JUSTIFICATION OF THE ESSENCE, FUNCTIONS AND PURPOSE OF GRAIN FUTURE EXCHANGE MARKET OF AGRICULTURAL PRODUCTS

In the article analyzes the development of the domestic stock market. It is substantiated that crop futures are a derivative financial instrument on the stock exchange, which provides for the obligation of its seller or buyer to periodically transfer sums of money to the opposite party depending on changes in the market price of grain, and (or) the obligation delivery of grain on time. It is determined that only under the conditions of joint efforts on the part of the state, the exchange community, participants of the agrarian market that will allow bringing the exchange commodity market closer to civilized bases.

Forecast Foreign Exchange Rate: The Case Study of PKR/USD

The main aim of this paper is to forecast the future values of the exchange rate of the USD. Dollar (USD) and Pakistani Rupee (PR). For this purpose was used the ARIMA model to forecast the future exchange rates, because the time series was stationary at first difference. Data reported to five years ranging from the first day of April 2014 to 31st March 2019. The results proved that ARIMA (1,1,9) is the most suitable model to forecast the exchange rate. The difference between the forecasted values and actual values are less than 1%; therefore, it was found that the ARIMA is robust and this model will be helpful for the government functionaries, monetary policymakers, economists and other stakeholders to identify and forecast the future trend of the exchange rate and make their policies accordingly.

Long-Term Exchange Rate Probability Density Forecasting Using Gaussian Kernel and Quantile Random Forest

In the midst of macro-economic uncertainties, accurate long-term exchange rate forecasting is crucial for decision-making and planning. To measure the uncertainty associated with exchange rate and obtaining additional information of future exchange rate, a hybrid model based on quantile regression forest and Gaussian kernel (GQRF) is constructed. Quarterly dataset of KSh/USD exchange rate and macro-economic variables from 2007 to 2016 are used. The forecast horizon spans from 2013 to 2016. With a prediction interval coverage probability and prediction interval average width of 95% and 29.6493%, the constructed model has a very high coverage probability. The method of determining the probabilistic forecasts is very significant to achieve forecasts with correct coverage. The probability density forecasting model for the exchange rate gave significant information–the probability distribution of the forecasted results. In this way, uncertainties around the forecast can be evaluated because the complete exchange rate distribution are forecasted. GQRF is efficient as it can uphold the uncertainty about the variance linked to each point, which is important for exchange rate forecasting. Using the constructed model, the probabilities of exceedance such as the probability of future exchange rate exceeding the average exchange rate for the year can be computed. This paper also adds to the scarce literature of exchange rate probability density forecasting using machine learning techniques.

Short-term foreign exchange forecasting: decision making based on expert polls

The paper aims to analyze the decision making based on expert polls for short-term foreign exchange (FX) forecasting from the viewpoint of the economic behavior theory. The paper offers the assessment of the problem of decision making for forecasting and investment into foreign currency. This study analyzes the relative accuracy of expert polls and forecasts, based on historical data, in the prediction of the most liquid currency pairs (EUR/USD, USD/JPY, GBP/USD) as well as USD/RUB currency pair on time horizons 1, 2, 6, and 12 months. Observation period lasted from January 2018 to January 2019. For EUR/USD (56-62 experts), the polls were more accurate than historical simulations. For GBP/USD (28-70 experts), historical simulations were more accurate than polls. For USD/JPY and USD/RUB, historical simulations are better earlier, while polls are slightly better later. The main conclusion is that EUR/USD historical modeling is usually less accurate on the horizon more than half a year as compared with expert polls for making the decisions about the future exchange rate.

Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson–Siegel Class of Models

This paper investigates the predictability of exchange rate changes by extracting the factors from the three-, four-, and five-factor model of the relative Nelson–Siegel class. Our empirical analysis shows that the relative spread factors are important for predicting future exchange rate changes, and our extended model improves the model fitting statistically. The regression model based on the three-factor relative Nelson–Siegel model is the superior model of the extended models for three-month-ahead out-of-sample predictions, and the prediction accuracy is statistically significant from the perspective of the Clark and West statistic. For 6- and 12-month-ahead predictions, although the five-factor model is superior to the other models, the prediction accuracy is not statistically significant.