scholarly journals Pricing of Margin Call Stock Loan Based on the FMLS

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kaili Xiang ◽  
Peng Hu ◽  
Xiao Li
Keyword(s):  
Free Boundary ◽  
Normal Distribution ◽  
Stock Price ◽  
Empirical Test ◽  
Stable Process ◽  
Standard Normal Distribution ◽  
Asymmetric Distribution ◽  
Finite Moment ◽  
The Impact ◽  
Stock Loan

In common stock loan, lenders face the risk that their loans will not be repaid if the stock price falls below loan, which limits the issuance and circulation of stock loans. The empirical test suggests that the log-return series of stock price in the US market reject the normal distribution and admit instead a subclass of the asymmetric distribution. In this paper, we investigate the model of the margin call stock loan problem under the assumption that the return of stock follows the finite moment log-stable process (FMLS). In this case, the pricing model of the margin call stock loan can be described by a space-fractional partial differential equation with a time-varying free boundary condition. We transform the free boundary problem to a linear complementarity problem, and the fully-implicit finite difference method that we used is unconditionally stable in both the integer and fractional order. The numerical experiments are carried out to demonstrate differences of the margin call stock loan model under the FMLS and the standard normal distribution. Last, we analyze the impact of key parameters in our model on the margin call stock loan evaluation and give some reasonable explanation.

scholarly journals Pricing Stock Loans with the CGMY Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Congyin Fan ◽  
Chunhao Zhou
Keyword(s):  
Stock Price ◽  
Asymmetric Distribution ◽  
Pricing Model ◽  
Fractional Partial Differential Equation ◽  
Penalty Term ◽  
First Order ◽  
Fully Implicit ◽  
Condition Problem ◽  
Implicit Difference Schemes ◽  
Stock Loan

The empirical research shows that the log-return of stock price in finance market rejects the normal distribution and admits a subclass of the asymmetric distribution. Hence, the pricing problem of stock loan is investigated under the assumption that the log-return of stock price follows the CGMY process in this work. Under this framework, the pricing model of stock loan can be described by a free boundary condition problem of space-fractional partial differential equation (FPDE). First of all, in order to change the original model defined in a fixed domain, a penalty term is introduced, and then a first order fully implicit difference schemes is developed. Secondly, based on the numerical scheme, we prove the stock loan value generated by our method does not fall below the value obtained when the contract of stock loan is exercised early. Finally, the numerical experiments are implemented and the impacts of key parameters in the CGMY model on the value and optimal redemption price of stock loan are analyzed, and some reasonable explanation should be given.

scholarly journals Stock loan valuation based on the Finite Moment Log-Stable process

2018 ◽  
Vol 75 (2) ◽  
pp. 374-387 ◽  
Author(s):  
Congyin Fan ◽  
Kaili Xiang ◽  
Shanzhen Chen
Keyword(s):  
Stable Process ◽  
Finite Moment ◽  
Stock Loan

scholarly journals Spectral risk measure of holding stocks in the long run

2020 ◽  
Vol 295 (1) ◽  
pp. 75-89
Author(s):  
Zsolt Bihary ◽  
Péter Csóka ◽  
Dávid Zoltán Szabó
Keyword(s):  
Stock Price ◽  
Risk Measures ◽  
Risk Measure ◽  
Stable Process ◽  
Important Special Case ◽  
Finite Moment ◽  
Holding Period ◽  
Long Run ◽  
Spectral Risk Measures ◽  
Spectral Risk Measure

AbstractWe investigate how the spectral risk measure associated with holding stocks rather than a risk-free deposit, depends on the holding period. Previous papers have shown that within a limited class of spectral risk measures, and when the stock price follows specific processes, spectral risk becomes negative at long periods. We generalize this result for arbitrary exponential Lévy processes. We also prove the same behavior for all spectral risk measures (including the important special case of Expected Shortfall) when the stock price grows realistically fast and when it follows a geometric Brownian motion or a finite moment log stable process. This result would suggest that holding stocks for long periods has a vanishing downside risk. However, using realistic models, we find numerically that spectral risk initially increases for a significant amount of time and reaches zero level only after several decades. Therefore, we conclude that holding stocks has spectral risk for all practically relevant periods.

An Investigation into the Impact of Information on Stock Price Volatilities in Vietnam

2015 ◽  
Vol 22 (3) ◽  
pp. 59-80
Author(s):  
NGUYEN KHAC QUOC BAO ◽  
NGUYEN HUU HUY NHUT ◽  
TRAN NGUYEN HUY NHAN
Keyword(s):  
Stock Price ◽  
The Impact

STUDY OF INACCURACY IN THE OPERATION OF THE MAIN CONTROLLER OF STB MACHINE

2019 ◽  
Keyword(s):  
Normal Distribution ◽  
Stable Process ◽  
Random Errors ◽  
Actual Length ◽  
Experimental Data Processing ◽  
Set Up ◽  
Main Regulator ◽  
Transmission Gear ◽  
Selection Of

At production of fabrics, including fabrics for agricultural purpose, an important role is played by the cor-rect adjustment of operation of machine main regulator. The quality of setup of machine main controller is determined by the proper selection of rotation angle of warp beam weaving per one filling thread. In the pro-cess of using the regulator as a result of mistakes in adjustment, wear of transmission gear and backlashes in connections of details there are random changes in threads length. The purpose of the article is the research of property of random errors of basis giving by STB machine regulator. Mistakes can be both negative, and positive. In case of emergence only negative or only positive mistakes operation of the machine becomes im-possible as there will be a consecutive accumulation of mistakes. As a result of experimental data processing for stable process of weaving and the invariable diameter of basis threads winding of threads it is revealed that the random error of giving is set up as linear function of the accidental length having normal distribution. Measurements of accidental deviations in giving of a basis by the main regulator allowed to construct a curve of normal distribution of its actual length for one pass of weft thread. The presented curve of distribution of random errors in giving of a basis is the displaced curve of normal distribution of the accidental sizes. Also we define the density of probability of normal distribution of basis giving errors connected with a margin er-ror operation of the main regulator knowing of which allows to plan ways of their decrease that is important for improvement of quality of the produced fabrics.

The Impact of Blockholder Activism on the Information Environment of Stock Price

2017 ◽  
Author(s):  
Stephen mname Brown ◽  
Elaine mname Hutson ◽  
Michael mname Wang ◽  
Jin mname Yu
Keyword(s):  
Stock Price ◽  
Information Environment ◽  
The Impact

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

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