Psychological Barrier in Foreign Exchange Rate and Implied Volatility in Currency Exchange Option

2014 ◽  
Vol 22 (2) ◽  
pp. 309-329
Author(s):  
Dong-Hoon Shin ◽  
Seonhyeon Kim ◽  
Hojoon Kim ◽  
Daehwi Jung
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Implied Volatility ◽  
Foreign Exchange Rate ◽  
Momentum Effect ◽  
Psychological Barriers ◽  
Currency Exchange ◽  
Psychological Barrier ◽  
Mean And Variance ◽  
The Mean

In this paper, we examine the existence of the psychological barriers in three foreign exchange rate, won/dollar, euro/dollar, yen/dollar, and test that the psychological barriers effect to the implied volatilities of the FX options. For each exchange rate, the existence and spots of the psychological barriers are estimated from roughly 10 years data for each currency rate, and GARCH (1, 1) model was applied to observe the momentum effect about the mean and variance of the conditional returns, and the implied volatility of the FX-options for each currency rate near the psychological barriers. Since this effect is more clearly observed on the implied volatility data, this fact supports that psychological barriers affects to the price of the FX-options.

scholarly journals An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

2019 ◽  
Vol 9 (15) ◽  
pp. 2980 ◽  
Author(s):  
Muhammad Yasir ◽  
Mehr Yahya Durrani ◽  
Sitara Afzal ◽  
Muazzam Maqsood ◽  
Farhan Aadil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Exchange Rate Forecasting ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
The Exchange Rate

Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

scholarly journals Forecasting INR Exchange Rate Against USD, GBP, JPY, SGD, EUR, AED Using Machine Learning

2021 ◽  
pp. 684-689
Author(s):  
Sumith Pevekar
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Random Forest ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Random Forest Algorithm ◽  
Currency Exchange ◽  
Predicting Performance ◽  
Machine Learning Model

The price of a native currency expressed in terms of another currency is known as a foreign exchange rate. In other terms, a foreign exchange rate compares the value of one currency to that of another. The value of standardized currencies varies with demand, supply, and consumer confidence around the world due to which their values fluctuate over time. To forecast the exchange rate of INR, I have developed a machine learning model. The model was trained to estimate six foreign currency exchange rates against the Indian Rupee using historical data. This model uses Random Forest algorithm to train and predict the values. The suggested system’s predicting performance is assessed and contrasted using statistical metrics. According to the findings, the Random Forest algorithm-based model predicts well and achieves an accuracy of 93.61%. KEYWORDS: Regression, Random Forest, Exchange Rate, INR

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.

Interest Rate, Foreign Exchange Rate, and Stock Market Development in Nigeria

2018 ◽  
Vol 9 (3) ◽  
pp. 247-253 ◽  
Author(s):  
Edward Adedoyin Adebowale ◽  
Akindele Iyiola Akosile
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Market ◽  
Foreign Exchange ◽  
The United States ◽  
Ordinary Least Squares ◽  
Foreign Exchange Rate ◽  
Market Development ◽  
National Bureau ◽  
Stock Market Development

This research investigated the effect of interest rate and foreign exchange rate on stock market development in Nigeria. This research was centered on two research problems. First, it was whether interest rate had a significant effect on stock market development in Nigeria. Second, it was whether foreign exchange rate had a significant impact on stock market development in Nigeria. The scope of the research covered the period from 1981 to 2017. Data for this period were chosen because it covered pre and post-liberalization periods of Nigerian financial system. This research made use of ex post facto research design. Secondary data were sourced from Nigerian Stock Exchange reports, Central Bank of Nigeria statistical bulletins, and National Bureau of Statistics publications. Data were collected on Stock Market Capitalization (SMC), Prime Lending Rate (PLR) and Real Exchange Rate (RER) (Nigerian Naira in relation to American Dollars of the United States). Data analysis was carried out with Ordinary Least Squares (OLS) and Cochrane-Orcutt Iterative techniques. The findings reveal that interest rate has a significant negative effect, and foreign exchange rate has a significant positive effect on Nigerian stock market development during the period covered. It is suggested that monetary authorities should strive to formulate policies that will make interest and foreign exchange rates stable, competitive, and at a level that will stimulate the investment of funds in the stock market.

Estimating daily seasonality in foreign exchange rate changes

1994 ◽  
Vol 13 (6) ◽  
pp. 519-528 ◽  
Author(s):  
Laurence S. Copeland ◽  
Peijie Wang
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Exchange Rate ◽  
Exchange Rate Changes
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