Search results

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

The purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.

This article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.

Findings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.

The originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

The purpose of this paper is to first develop indicators for how total inequality, measured through the ANalysis Of GIni (ANOGI) framework, is mapped onto each group – i.e. indicators for each group's share of total inequality. Second, to develop indicators for the sensitivity of total inequality and its structure to changes in the composition of the population. Specifically, to develop indicators for how the Gini index and its ANOGI components react to (1) changes in the population-share of each group, (2) migration between groups, (3) changes in group incomes and (4) income transfers between groups.

First, the expressions for these indicators are derived analytically. Following this, the indicators are applied to labour-market data from Brazil, contrasting the results to others available in the literature.

The indicators described above are presented and their characteristics discussed. Empirically, it is illustrated how labour formalisation in Brazil was an inequality-reducing process between 2002 and 2011, contrary to previous incorrect measurements of the phenomenon based on income-source decompositions for Latin American countries.

Indicators for how total inequality reacts to changes in group sizes and income were unavailable for the ANOGI framework, which this article provides. The empirical illustration shows how this leads to a reassessment of important inequality dynamics, using the example of labour formalisation in Brazil. Contrary to the existing literature, it is shown how this was a progressive development, with key implications for social and labour-market policy. This framework can be used to assess the impact of diverse processes in the ANOGI methodology.

The purpose of this article is to determine necessary and sufficient conditions in order that (D, K) to be an S-accr pair, where D is an integral domain and K is a field which contains D as a subring and S is a multiplicatively closed subset of D.

The methods used are from the topic multiplicative ideal theory from commutative ring theory.

Let S be a strongly multiplicatively closed subset of an integral domain D such that the ring of fractions of D with respect to S is not a field. Then it is shown that (D, K) is an S-accr pair if and only if K is algebraic over D and the integral closure of the ring of fractions of D with respect to S in K is a one-dimensional Prüfer domain. Let D, S, K be as above. If each intermediate domain between D and K satisfies S-strong accr*, then it is shown that K is algebraic over D and the integral closure of the ring of fractions of D with respect to S is a Dedekind domain; the separable degree of K over F is finite and K has finite exponent over F, where F is the quotient field of D.

Motivated by the work of some researchers on S-accr, the concept of S-strong accr* is introduced and we determine some necessary conditions in order that (D, K) to be an S-strong accr* pair. This study helps us to understand the behaviour of the rings between D and K.

Covid-19 pandemic is a unique and extraordinary situation for the globe, which has potentially disrupted almost all aspects of life. In this global crisis, the tourism and hospitality sector has collapsed in almost all parts of the world, and the same is true for India. Therefore, this paper aims to investigate the impact of Covid-19 on the Indian tourism industry.

This study develops an appropriate model to forecast the expected loss of foreign tourist arrivals (FTAs) in India for 10 months. Since the FTAs follow a seasonal trend, seasonal autoregressive integrated moving average (SARIMA) method has been employed to forecast the expected FTAs in India from March 2020 to December 2020. The results of the proposed model are then compared with the ones obtained by Holt-Winter's (H-W) model to check the robustness of the proposed model.

The SARIMA model seeks to manifest the monthly arrival of foreign tourists and also elaborates on the progressing expected loss of foreign tourists arrive for the next three quarters is approximately 2 million, 2.3 million and 3.2 million, respectively. Thus, in the next three quarters, there will be an enormous downfall of FTAs, and there is a need to adopt appropriate measures. The comparison demonstrates that SARIMA is a better model than H-W model.

Several studies have been reported on pandemic-affected tourism sectors using different techniques. The earlier pandemic outbreak was controlled and region-specific, but the Covid-19 eruption is a global threat having potential ramifications and strong spreading power. This work is one of the first attempts to study and analyse the impact of Covid-19 on FTAs in India.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The study examines the impact of real exchange rates and asymmetric real exchange rates on real stock prices in Malaysia, the Philippines, Singapore, Korea, Japan, the United Kingdom (UK), Germany, Hong Kong and Indonesia.

This study uses the asymmetric autoregressive distributed lag (ARDL) approach and non-linear autoregressive distributed lag (NARDL) approach.

The asymmetric ARDL approach shows more economic variables are found to be statistically significant than the ARDL approach. The asymmetric real exchange rate is mostly found to have a significant impact on the real stock price. Moreover, real output and real interest rates are found to have a significant impact on the real stock price. The Asian financial crisis (1997–1998) and the global financial crisis (2008–2009) are found to have a significant impact on the real stock price in some economies.

Economic variables are important in the determination of stock prices.

It is important to examine the impact of asymmetric real exchange rate on the real stock price as the depreciation of real exchange rate could have different impacts than the appreciation of real exchange rate on the real stock price. The previous studies in the literature mostly found the significant impact of nominal exchange rate on the nominal stock price.

This research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).

Spectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.

The results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).