The Impact of Macroeconomic and Financial Variables on Market Risk: Evidence from International Equity Returns

2001 ◽  
Author(s):  
Dilip K. Patro ◽  
John K. Wald ◽  
Yangru Wu
Keyword(s):  
Market Risk ◽  
Equity Returns ◽  
Financial Variables ◽  
International Equity ◽  
The Impact

scholarly journals A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli
Keyword(s):  
Investment Decision ◽  
Theoretical Work ◽  
Financial Asset ◽  
Dynamic Features ◽  
Asset Return ◽  
Financial Variables ◽  
Ann Models ◽  
Hidden Layer ◽  
Association Structure ◽  
The Impact

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

scholarly journals The Influence of Trading Locations on Equity Returns

2016 ◽  
Vol 12 (12) ◽  
pp. 188
Author(s):  
Nguyen N.T. Vo
Keyword(s):  
Risk Premium ◽  
Stock Price ◽  
Listed Companies ◽  
Equity Returns ◽  
Stock Exchanges ◽  
Home Market ◽  
Trade Effect ◽  
Market Index ◽  
Highly Correlated ◽  
The Impact

This paper evaluates the impact of trading locations on equity returns by examining the stock price behaviour of three Anglo-Dutch dual-listed companies which result from mergers where two corporations agree to function as a single operating business, but maintain separate identities. The shares of these stocks are traded not only in their home market but also on several US stock exchanges in the form of American Depository Receipts. Regressing the return differentials on these dual-listed and cross-listed stocks on the relative market index returns and currency changes provides evidence of an apparent violation of the Law of One Price. The regression results show that the return on each part of dual-listed companies is highly correlated with the market on which it is most intensively traded. Similarly, returns on cross-listed stocks have considerably higher co-movement with US market indices and considerably lower co-movement with home-market indices than their home-market counterparts. Market risk premium is not a significant explanatory variable of the location of trade effect.

scholarly journals The Caribbean and its Linkages with the World

2019 ◽  
Vol 2019 (256) ◽  
Author(s):  
Mauricio Vargas ◽  
Daniela Hess
Keyword(s):  
Global Economy ◽  
Caribbean Region ◽  
Financial Variables ◽  
The World ◽  
Trading Partners ◽  
Global Var ◽  
The Caribbean ◽  
Using Data ◽  
The Impact ◽  
The U.S

Using data from 1980-2017, this paper estimates a Global VAR (GVAR) model taylored for the Caribbean region which includes its major trading partners, representing altogether around 60 percent of the global economy. We provide stilyzed facts of the main interrelations between the Caribbean region and the rest of the world, and then we quantify the impact of external shocks on Caribbean countries through the application of two case studies: i) a change in the international price of oil, and ii) an increase in the U.S. GDP. We confirmed that Caribbean countries are highly exposed to external factors, and that a fall in oil prices and an increase in the U.S. GDP have a positive and large impact on most of them after controlling for financial variables, exchange rate fluctuations and overall price changes. The results from the model help to disentangle effects from various channels that interact at the same time, such as flows of tourists, trade of goods, and changes in economic conditions in the largest economies of the globe.

scholarly journals Does the Financial Status of Company Affect the Bond Credit Rating? ——Empirical Evidence from China's Shanghai and Shenzhen Stock Exchanges

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yuyan Cai
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
Corporate Bonds ◽  
Principal Component ◽  
Corporate Bond ◽  
Stock Exchanges ◽  
Analysis Method ◽  
Financial Status ◽  
Financial Variables ◽  
The Impact

This article takes the companies that publicly issued corporate bonds on the Shanghai and Shenzhen Stock Exchanges from 2006 to 2018 as the research objects selecting six aspects that comprehensively reflect the 17 financial variables in 6 aspects: profitability, operating ability, bond repayment ability, development ability, cash flow and market value of the company. Principal component analysis method and factor analysis method are used to extract the principal factors of these financial indicator variables. That is how an ordered multi-classification Logistic regression model is constructed to test the impact of the Shanghai and Shenzhen Stock Exchanges’ financial status on the corporate bond credit rating. It turns out that the financial status of the Shanghai and Shenzhen Stock Exchanges have an important impact on the credit rating of corporate bonds. The financial status has a greater impact on corporate bonds with credit ratings of A- and AA-, while it has a smaller impact on corporate bonds with credit ratings above AA. The results of this article can help individual and institutional investors prevent risks from investing.

scholarly journals Measuring Market Risk of Commercial Banks Implementing VaR with Historical Simulation Approach

2021 ◽  
pp. 79-99
Author(s):  
Minhaz-Ul Haq
Keyword(s):  
Stock Prices ◽  
Commercial Banks ◽  
Value At Risk ◽  
First Generation ◽  
Market Risk ◽  
Jel Classification ◽  
Simulation Approach ◽  
Share Prices ◽  
Historical Simulation ◽  
The Impact

This paper attempts to picture the impact of the market risk of ten commercial banks located in Bangladesh with the help of a non-parametric model known as the Historical Simulation Approach over the course of eight years. These banks' daily stock prices were used as inputs and analyzed in Microsoft Excel by means of Percentile and LN function. The study revealed market risk exposure as third, second-and first-generation banks from the least to the highest. It also pointed out the ups and downs of these banks' share prices in the selected period. Further analysis showed the portfolio VaR estimation for different time intervals. JEL classification numbers: G32. Keywords: Value-at-risk, Historical Simulation, Market Risk, Confidence Interval.

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