Tumgik
#shuyi tan
Text
Pricing Convertible Bonds Using a Neural Networks Based Approach
A convertible bond is a bond that can be converted into a certain amount of the company's stock at specified prices and dates. Convertible bonds are usually issued by young, growing companies that want to raise capital without giving up too much control of their company. For example, a company might issue a $1,000 bond that can be converted into 100 shares of the company's stock at $10 per share. If the company's stock price increases to $20 per share, the bondholder can convert their bond into shares and immediately sell them for a $1,000 profit.
Since a convertible bond is part debt, part equity, its valuation is more complex. Reference [1] summarized well the challenging problems when pricing a convertible bond,
Convertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides, embedded options, such as conversion, call, and put provisions are often restricted to certain periods, may vary over time, and are subject to additional path-dependent features of the state variables. Moreover, the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties.
The usual methods for pricing convertible bonds are:
Binomial or trinomial trees
Partial Differential Equations
Monte-Carlo simulations
The article proposed a novel approach for pricing convertible bonds,
In this paper, we propose DeepPricing, a novel data-driven convertible bonds pricing model, which is inspired by the recent success of generative adversarial networks (GAN), to address the above challenges. The method introduces a new financial time-series generative adversarial networks (FinGAN), which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions, the long-range dependence, and the asymmetry structure etc., and then transit to its risk-neutral distribution. Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models.
The authors tested their method in the Chinese market and the results are promising.
References
[1] Xiaoyu Tan , Zili Zhang, Xuejun Zhao, and Shuyi Wang, DeepPricing: pricing convertible bonds based on financial time-series generative adversarial networks, Financial Innovation (2022) 8:64
Originally Published Here: Pricing Convertible Bonds Using a Neural Networks Based Approach
from Harbourfront Technologies - Feed https://harbourfronts.com/pricing-convertible-bonds-neural-networks/
0 notes
cantseemtohide · 3 years
Text
Tumblr media
Excuse me, what are you doing? A startled Zoubi wondered.
And then, as her bowl of cat food floated into the air: Noooooooo!
49 notes · View notes
reportwire · 2 years
Text
Improbable Research » Blog Archive
Improbable Research » Blog Archive
Bat Chat, the podcast produced by the Bat Conservation Trust, visited with Ig Nobel Prize winner Gareth Jones. The 2010 Ig Nobel Prize for Biology was awarded to Libiao Zhang, Min Tan, Guangjian Zhu, Jianping Ye, Tiyu Hong, Shanyi Zhou, and Shuyi Zhang of China, and Gareth Jones of the University of Bristol, UK, for scientifically documenting fellatio in fruit bats. The team documented that…
Tumblr media
View On WordPress
0 notes
cantseemtohide · 3 years
Text
Tumblr media
Soufiane looked at the second raging fire since they'd moved in. She's doing this deliberately isn't she, he said darkly to himself. So he got his revenge the only way he knew how and extinguished Shuyi's head.
43 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
Oussie joined Soufiane and Shuyi at the table.
'It's his favourite place,' said Soufiane. Shuyi was not about to accept this.
'Get off this table at once,' she lectured the cat.
The cat did not move.
'Get off this table or I will call the police!'
37 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
I suppose in theory a ghost should be able to do this but I'm guessing they're not meant to 🤔
36 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
She looks a bit intimidating but I like to think Zoubi was encouraging Oussie to join her at the window to admire the wonderful view of a brick wall which she has been enjoying all this time. Oussie decided to stay on the floor though.
32 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
the action in Shuyi's queue said admire but she didn't seem very admiring of Soufiane's autograph of Judith Ward.
31 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
Shuyi has got to be the angriest sim I have ever played.
29 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
...and we all know why!
30 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
It was somehow inevitable that Shuyi's childish prank was witnessed by Raj who was just leaving number 18. Today's incident would get a mention on the noticeboard that was for sure.
35 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
'So how are you finding it in San Myshuno? I'm new to the city myself!' said Jordan Mayer cheerfully.
'Well,' Amandine began cautiously, avoiding looking at her ghostly flatmate. 'The apartment isn't exactly... what we were expecting.'
43 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
Shuyi may be angry but she's always got time for the cats 👍
27 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
Geoffrey didn't appreciate Shuyi laughing at his terror.
31 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
Shuyi possessed the smoke alarm next 😁
Didn't know they could do that... what has she got in mind?
41 notes · View notes
cantseemtohide · 3 years
Text
Tumblr media
And now she's angry again.
37 notes · View notes