Forex Strategy Adjustments For Texas Economic Trends

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Forex Strategy Adjustments For Texas Economic Trends

Forex Strategy Adjustments For Texas Economic Trends

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By Manuel Monge Manuel Monge Scilit Google Scholar 1, *, † and Ana Lazcano Ana Lazcano Scilit Google Scholar 1, 2, ‡

Present Address: Department of Financial Economics, Universidad Francisco de Vitoria, Crta. Pozuelo-Majadahonda, Km. 1800, Pozuelo de Alarcón, E-28223 Madrid, Spain.

What Determines Oil Prices?

Present Address: Departamento de Ingeniería de Sistemas y Control, ETSI Informática, UNIT, C/Juan del Rosal, 16, E-28040 Madrid, Spain.

Received: 26 April 2022 / Revised: 30 May 2022 / Accepted: 6 June 2022 / Published: 16 June 2022

Since December 2019 we have been living with the virus called SARS-CoV-2, a situation that has led to the prevalence of health policies over economic ones and has caused paralysis in the demand for raw materials for several months because the number restrictions imposed worldwide. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractional integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic period, we observe that commodity prices have a mean reversion behavior, indicating that additional measures will not be necessary as the series will return, on their own, to their long-term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will regain its upward trend, increasing by about 56.67% to the price before the start of the COVID-19 pandemic episode.

Forex Strategy Adjustments For Texas Economic Trends

The pandemic episode, caused by the SARS-CoV-2 virus, led to the paralysis of demand for raw materials over several months due to the restrictions and locks imposed around the world, leading to a collapse in prices.

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Following the worst of the COVID-19 pandemic, demand for industrial metals, along with demand for other raw materials, is now recovering. Many economic analysts (see Monge and Poza 2021; Monge and Gil-Alana 2021; among others) assume that a new cycle of economic growth will occur or, indeed, is already occurring, supported by various stimuli from governments and the main central. banks.

The new projects that will be implemented, or that will be launched to reform infrastructures (Biden Administration in the United States, Recovery Funds in Europe, among others), will mean an increase in the demand for raw materials, with markets going into a new bullish. cycling.

The beginning of the price increase can already be seen in the various commodity markets. For example, the price of wood used for construction in the United States, such as pine or fir, has had a strong growth in prices due to the high demand for housing and the shortage in the supply of this raw material. The increase in timber futures prices is 63.5% this year and stands at 450%, approximately, compared to the price trough in 2020.

The rapid increase in the prices of raw materials underlines the importance of determining the evolution of prices and analyzing the impact of this increase in economies, as well as the possible indirect effects resulting from it, such as the increase in the prices of basic necessities. transfer to the end user. To this end, a study of the persistence of current price behavior is needed.

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History has proven that market crises caused by the spread of disease often have a short-term effect. Following the detection of SARS on 12 March 2003, the Bloomberg Commodity Index fell by 8% in the following two weeks, and had fallen by 4% by the time SARS was brought under control.

In 2020, the Bloomberg Commodity Index fell 9% in the month after the coronavirus was recognized by the World Health Organization as a pandemic disease, falling to 59.5 points in March 2020, and eventually recovering to 86 points in February 2021, which is a 44.5% increase.

Although last year’s rebound could be seen as a V-shaped recovery period, between January and April 2020, energy prices fell by almost 60%, while metal and food prices fell by 15 and 10%, respectively. Metal prices rebounded in response to supply shocks and a faster-than-expected recovery in China’s industrial activity, and food prices stabilized as concerns over restrictive policy measures faded. Goldman Sachs predicted a new bull market in December 2020 due to the mismatch between supply and an expected increase in demand, caused by the increased maritime movement of medical supplies and the hope that vaccines against COVID-19 would return normality to’ the markets.

Forex Strategy Adjustments For Texas Economic Trends

Due to the wide variety of assets included in the Bloomberg Commodity Index, volatility is very low, so events in specific industries can be prevented from having a significant impact.

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According to (Erten and Ocampo 2013), the supercycles of raw materials are characterized by decade-long periods where commodities are traded above their long-term price trend. The history of these markets shows that these supercycles last 20 to 70 years between peaks (Erdem and Ünalmış 2016). Some banks and market analysts see signs of the start of a new supercycle, caused by the weakening of the dollar and central banks, along with the fiscal stimulus aimed at renewable energy and infrastructure spending.

The main objective of this article is to analyze the impact that the COVID-19 disease has had on commodity prices, recorded in the Eikon Bloomberg Thomson Reuters Commodity index, and to identify the possible emergence of a new commodity cycle that begins after a crisis health 2020. , while studying, for this purpose, the trend and persistence of prices. To this end, different standard unit root tests and fractional integrated methods based on ARFIMA (p, d, q) models were used. In addition, the results were supported by an Artificial Neural Network model using a Multilayer Perceptron (MLP) neural network for time series prediction. We examine the properties of a time series of commodity prices, from 2 January 1991 to 2 April 2021, using monthly data.

One of the interesting results is that when focusing on the COVID-19 pandemic episode, we observe that both time series (Bloomberg Spot Commodity Index and Bloomberg Commodity Index Total Return) have a mean reversion behavior, indicating that it will not. necessary to take additional measures as the series will return, on their own, to their long-term projections, but given the very wide confidence intervals (obviously due to the small sample sizes in some of the periods examined) , we cannot reject Hypothesis I( 1), where the impact of the shock continues indefinitely. Therefore, we observe an increase in the index price, from before the start of the pandemic period of COVID-19, of 52.2%, using the algorithm of the Artificial Neural Network.

The rest of the paper is structured as follows. The next section briefly reviews the literature on commodity prices and their cycles. In the following two sections, the source of the data and the methodology used in the paper are detailed. Section 5 presents the main empirical results, while the last section shows the main conclusions of this work.

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(Marshall 1890) describes the operation of commodity markets, how supply and demand determine the price, with buyers and sellers being the main actors, and how the market demand curve is determined by the consumer demand curves. When the supply of the good is equal to the demand, this is called equal price; however, we are dealing with a competitive market where all economic agents consider the price to be beyond their control.

In recent decades, the evolution of commodity prices has been repeatedly debated. The growth of the world economy has also stimulated the growth of commodity markets, while supply is less

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