A State-Price Volatility Index for China's Stock Market

2015 ◽  
Vol 56 (3) ◽  
pp. 607-626 ◽  
Author(s):  
Michael O'Neill ◽  
Kent Wang ◽  
Zhangxin Frank Liu
Keyword(s):  
Stock Market ◽  
Price Volatility ◽  
State Price ◽  
Volatility Index

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

scholarly journals Sugar Prices vs. Financial Market Uncertainty in the Time of Crisis: Does COVID-19 Induce Structural Changes in the Relationship?

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 93
Author(s):  
Pavel Kotyza ◽  
Katarzyna Czech ◽  
Michał Wielechowski ◽  
Luboš Smutka ◽  
Petr Procházka
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Financial Market ◽  
Structural Breaks ◽  
Structural Changes ◽  
Global Financial Crisis ◽  
Market Uncertainty ◽  
Volatility Index ◽  
The Global Financial Crisis ◽  
The Relationship

Securitization of the agricultural commodity market has accelerated since the beginning of the 21st century, particularly in the times of financial market uncertainty and crisis. Sugar belongs to the group of important agricultural commodities. The global financial crisis and the COVID-19 pandemic has caused a substantial increase in the stock market volatility. Moreover, the novel coronavirus hit both the sugar market’s supply and demand side, resulting in sugar stock changes. The paper aims to assess potential structural changes in the relationship between sugar prices and the financial market uncertainty in a crisis time. In more detail, using sequential Bai–Perron tests for structural breaks, we check whether the global financial crisis and the COVID-19 pandemic have induced structural breaks in that relationship. Sugar prices are represented by the S&P GSCI Sugar Index, while the S&P 500 option-implied volatility index (VIX) is used to show stock market uncertainty. To investigate the changes in the relationship between sugar prices and stock market uncertainty, a regression model with a sequential Bai–Perron test for structural breaks is applied for the daily data from 2000–2020. We reveal the existence of two structural breaks in the analysed relationship. The first breakpoint was linked to the global financial crisis outbreak, and the second occurred in December 2011. Surprisingly, the COVID-19 pandemic has not induced the statistically significant structural change. Based on the regression model with Bai–Perron structural changes, we show that from 2000 until the beginning of the global financial crisis, the relationship between the sugar prices and the financial market uncertainty was insignificant. The global financial crisis led to a structural change in the relationship. Since August 2008, we observe a significant and negative relationship between the S&P GSCI Sugar Index and the S&P 500 option-implied volatility index (VIX). Sensitivity analysis conducted for the different financial market uncertainty measures, i.e., the S&P 500 Realized Volatility Index confirms our findings.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

scholarly journals Sensitivity of the Art Market to Price Volatility

2020 ◽  
Vol 2 (26) ◽  
pp. 11-36
Author(s):  
Krzysztof Borowski
Keyword(s):  
Stock Market ◽  
Art Market ◽  
Price Volatility ◽  
Income Risk ◽  
Rates Of Return ◽  
Calendar Effects ◽  
Post War ◽  
Investment Portfolios ◽  
Market Indexes ◽  
Almost All

The purpose of the article: The art market becomes very popular among investors, when there is strong turbulence on the stock market. In times of calm, the art market is used by investors to diversify risk and build more efficient investment portfolios according to the Markovitz’s theory. The aim of this paper is to: (i) present the peculiarity of investment on the art market, represented by art market indexes in comparison to traditional investments in other financial market segments (money market, equity indexes and commodity market), (ii) to verify the hypothesis of normality of the distribution of rates of return of the analyzed art market indices as well as (iii) to analyze calendar effects occurrence on the art market.Methodology: Comparison of rates of return on the stock, bond, commodity and money markets with rates on the art market in four different time intervals. For each of the analyzed periods, an income-risk map was presented, taking into account the spectrum of financial instruments, including six art indexes: Old Masters, 19th Century, Modern art, Post War art, Contemporary art and Global art. The hypothesis of normality of the distribution of rates of return of the art market indices for four analyzed periods was verified with the use of Jarque-Bera test.Results of the research: Comparison of rates of return on the stock market and art market leads to the conclusion that their relationship depends on the period chosen. For two of the analyzed periods, the rates of return on the stock market were higher than on the art market, but for others periods, the opposite. The distribution of quarterly rates of return resulted to be a normal distribution for almost all of analyzed indices and time periods. Calendar effects were observed in the case of four analyzed indexes.

scholarly journals The Dynamic Relationship between Crude Oil Prices and Stock Market Price Volatility in Nigeria: A Cointegrated VAR-GARCH Model

2019 ◽  
pp. 1-12
Author(s):  
David Adugh Kuhe
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Market Price ◽  
Garch Model ◽  
Price Volatility ◽  
Oil Prices ◽  
Market Prices ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run

This study investigates the dynamic relationship between crude oil prices and stock market price volatility in Nigeria using cointegrated Vector Generalized Autoregressive conditional Heteroskedasticity (VAR-GARCH) model. The study utilizes monthly data on the study variables from January 2006 to April 2017 and employs Dickey-Fuller Generalized least squares unit root test, simple linear regression model, unrestricted vector autoregressive model, Granger causality test and standard GARCH model as methods of analysis. Results shows that the study variables are integrated of order one, no long-run stable relationship was found to exist between crude oil prices and stock market prices in Nigeria. Both crude oil prices and stock market prices were found to have positive and significant impact on each other indicating that an increase in crude oil prices will increase stock market prices and vice versa. Both crude oil prices and stock market prices were found to have predictive information on one another in the long-run. A one-way causality ran from crude oil prices to stock market prices suggesting that crude oil prices determine stock prices and are a driven force in Nigerian stock market. Results of GARCH (1,1) models show high persistence of shocks in the conditional variance of both returns. The conditional volatility of stock market price log return was found to be stable and predictable while that of crude oil price log return was found to be unstable and unpredictable, although a dependable and dynamic relationship between crude oil prices and stock market prices was found to exist. The study provides some policy recommendations.

scholarly journals Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jian Wang ◽  
Junseok Kim
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Rapid Development ◽  
Price Trend ◽  
Technical Indicators ◽  
Volatility Index ◽  
Fixed Weight ◽  
The Stability

With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.

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