scholarly journals Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 19
Author(s):  
Smail Tigani ◽  
Hasna Chaibi ◽  
Rachid Saadane
Keyword(s):  
Foreign Exchange Market ◽  
Expectation Maximization Algorithm ◽  
Kernel Density ◽  
Gaussian Mixture ◽  
Density Estimator ◽  
Exchange Market ◽  
Computation Result ◽  
Volatility Clustering ◽  
Clustering Model ◽  
Proposed Model

This paper carried out a hybrid clustering model for foreign exchange market volatility clustering. The proposed model is built using a Gaussian Mixture Model and the inference is done using an Expectation Maximization algorithm. A mono-dimensional kernel density estimator is used in order to build a probability density based on all historical observations. That allows us to evaluate the behavior’s probability of each symbol of interest. The computation result shows that the approach is able to pinpoint risky and safe hours to trade a given currency pair.

scholarly journals Mixture Of Probabilistic Factor Analyzers For Market Risk Measurement: Empirical Evidence From The Tunisian Foreign Exchange Market

2017 ◽  
Vol 7 (2) ◽  
pp. 158-169
Author(s):  
Mohamed Nidhal Mosbahi ◽  
Mohamed Saidane ◽  
Sarra Messabeb
Keyword(s):  
Foreign Exchange ◽  
Value At Risk ◽  
Foreign Exchange Market ◽  
Time Window ◽  
Gaussian Mixture Models ◽  
Likelihood Estimation ◽  
Gaussian Mixture ◽  
Exchange Market ◽  
Reduced Dimension ◽  
Analysis Models

In this paper, we propose a new approach for Basel-Compliant Value-at-Risk (VaR) estimation in financial portfolio risk management, which combines Gaussian Mixture Models with probabilistic factor analysis models. This new mixed specification provides an alternative, compact, model to handle co-movements, heterogeneity and intra-frame correlations in financial data. This results in a model which concurrently performs clustering and dimensionality reduction, and can be considered as a reduced dimension mixture of probabilistic factor analyzers. For maximum likelihood estimation we have used an iterative approach based on the Alternating Expectation Conditional Maximization (AECM) algorithm. Using a set of historical data in a rolling time window, from the Tunisian foreign exchange market, the model structure as well as its parameters are determined and estimated. Then, the fitted model combined with a modified Monte-Carlo simulation algorithm was used to predict the VaR. Through a Backtesting analysis, we found that this new specification exhibits a good fit to the data compared to other competing approaches, improves the accuracy of VaR prediction, possesses more flexibility, and can avoid serious violations when a financial crisis occurs.

Modeling Volatility of Exchange Rates Returns in the Nigerian Foreign Exchange Market in the Presence of Non-Gaussian Errors

2019 ◽  
Vol 1 ◽  
pp. 246-258
Author(s):  
D A Kuhe ◽  
J T Aarga ◽  
I T Ayigege
Keyword(s):  
Exchange Rates ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Good News ◽  
Foreign Exchange Rates ◽  
Exchange Market ◽  
Volatility Clustering ◽  
Long Run ◽  
Modeling Volatility ◽  
Non Gaussian

This study investigates volatility behaviour of exchange rates returns of Naira against CFA, Euro, Great British Pounds, US Dollar, West African Unit of Account (WAUA) and Japanese Yen in Nigeria using historical volatility approach as well as symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (GARCH) models in the presence of non-Gaussian errors. The study utilizes daily quotations of these exchange rates from 12/11/2001 to 04/13/2018 making a total of 4008 observations each. Historical (annualized) volatility approach as well as symmetric GARCH (1,1) and asymmetric EGARCH (1,1) models were used to model the exchange rates return series. Results showed that CFA and USD have the highest and least annualized volatilities (market risk) respectively among the six exchange rates returns as measured by historical approach. The symmetric GARCH (1,1) model showed volatility clustering with evidence of shock persistence in the six exchange rates return series. The asymmetric EGARCH (1,1) model found evidence of asymmetry and leverage effects in the Nigerian foreign exchange market indicating that negative shocks (bad news) generate more volatility than positive shocks (good news) of similar magnitudes. All the estimated models were found to be stationary and mean reverting indicating the predictability and stability of the conditional variances of the foreign exchange rates returns. This result suggests that no matter how high or low the foreign exchange rates shall move in the exchange market, they shall eventually return to their long-run averages. Stationary and mean reverting stocks provide good and long term investment opportunities for investors.

A Study On Technical Analysis With Reference To International Forex

Think India ◽  
2019 ◽  
Vol 22 (3) ◽  
pp. 1129-1144
Author(s):  
Bichith C. Sekhar ◽  
A. Umamaheswari
Keyword(s):  
Foreign Exchange ◽  
Abnormal Return ◽  
Foreign Exchange Market ◽  
Relative Strength ◽  
Exchange Market ◽  
Strength Index ◽  
Technical Tool ◽  
Alpha Beta ◽  
Decentralized Market ◽  
Currency Conversion

The foreign exchange market (Forex, FX, or currency market) is a global decentralized market for the trading of currencies. The foreign exchange market assists international trade and investments by enabling currency conversion. Our study is to test the technical tools to analyze about the technical impact and its return in the market.  For this purpose 13 cross currency pairs were taken as sample size and Jensen’s Alpha, Beta, Relative Strength Index, and Buy and Hold Abnormal Return were used as technical tool for analysis and the conclusion is that it’s not preferred to invest in JPY pairs as the volatility and the return are not up to the mark and its preferred to invest in EURCAD as the return was high when compared to other scripts and the market was moving accordingly to its cross currency pair.

Chartists, Fundamentalists, and Dispersion in the Foreign Exchange Market

2009 ◽  
Author(s):  
Ron Jongen ◽  
Christian C. P. Wolff ◽  
Remco C. J. Zwinkels ◽  
Willem F. C. Verschoor
Keyword(s):  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Exchange Market

Foreign Exchange Market and Equity Risk Premium Forecasting

2013 ◽  
Author(s):  
Yuchen Wang ◽  
Dashan Huang ◽  
Jun Tu
Keyword(s):  
Foreign Exchange ◽  
Risk Premium ◽  
Foreign Exchange Market ◽  
Exchange Market ◽  
Equity Risk Premium ◽  
Equity Risk

Is the 52-Week High Momentum Strategy Profitable in the Foreign Exchange Market?

2015 ◽  
Author(s):  
Ahmad Raza ◽  
Ben R. Marshall ◽  
Nuttawat Visaltanachoti
Keyword(s):  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
High Momentum ◽  
Exchange Market ◽  
Momentum Strategy
Sign in / Sign up

Export Citation Format

Share Document