Forecasting Value at Risk of Foreign Exchange Rate by Integrating Geometric Brownian Motion

2018 ◽  
pp. 186-198
Author(s):  
Siti Noorfaera Karim ◽  
Maheran Mohd Jaffar
Keyword(s):  
At Risk ◽  
Brownian Motion ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Value At Risk ◽  
Geometric Brownian Motion ◽  
Foreign Exchange Rate

scholarly journals MULTIFRACTAL FLUCTUATIONS IN FINANCE

2000 ◽  
Vol 03 (03) ◽  
pp. 361-364 ◽  
Author(s):  
FRANCOIS SCHMITT ◽  
DANIEL SCHERTZER ◽  
SHAUN LOVEJOY
Keyword(s):  
Brownian Motion ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Stochastic Models ◽  
Evolution Equations ◽  
Foreign Exchange Rate ◽  
Scaling Exponent ◽  
Stochastic Evolution ◽  
Stochastic Evolution Equations ◽  
Multiplicative Processes

We consider the structure functions S(q)(τ), i.e. the moments of order q of the increments X(t + τ)-X(t) of the Foreign Exchange rate X(t) which give clear evidence of scaling (S(q)(τ)∝τζ(q)). We demonstrate that the nonlinearity of the observed scaling exponent ζ(q) is incompatible with monofractal additive stochastic models usually introduced in finance: Brownian motion, Lévy processes and their truncated versions. This nonlinearity correspond to multifractal intermittency yielded by multiplicative processes. The non-analyticity of ζ(q) corresponds to universal multifractals, which are furthermore able to produce "hyperbolic" pdf tails with an exponent qD > 2. We argue that it is necessary to introduce stochastic evolution equations which are compatible with this multifractal behaviour.

scholarly journals PENENTUAN NILAI VALUE at RISK PADA SAHAM IHSG MENGGUNAKAN MODEL GEOMETRIC BROWNIAN MOTION DENGAN LOMPATAN

2015 ◽  
Vol 4 (2) ◽  
pp. 67
Author(s):  
I GEDE ARYA DUTA PRATAMA ◽  
KOMANG DHARMAWAN ◽  
LUH PUTU IDA HARINI
Keyword(s):  
At Risk ◽  
Brownian Motion ◽  
Confidence Level ◽  
Value At Risk ◽  
Model Calibration ◽  
Historical Data ◽  
Likelihood Estimation ◽  
Geometric Brownian Motion ◽  
Stock Index ◽  
Peak Over Threshold

The aim of this research was to measure the risk of the IHSG stock data using the Value at Risk (VaR). IHSG stock index data typically indicates a jump. However, Geometric Brownian Motion (GBM) model can not catch any of the jumps. To view the jumps, it is necessary that the model was then developed into a Geometric Brownian Motion (GBM) model with Jumps. On the GBM model with Jumps, returns the data are discontinuous. To determine the value of VaR, the value of return to perform the simulation model of GBM with Jumps is required. To represent processes that contain jumps, discontinuous Poisson process using the Peak-Over Threshold is required. To determine the parameters of model, calibration of historical data using the Maximum Likelihood Estimation (MLE) method is performed. VaR value for GBM model with Jumps with a 95% and 99% confidence level are -0,0580 and -0,0818 while VaR value for GBM model with a 95% and 99% confidence level are -0,0101 and -0,0199. VaR for GBM model with Jumps with a confidence level of 95% and 99% show greater than the model VaR for GBM.

Bayesian and EVT Value-At-Risk Estimates of India's Non-Financial Firms

2018 ◽  
Vol 19 (1) ◽  
pp. 50-75
Author(s):  
Pankaj Sinha ◽  
Shalini Agnihotri
Keyword(s):  
At Risk ◽  
Exchange Rate ◽  
Board Of Directors ◽  
Value At Risk ◽  
Bayesian Method ◽  
Stress Test ◽  
Value Theory ◽  
Bivariate Distribution ◽  
Foreign Exchange Rate ◽  
Financial Firms

The Companies Act 2013 has made it mandatory for firm’s Board of Directors Report to include a statement indicating elements of risk faced by companies. In the IMF report of March 2015, it is mentioned that India’s non-financial company’s external commercial borrowings rose by 107% between March 2010 to March 2014. The stress test based on exchange rate and profits demonstrated continuing high vulnerabilities of the firms. Looking at both the important factors, the current study estimates the Value-at-Risk (VaR) of 106 non-financial Indian firms. It is well a documented fact that return series is nonnormal, therefore taking bivariate distribution of return and foreign exchange rate. VaR is calculated using the extreme value theory method and Bayesian method. The results suggest that Bayesian method provides the best VaR estimates

scholarly journals A new perspective for the use of financial derivatives to hedge foreign exchange rate: L-BFGS perspective to assess the strikes and principals of plain vanilla options

Activos ◽  
2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Willie Hernández ◽  
Jairo Borray Benavides
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Financial Market ◽  
Value At Risk ◽  
Financial Derivatives ◽  
Foreign Exchange Rate ◽  
Economic Agents ◽  
Limited Memory ◽  
Financial Products ◽  
New Perspective

The international relationships between nations and economic agents have evolved so rapidly that the services and products are being traded almost instantly. Nevertheless, a great amount of these services and products are quoted in different currencies; thus, it is necessary the use of financial products to trade these products and services without incurring in unnecessary risks. The use of financial derivatives in the financial market has been increasing over the last decade. Moreover, foreign exchange derivatives have become an essential tool for companies to hedge their exposure in a foreign exchange currency. Nonetheless, there has not been enough research about methodologies that emphasize in the mixing of strategies. In this document, we develop a methodology to hedge effectively. Hence, we propose the Limited-memory bfgs in order to find the optimal percentage of the position of the derivative based on simulations created by a garch model. In this paper, we show an example with an exporting Colombian company based on Colombia, which has an exposure in us American Dollars. In this example, we find that the methodology proposed has a lower Value at Risk than a strategy using derivatives operating in isolation.

An Empirical Analysis of Foreign Exchange Exposure of CNX 100 Companies

GIS Business ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. 1-9 ◽  
Author(s):  
Sriram Mahadevan
Keyword(s):  
Exchange Rate ◽  
Stock Returns ◽  
Foreign Exchange ◽  
Foreign Exchange Rate ◽  
Future Research ◽  
Export Ratio ◽  
Exchange Rate Exposure ◽  
Ratio Size ◽  
Foreign Exchange Exposure ◽  
Exchange Rate Changes

The present study has empirically examined the level of foreign exchange exposure and its determinants of CNX 100 companies. For the purpose of study, the relationship between exchange rate changes and stock returns for a sample of 82 companies was determined for the period April 2011-March 2016. The study finds that 49% of the sample companies had significant positive foreign exchange rate exposure and the found that the companies could be exporters or net importers. To explore factors determining foreign exchange rate exposure, variables such as export ratio, import ratio, size of a company, hedging activities were regressed against the exchange exposure and the study found that none of the factors was influencing the exchange rate exposure. The study concludes that the reasons for insignificant influence of the variables could be the natural hedging practices of companies, offsetting of exports and imports and heterogeneous of the sample size. The study offers few directions for future research in this area.

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