015 ◽  
Vol 9 (1and2) ◽  
Author(s):  
Ms. Mamta Shah
Keyword(s):  
Stock Price ◽  
Implied Volatility ◽  
Safety Net ◽  
Volatility Smile ◽  
Model Case ◽  
Underlying Asset ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Option Strategies ◽  
Additional Income

The power of options lies in their versatility. It enables the investors to adjust position according to any situation that arises. Options can be speculative or conservative. This means investor can do everything from protecting a position from a decline to outright betting on the movement of a market or index. Options can enable the investor to buy a stock at a lower price, sell a stock at a higher price, or create additional income against a long or short stock position. One can also uses option strategies to profit from a movement in the price of the underlying asset regardless of market direction. the responsible act and safe thing to do. Options provide the same kind of safety net for trades and investments already committed, which is known as hedging. The research paper is based on Black Scholes Model. The study includes the Implied Volatility Test and Volatility Smile Test. This study also includes the solver available in MS Excel. This study is based on stock price of Reliance and Tata Motors.

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