Price Cycles Research Articles

Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models

A time series can be thought of as a numerical organism with a continuous nature from a chronological point of view and something that is permanently updated. Up to this moment time series research related with their features, traits, and characteristics, is mainly focused on data mining, in order to discover hidden information or specific knowledge within the time series or their transformations. However, time series representation is crucial, as they are difficult to handle in their original structure due to their high dimensionality.In this paper, the “theory of transgenic time series” is developed, and applied to the forecasting of several rare earth oxide prices: dysprosium, europium, terbium, neodymium, and praseodymium oxides. This theory addresses, specifically, the existence of metal price cycles and the presence of anomalous phenomena that the theory allows to eliminate from the time series, improving the accuracy of the forecast.After representing the time series in a way that allows their genome to be sequenced, a restriction enzyme is defined in order to create a genetically modified time series. There was no need to develop DNA ligases as time series can be cut and pasted without further considerations.Results clearly state that transgenic time series lead to more accurate short term forecasts in cases where a consistent time series genome can be represented. Further research should address the feasibility of developing more accurate long term forecasts by adding new gene sequences based on the time series genome, in order to achieve greater confidence from investors and professional advisers in the feasibility studies developed for future mining investment projects.Finally, it has to be remarked that this theory has nothing to do with “genetic algorithms”, a metaheuristic that was inspired by the natural selection process and not by the sequencing and manipulation of the genome.

Housing prices and population dynamics in urban China

AbstractIn this study, a dynamic stochastic general equilibrium (DSGE) model is used to investigate the influence of population on housing price dynamics in urban China. The contribution of population growth to the housing price trend and the contribution of population shocks to housing price fluctuations are quantitatively measured. The empirical results indicate that the ongoing policy to control the population size in large cities in China can decrease the growth rate of housing prices by 0.49% every year, which accounts for 1/10 of the total housing price trend. Furthermore, this policy cannot stabilize housing prices because population shocks have negligible effects on the housing price cycle. Housing technology shocks and housing preference shocks are responsible for most of the housing price fluctuations.

Calendar synchronization of gasoline price increases

AbstractIn many retail gasoline markets with Edgeworth price cycles, large and regular price increases occur on the same day of the week every week, that is, they are calendar synchronized. In this article, I test whether calendar synchronization leads to higher or lower consumer expenditures on gasoline compared to a world with cycles but without calendar synchronization. On one hand, firms may attempt to trigger price increases just prior to periods of normally high demand. On the other, consumers may be better able to predict and shift purchases to low price days of the cycle. Using high‐frequency gasoline volume data and matching it to high‐frequency price data, I find that the latter effect dominates. All else equal, consumer expenditures on gasoline fall with calendar synchronization in the study markets. I also calculate intertemporal price elasticities and find them to be high.

Navigating the US truckload capacity cycle: Where are freight rates headed and why?

The US truckload transportation market, like many markets, can be characterised as an ongoing rebalancing between supply and demand — or in this case, the number of trucks (and drivers) to haul goods and the demand for those trucks to meet current market demand. This creates a cycle that swings from relative capacity shortage, where there is more demand relative to supply which drives market rates higher, to relative capacity surplus, where there is more supply available relative to demand which drives rates lower. The term ‘relative’ is an important one as the simultaneous rate of change on both sides of the marketplace ultimately drives market rate activity. But as this article will go on to describe, the simultaneous rates of change in supply and demand are rarely in a state of relative equilibrium. It is this dynamic that creates a recurring pricing cycle observed to display at least some level of consistency and can therefore be used to predict the future — but only if you understand the past and the present. In other words, the US truckload market operates as a machine with certain mechanical properties that tend to produce similar outputs given similar inputs. The real world is governed by economic and geopolitical uncertainty, shifting technology and regulatory landscapes, and human psychology that often drives irrational behaviour, so those mechanical properties and input signals can be exceedingly difficult to separate from the noise. However, with enough historical transaction data, market visibility, and insight, this paper proposes that it can be done; the past can be used to predict the future with at least some level of directional certainty, as long as the fundamental structure of the marketplace remains intact.

A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles

This study provides fresh implications for the puzzle of the recent housing boom-bust cycle in the United States. It extracts housing factors from housing price and volume time series at state and regional levels under a dynamic factor model, which considers three varieties of structural instability in local housing markets. The findings suggest that state-level housing price cycles are more unstable than housing volume cycles, and the probability of rejecting stability for the Northeast is the highest among four regional housing markets. In general, the housing market forecasts based on 1988–2012 full-sample factors and time-varying coefficients across pre- and post-1999 subperiods are superior to alternatives. The factor-based forecast results provide new evidence for a nationwide housing crisis in 2007–2008, and thus suggest possible effectiveness of monetary policies in stabilizing recent housing boom-bust cycles.

Analysis of expert and official oil price forecasts

The article studies the factors affecting formation of oil price forecasts by leading expert and official organizations (International Energy Agency, US Energy Information Administration, World Bank, OPEC, RF Ministry of Economic Development). It is demonstrated that all these forecasts take into account both oil market fundamentals and the current conjuncture, but the significance of these factors differs by agency. Non-linear dependence between forecast accuracy and horizon length is identified. The error is greatest for the projections 6 to 8 years ahead, which may be explained by the mismatch between the linear nature of the forecasts and the actual cyclical oil price dynamics during the last 50 years. Accuracy of short- to medium-term projections by the Ministry of Economic Development is shown to hold a median position among forecasting agencies, with the leading position held by the US Energy Information Administration.

Resource rents and populism in resource-dependent economies

A common explanation for populism is the existence of autocratic institutions, with an autocratic regime dispelling opposition by distributing income to the ‘masses’ in the manner of the ‘bread and circuses’ of Imperial Rome. In Bolivia, Ecuador, and Venezuela, populist redistribution occurred however in conjunction with weakening of democratic institutions. We associate populist redistribution with resource rents available in the course of the commodity price cycle. When domestic production is predominantly natural resources, other industry interests are ineffective in opposing populist redistribution and preventing the undermining of democracy. Rather than associating populism with preexisting autocracy as others have done, we show empirically that, in the cases we study, resource rents facilitated populism that allowed regime change whereby authoritarian institutions were created.

FUNDAMENTALS OF INTEGRATED COMMERCIAL BANK IN MACROECONOMIC AND SHARIA PERSPECTIVE IN INDONESIA

This research analyses the fundamentals of integrated commercial bank in macroeconomic and sharia perspective in Indonesia. Based on the calculation of Vector Autoregression (VAR), the impact of macroeconomic variables (Jakarta Stock Islamic Index / JKSII, Indonesian Stock Price Composite Index / JKSE, Crude Oil Price, and Exchange Rate) on stock prices of commercial banks vary. These shocks indicate an indirect price transmission through exchange rate channels and economic growth. From the Structrural Time Series Model (STSM), JKSII, JKSE, and commercial bank share price prediction will generally increase at the end of 2017 and 2018. This will generate hope and benefit for policy maker and business actors in the banking, finance and sharia sectors. In general, the ARMA-ARCH/GARCH model with dummy variables found negative impact of “Fasting Period and Eid Al-Fitr” on return of JKSII, JKSE, and commercial bank stock price. This indicates a cycle of stock price decline that occurs when consumers spend more money to purchase goods and services. However, this cycle of stock price declines is only temporary because the recovery of the world economy and the increase in demand for goods and services in the future can be a pull factor for stock prices (demand factor). Policy makers and stakeholders related to the financial system, banking and capital markets, especially the sharia sector need to see the movement of conventional bank stocks and “Fasting Period and Eid Al-Fitr” as they move in the opposite direction for a certain period. Keywords: Stock Price of Commercial Bank, Macroeconomic and Sharia Perspective, Vector Autoregression (VAR), Structural Time-Series Models (STSM), ARMA-ARCH/GARCH JEL Classification Codes: F31, F47, G15, G21

Online Learning and Optimization of (Some) Cyclic Pricing Policies for Revenue Management with Patient Customers

We consider the joint learning and optimization problem of cyclic pricing policies in the presence of patient customers. In our problem, some customers can be patient, so that they are willing to wait in the system for several periods to make a purchase until the price is lower than or equal to their valuation. We assume that customers are heterogeneous in both their valuation and patience level. But, the decision maker (i.e., the seller) does not know the joint distribution of customers’ valuation and patience level a priori, and can only learn from the realized total sales in every period, which is subject to noise. He also cannot distinguish between different customer types and cannot observe the number of patient customers waiting in the system. In this paper, we first introduce a learning algorithm that can converge to an optimal decreasing cyclic policy with a logarithmic regret, by only using the total sales information. Then, we introduce a larger family of policies, called threshold-regulated policies, which contains both the decreasing cyclic policies and the nested decreasing cyclic policies. For this broader set of policies, we introduce our second learning algorithm that can converge to an optimal threshold-regulated policies at a near-optimal rate. We also conduct extensive numerical studies to show that both learning algorithms converge very efficiently and the gap between the expected average revenue of an optimal threshold-regulated policy and an optimal policy is usually negligible.

Mortgage Spreads, House Prices, and Business Cycles in Emerging Countries

We investigate an unexplored link between the US mortgage spread and business cycle and house price fluctuations in emerging market economies (EMEs). An increase in the US mortgage spread leads to substantially lower output, investment, consumption, house and stock prices, and to an improvement in the trade balance-to-output ratio in EMEs. We find that the financial channel is the main transmission mechanism through which US mortgage spread shocks affect business and house price cycles in EMEs. The US mortgage spread remains a key driver of aggregate economic activity in EMEs when extending the baseline model with alternative domestic and foreign variables, such as global financial risk, and considering alternative country subgroups.

Synchronisation of Price Cycles in European Housing Markets

The housing market plays a crucial role in every economy. Its importance is determined not only by the size of the housing sector but also by the number of socio‑economic functions of residential properties. Just as any other market or economy in general, the housing sector develops cyclically. Housing cycles were traditionally considered to be driven by domestic factors. However Economic integration among the countries of the European Union has fostered a general tendency towards synchronisation of national housing cycles. The aim of the research was to identify the properties of housing cycles in selected European countries as well as to verify the degree of their synchronisation. The study covered residential markets in Belgium, France, Holland, Germany, Great Britain, and Italy in the years 1971–2014. The Christiano‑Fitzgerald filter was used to isolate price cycles, while correlation coefficients and the concordance index were used to assess synchronisation. The obtained results confirmed the occurrence of the phenomenon of synchronisation of housing cycles. Its intensity increased at the end of the 1990s, and one of the main causes for this increase was the introduction of a single European currency and the accompanying convergence of financial markets.

Constructing an index to examine house price misalignment with fundamentals in Malta

PurposeThe purpose of this paper is to compute an aggregate misalignment index using a multiple indicator approach to identify under- or over-valuation of house prices in Malta based on fundamentals.Design/methodology/approachA total of six indicators are used that capture households, investors and system-wide factors: the house price-to-Retail Price Index ratio, the price-to-hypothetical borrowing volume ratio, price-to-construction costs ratio, price-to-rent ratio, dwelling investment-to-GDP ratio and the loan bearing capacity. The weights are derived using principal component analysis. The analysis is performed using both the house price indices of the National Statistics Office (NSO) and the Central Bank of Malta (CBM), which are based on contract and advertised prices, respectively.FindingsHouse prices in Malta were overvalued by around 20 to 25 per cent in the pre-crisis boom. This disequilibrium started to be corrected following the decline in house prices, with the CBM and NSO house price cycles reaching a trough in 2013 and 2014, respectively. At the trough, house prices were undervalued by around 10 to 15 per cent. Since then, house prices started to recover although the recovery in advertised prices was more pronounced compared to that based on contract prices. In mid-2017, advertised house prices were slightly overvalued, while contract prices still have to reach their equilibrium level. The dynamics from the misalignment index, including its peaks and troughs, are remarkably similar to the range derived from statistical filters.Practical implicationsEstimates of house price misalignment have both economic and financial stability implications.Originality/valueThis paper allows for a decomposition of the house price cycle, tailored for the particular characteristics of the Maltese housing market. It also takes into account the relationship between house prices and private sector rents, which in recent years have been buoyed, among other factors, by the high inflow of foreign workers and changing patterns in the tourism industry.

Real and Financial Cycles in EU Countries - Stylised Facts and Modelling Implications

This paper studies the cyclical properties of real GDP, house prices, credit, and nominal liquid financial assets in 17 EU countries, by applying several methods to extract cycles. The estimates confirm earlier findings of large medium-term cycles in credit volumes and house prices. GDP appears to be subject to fluctuations at both business-cycle and medium-term frequencies, and GDP fluctuations at medium-term frequencies are strongly correlated with cycles in credit and house prices. Cycles in equity prices and long-term interest rates are considerably shorter than those in credit and house prices and have little in common with the latter. Credit and house price cycles are weakly synchronous across countries and their volatilities vary widely – these differences may be related to the structural properties of housing and mortgage markets. Finally, DSGE models can replicate the volatility of cycles in house and equity prices, but not the persistence of house price cycles. JEL Classification: C32, E32, E44

Real and Financial Cycles in EU Countries: Stylised Facts and Modelling Implications

This paper studies the cyclical properties of real GDP, house prices, credit, and nominal liquid financial assets in 17 EU countries, by applying several methods to extract cycles. The estimates confirm earlier findings of large medium-term cycles in credit volumes and house prices. GDP appears to be subject to fluctuations at both business-cycle and medium-term frequencies, and GDP fluctuations at medium-term frequencies are strongly correlated with cycles in credit and house prices. Cycles in equity prices and long-term interest rates are considerably shorter than those in credit and house prices and have little in common with the latter. Credit and house price cycles are weakly synchronous across countries and their volatilities vary widely – these differences may be related to the structural properties of housing and mortgage markets. Finally, DSGE models can replicate the volatility of cycles in house and equity prices, but not the persistence of house price cycles.

Housing Price, Credit, and Output Cycles: How Domestic and External Shocks Impact Lithuania's Credit

Housing Price, Credit, and Output Cycles: How Domestic and External Shocks Impact Lithuania's Credit

Housing Price, Credit, and Output Cycles: How Domestic and External Shocks Impact Lithuania's Credit

Lithuania's current credit cycle highlights the strong link between housing prices and credit. We explore this relationship in more detail by analyzing the main features of credit, housing price, and output cycles in Baltic and Nordic countries during1995-2017. We find a high degree of synchronization between Lithuania's credit and housing price cycles. Panel regressions show a strong correlation between a credit upturn and housing price upturn. Moreover, panel VAR suggests that shocks in housing prices, credit, and output within and outside Lithuania strongly impact Lithuania's credit.

Analysis of the Energy Efficiency of Diesel Oil Consumption in the Brazilian Iron Ore Mining Company

This paper aims to evaluate the diesel oil consumption between 2008 and 2015 in the production of iron ore in Brazil, creating correlations between energy intensity (production), economy and checking the impact of fuel prices on the commodity. During the analyzed period, the years 2008-2009 indicated economic crises, which interfered in the price and the commercialization of iron ore products. The physical intensity was 0.2% higher than the economic intensity. In the period from 2010 to 2014, economic activity remained more stable, with a decreasing trend, mainly due to the increase of iron ore prices. The physical intensity is much higher than the economic intensity influenced by the expansion of the Chinese economy. The year of 2014 indicated the end of the high iron ore price cycle and the beginning of supply and demand stabilization with consequent reduction in prices. In 2015, the market entered the stabilization phase, with a continuous reduction in unit production costs and transportation logistics. There was an abrupt change due to the strong increase of the economic intensity due to the fall of the international prices of iron ore. The diesel oil consumption plays a vital role in the scenario of cost reduction in iron ore production and a deeper analysis must be done in order to discover some options to change the energy matrix.

A Study on the Law of Cyclical Fluctuation of Carbon Price—Empirical Evidence from EU ETS

Based on the trading data of the Bluenext and the European Climate Exchange (ECX), this paper analyzes the cyclic price fluctuations of the EU carbon emission rights by means of the maximum entropy spectrum and wavelet variance. The results show that: 1) there are obvious cyclical price fluctuations in the EU carbon trading market, with the longest cycle being 33 months and the shortest 5.7; 2) researches on the factors that affect the cyclical price fluctuations of carbon emission rights manifest that power prices (POWER) exert the greatest implication on the prices of carbon emission rights, followed by coal prices (COAL). For every 1% change in POWER, the price of carbon emission rights changes 10.95% towards the same direction. For every 1% change in COAL, the price of carbon emissions changes 9.28% towards the opposite; 3) research based on variance decomposition demonstrates that electricity prices contribute the most to the changes of the price of carbon emissions, and the variance contribution rate is 13% at a lag cycle of 30 days.

Models of Investor Forecasting Behavior — Experimental Evidence

Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions.

Loan-to-Value Ratio and Housing Price Cycle: Empirical Evidence From Indonesia

The subprime mortgage crisis in 2007-2009 which led to a global recession has highlighted the importance of regulating credit for housing market. The urgency arises not only to manage non-performing ratio, but further to manage price in the housing market which is a potent source of financial imbalance. Loan-to-value (LTV) regulation is imposed in order to dampen the housing price cycle, preventing the occurrence of bubble issue. This study tries to capture the influence of LTV implementation on housing price and assesses its effectiveness in the national scope. Error correction model is used to portray the short and long-term dynamics of housing cycle with regard to policy, macroeconomic, and financial variables. We concluded that LTV is an effective policy to dampen the price cycle in the long run, but not in the short run. In the short run, housing price is closely determined by the macroeconomic factors. Furthermore, we found that the implementation of LTV has made housing price to become more persistent, suggesting a change in the market expectation structure and the behavior of housing price cycle.