Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques

Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange (FX) market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory (LSTM) and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index (FXVIX) as a measure of FX volatility. In particular, the three major FXVIX indices (EUVIX, BPVIX, and JYVIX) from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting.

Analysis of the Influence of CPEC on Monetary Policy Decision in Pakistan

The purpose of this research is to extent to which the China- Pakistan Economic Corridor (CPEC) can impact monetary policy decisions in Pakistan. This is in response to further research in previous studies. A robust econometric model was applied to economic data procured from the financial and statistical institutions in Pakistan and China. The analysis suggests the potential of the economic cooperation to inure to the benefit of both Pakistan and China in terms of economic development. Return on the analysis indicated that financial sector in Pakistan in particular is more susceptible to risk based on the influx of Chinese funds into the system. Specifically it is concluded the inflation and foreign exchange volatility are the most vulnerable areas of concern. Based on this knowledge it is proposed that the financial regulatory authorities in Pakistan must develop appropriate response strategies to safeguard the stability of the entire financial system on a constant basis.

An Open Innovation Intraday Implied Volatility for Pricing Australian Dollar Options

This study introduces the intraday implied volatility (IV) for pricing the Australian dollar (AUD) options. The IV is estimated using the at-the-money one-month, two-month, and three-month maturity AUD options traded in the opening, midday, and closing period of a trading day. The Mincer-Zarnowitz regression test evaluates the predictive power of IV to forecast the foreign exchange volatility for the within-week, one-week, and one-month horizon. The mean absolute error, mean squared error, and root mean squared error measures are employed to assess the performance of IV in estimating the price of currency options for the within-week, one-week, and one-month horizon. This study reveals four critical findings. First, a three-month maturity IV does not contain vital information for pricing options. Second, IV incorporated information is not relevant to compute the value of options for a horizon of less than a week. Third, IV in the closing period of Monday or Tuesday subsumes most of the essential information to estimate options price. Fourth, the shorter (longer) maturity IV provides critical information to price options for the shorter (longer) horizon. The intraday IV is a new dimension of unobservable volatility in accurately pricing currency options for researchers and practitioners.

Comparison of the cost of illness of primary liver cancer between Japan and Taiwan

Background Primary liver cancer (PLC) is the fifth and second leading cause of death in Japan and Taiwan, respectively. The aim of this study was to compare the economic burden of PLC between the two countries using the cost of illness (COI) method and identify the key factors causing the different trends in the economic burdens of PLC. Materials and methods We calculated the COI every 3 years using governmental statistics of both countries (1996–2014 data for Japan and 2002–2014 data for Taiwan). The COI was calculated by summing the direct costs, morbidity costs, and mortality costs. We compared the COIs of PLC in both countries at the USD-based cost. The average exchange rate during the targeted years was used to remove the impact of foreign exchange volatility. Results From 1996 to 2014, the COI exhibited downward and upward trends in Japan and Taiwan, respectively. In Japan, the COI in 2014 was 0.70 times the value in 1996, and in Taiwan, the COI in 2014 was 1.16 times greater than that in 1996. The mortality cost was the greatest contributor in both countries and had the largest contribution ratio to the COI increase in Japan. However, the direct cost in Taiwan had the largest contribution ratio to the COI decrease. Conclusions To date, the COI of PLC in Japan has continuously decreased, whereas that in Taiwan has increased. Previous health policies and technological developments are thought to have accelerated the COI decrease in Japan and are expected to change the trend of COI of PLC, even in Taiwan.