scholarly journals A Time-Series Approach to Non-Self-Financing Hedging in a Discrete-Time Incomplete Market

2008 ◽  
Vol 2008 ◽  
pp. 1-20 ◽  
Author(s):  
N. Josephy ◽  
L. Kimball ◽  
V. Steblovskaya
Keyword(s):  
New York ◽  
Time Series ◽  
Stock Price ◽  
Stock Exchange ◽  
Market Price ◽  
Incomplete Market ◽  
Model Parameters ◽  
York Stock Exchange ◽  
Risk Criterion ◽  
Time Series Approach

We present an algorithm producing a dynamic non-self-financing hedging strategy in an incomplete market corresponding to investor-relevant risk criterion. The optimization is a two-stage process that first determines market calibrated model parameters that correspond to the market price of the option being hedged. In the second stage, an optimal set of model parameters is chosen from the market calibrated set. This choice is based on stock price simulations using a time-series model for stock price jump evolution. Results are presented for options traded on the New York Stock Exchange.

scholarly journals The Market Reaction Around Ex-Dates of Stock Splits Before and After Decimalization

2005 ◽  
Vol 08 (02) ◽  
pp. 201-216 ◽  
Author(s):  
Robin K. Chou ◽  
Wan-Chen Lee ◽  
Sheng-Syan Chen
Keyword(s):  
New York ◽  
Stock Price ◽  
Trading Volume ◽  
Stock Exchange ◽  
New York Stock Exchange ◽  
Short Term ◽  
Tick Size ◽  
York Stock Exchange ◽  
Before And After ◽  
Abnormal Trading Volume

This paper examines the stock price behavior around the ex-split dates both before and after the decimalization on the New York Stock Exchange (NYSE). We find that the abnormal ex-split day returns decrease and the abnormal trading volume increases in the 1/16th and decimal pricing eras, relative to the 1/8th pricing era. These findings are consistent with the microstructure-based explanations for the ex-day price movements. Our study also supports the hypothesis that short-term traders perform arbitrage activities during the ex-split dates when transaction costs become lower after the tick size is reduced.

scholarly journals Time for Business: Business Ethics, Sustainability, and Giorgio Agamben’s ‘Messianic Time’

2016 ◽  
Vol 2 (3) ◽  
pp. 39-51
Author(s):  
Jeremy Kidwell
Keyword(s):  
New York ◽  
Stock Price ◽  
Stock Exchange ◽  
Recent Attempt ◽  
New York Stock Exchange ◽  
York Stock Exchange ◽  
Modern Business ◽  
Future Consequences ◽  
Economic Future ◽  
Hourly Basis

Contemporary business continues to intensify its radical relation to time. The New York Stock Exchange recently announced that in pursuing (as traders call it) the ‘race to zero’ they will begin using laser technology originally developed for military communications to send information about trades nearly at the speed of light. This is just one example of short-term temporal rhythms embedded in the practices of contemporary firms which watch their stock price on an hourly basis, report their earnings quarterly, and dissolve future consequences and costs through discounting procedures. There is reason to believe that these radical conceptions of time and its passing impair the ability of businesses to function in a morally coherent manner. In the spirit of other recent critiques of modern temporality such as David Couzen Hoys The Time of Our Lives, in this paper, I present a critique of the temporality of modern business. In response, I assess the recent attempt to provide an alternative account of temporality using theological concepts by Giorgio Agamben. I argue that Agamben’s more integrative account of messianic time provides a richer ambitemporal account which might provide a viable temporality for a new sustainable economic future.

Analysis of Big Data Using Two Mapper Files in Hadoop

2021 ◽  
Vol 13 (1) ◽  
pp. 69-77
Author(s):  
Jyotsna Malhotra ◽  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
New York ◽  
Big Data ◽  
Stock Price ◽  
Stock Exchange ◽  
Uncertain Data ◽  
Real Life ◽  
Big Data Analytics ◽  
Private Companies ◽  
Government Organizations ◽  
York Stock Exchange

Nowadays, a large amount of valuable uncertain data is easily available in many real-life applications. Many industries and government organizations can exploit this data to extract valuable information. This information can help the managers to enhance their strategies and optimize their plans in making decisions. In fact, various private companies and governments have launched programs with investments and funds in order to maximize profits and optimize resources. This vast amount of data is called big data. The analysis of big data is important for future growth. This paper depicts big data analytics through experimental results. In this paper, data for New York stock exchange has been analyzed using two mapper files in Hadoop. For each year, the calculation of maximum and minimum price of every stock exchange and the average stock price is done.

scholarly journals QUANTILE CORRELATIONS: UNCOVERING TEMPORAL DEPENDENCIES IN FINANCIAL TIME SERIES

2015 ◽  
Vol 18 (07) ◽  
pp. 1550044
Author(s):  
THILO A. SCHMITT ◽  
RUDI SCHÄFER ◽  
HOLGER DETTE ◽  
THOMAS GUHR
Keyword(s):  
New York ◽  
Time Series ◽  
Correlation Function ◽  
Stochastic Processes ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Financial Time ◽  
York Stock Exchange ◽  
Intraday Data ◽  
Temporal Dependencies

We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S&P500 stocks from the New York Stock Exchange (NYSE). After establishing an empirical overview, we compare the quantile-based correlation function to stochastic processes from the GARCH family and find striking differences. This motivates us to propose the quantile-based correlation function as a powerful tool to assess the agreements between stochastic processes and empirical data.

International Stock Price Co-movement

2013 ◽  
Vol 12 (3) ◽  
pp. 157-191 ◽  
Author(s):  
Kenjiro Hirayama ◽  
Yoshiro Tsutsui
Keyword(s):  
New York ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
New York Stock Exchange ◽  
Common Shocks ◽  
Standard Time ◽  
Minute Period ◽  
York Stock Exchange ◽  
International Investors

Two possible causes of international stock price co-movement are examined: the existence of global common shocks and portfolio adjustments by international investors. Empirical analyses indicate that the former explains a significant part of the co-movement and the latter is unlikely to play an important role. We extend the analysis to intra-day high-frequency data. For example, when the Tokyo Stock Exchange begins its daily trading at 9:00 A.M. Japan Standard Time (JST), stock prices in Tokyo exhibit responses to preceding changes in New York. An analysis with minute-byminute data indicates that Tokyo's response to New York dissipates within about six minutes after opening. On the other hand, when the New York Stock Exchange (NYSE) opens at 9:30 A.M. Eastern Standard Time (EST), its response to Tokyo dissipates within 14 minutes. Thus, the movement of stock prices is transmitted rapidly across countries. Finally real-time simultaneous interactions between Shanghai (Shenzhen) and Tokyo are analyzed for a 30-minute period in the morning and a 60-minute period in the afternoon. Investors in Tokyo are watching stock prices in Shanghai, but not vice versa. Tight regulations on Chinese investors to prevent them from holding foreign stocks may be the reason why they do not pay any attention to stock price movements in Tokyo.

scholarly journals STATISTICAL PROPERTIES OF STATISTICAL ENSEMBLES OF STOCK RETURNS

2000 ◽  
Vol 03 (03) ◽  
pp. 405-408 ◽  
Author(s):  
FABRIZIO LILLO ◽  
ROSARIO N. MANTEGNA
Keyword(s):  
New York ◽  
Stock Returns ◽  
Stock Price ◽  
Stock Exchange ◽  
Temporal Correlation ◽  
Statistical Properties ◽  
Central Moments ◽  
York Stock Exchange ◽  
Daily Stock Returns ◽  
Correlation Properties

We select n stocks traded in the New York Stock Exchange and form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fluctuating in time and are stochastic processes themselves. We characterize the statistical properties of central moments by investigating their probability density function and temporal correlation properties.

Sign in / Sign up

Export Citation Format

Share Document