THE CALL OPTION IMPLICIT IN PROXY CONTESTED FIRMS

1992 ◽  
Vol 1 (2) ◽  
pp. 53-67 ◽  
Author(s):  
G.D. Hancock
Keyword(s):  
1968 ◽  
Vol 24 (5) ◽  
pp. 149-151 ◽  
Author(s):  
Jerome Bracken
Keyword(s):  

2014 ◽  
Vol 49 (3) ◽  
pp. 541-574 ◽  
Author(s):  
George Pennacchi ◽  
Theo Vermaelen ◽  
Christian C. P. Wolff

AbstractThis paper introduces and analyzes a new form of contingent convertible: a call option enhanced reverse convertible (COERC). If an issuing bank’s market value of capital breaches a trigger, COERCs convert to many new equity shares that would heavily dilute existing shareholders, except that shareholders have the option to purchase these shares at the bond’s par value. COERCs have low risk: They are almost always fully repaid in cash. Yet, they reduce government bailouts by replenishing a bank’s capital. COERCs’ design also avoids problems with market-value triggers, such as manipulation or panic, while reducing moral hazard and debt overhang.


Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2890
Author(s):  
Alessio Giorgini ◽  
Rogemar S. Mamon ◽  
Marianito R. Rodrigo

Stochastic processes are employed in this paper to capture the evolution of daily mean temperatures, with the goal of pricing temperature-based weather options. A stochastic harmonic oscillator model is proposed for the temperature dynamics and results of numerical simulations and parameter estimation are presented. The temperature model is used to price a one-month call option and a sensitivity analysis is undertaken to examine how call option prices are affected when the model parameters are varied.


2008 ◽  
Vol 6 (3) ◽  
pp. 557-568 ◽  
Author(s):  
Jungmin Choi ◽  
Kyounghee Kim

2021 ◽  
Vol 22 (1) ◽  
pp. 30-39
Author(s):  
Riko Hendrawan ◽  
Anggadi Sasmito

The purpose of this study is to examine the implementation of option contracts using Black Scholes and GARCH on the LQ45 index using the long straddle strategy. This study uses time-series data as a time frame for conducting research, using a sample of closing price data for the LQ 45 daily index for 2009-2018. For the test the model, we used the secondary data of the closing stock price index from February 28, 2009 to March 31, 2019The results of this study are seen by comparing the average percentage value of Average Mean Squared Error (AMSE) of Black Scholes and GARCH with the application of a long straddle strategy, where the smaller the percentage value, the better the model will be. Within one month of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 2.77% and the put option of 1.56%. Within two months of option contract due date, GARCH is better than Black Scholes, with an error value on the call option of 8.12% and the put option of 4.00%. Within three months of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 12.38% and on the put option of 5.50%. The long straddle strategy in the LQ45 index only reached a maximum of 60% of possible profits, with an average of around 30% possible profits.


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