Search results

This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

This study aims to develop a high-quality impairment loss allowance model in conformity with Indonesian Financial Accounting Standards 71 (PSAK 71) that has significant contribution to national interests and the banking industry.

The determination of the impairment loss allowance model is settled through 7 stages, using integration of some statistical methods such as Markov chain, exponential smoothing, time series analysis of behavioral inherent trends of probability of default, tail conditional expectation and Monte Carlo simulation.

The model which is developed by the authors is proven to be a high-quality and reliable model. By using the model, it can be shown that the implementation of the expected credit losses model on Indonesian Financial Accounting Standards 71 is more prudent than the implementation of the incurred loss model on Indonesian Financial Accounting Standards 55.

Determination of defaults was based on days past due, and the analysis in this study did not touch the aspects of hedge accounting in general.

This developed model will contribute significantly to national interests as a source of reference for other banks operating in Indonesia in calculating impairment loss allowance (CKPN) and can be used by the Financial Services Authority of Indonesia (OJK) as a guideline in assessing the formation of impairment loss allowance for banks operating in Indonesia.

As so far there is not yet an available standardized model for calculating impairment loss allowance on the basis of Indonesian Financial Accounting Standards 71, the model developed by the authors will be a new breakthrough in Indonesia.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

This paper aims to estimate a New Keynesian small open economy dynamic stochastic general equilibrium (DSGE) model for Egypt using Bayesian techniques and data for the period FY2004/2005:Q1-FY2015/2016:Q4 to assess monetary and fiscal policy interactions and their impact on economic stabilization. Outcomes of monetary and fiscal authority commitment to policy instruments, interest rate, government spending and taxes, are evaluated using Taylor-type and optimal simple rules.

The study extends the stylized micro-founded small open economy New Keynesian DSGE model, proposed by Lubik and Schorfheide (2007), by explicitly introducing fiscal policy behavior into the model (Fragetta and Kirsanova, 2010 and Çebi, 2011). The model is calibrated using quarterly data for Egypt on key macroeconomic variables during FY2004/2005:Q1-FY2015/2016:Q4; and Bayesian methods are used in estimation.

The results show that monetary and fiscal policy instruments in Egypt contribute to economic stability through their effects on inflation, output and debt stock. The monetary policy Taylor rule estimates reveal that the Central Bank of Egypt (CBE) attaches significant importance to anti-inflationary policy and (to a lesser extent) to output targeting but responds weakly to nominal exchange rate variations. CBE decisions are significantly influenced by interest rate smoothing. Egyptian fiscal policy has an important role in output and government debt stabilization. Additionally, the fiscal authority chooses pro-cyclical government spending and counter-cyclical tax policies for output stabilization. Again, past values of the fiscal instruments are influential in the evolution of the future fiscal policy-making process.

A few studies have examined the interaction between monetary and fiscal policy in Egypt within a unified framework. The presented paper integrates the monetary and fiscal policy analysis within a unified dynamic general equilibrium open economy rational expectations framework. Without such a framework, it would not be easy to jointly analyze monetary and fiscal transmission mechanisms for output, inflation and debt. Also, it would be neither possible to contrast the outcome of monetary and fiscal authorities commitment to a simple Taylor instrument rule vis-à-vis optimal policy outcomes nor to assess the behavior of monetary and fiscal agents in macroeconomic stability in context of an active/passive policy decisions framework.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles.

First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A^* algorithm and uses the improved A^* algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained.

The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’ autonomous obstacle avoidance decision-making.

This study establishes navigation area boundary for the environment based on the VFA and uses the improved A* algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.

The purpose of this paper is to evaluate the influence of the informational environment on the relevance of accounting information in companies traded in stock exchanges of emerging markets.

For this purpose, the authors calculated indicators based on figures derived from the financial statements and variables that sought to capture the influence of the economic and institutional environment. The sample consisted of publicly traded companies from 20 countries classified as emerging by Standard & Poors. Macroeconomic information was obtained through the International Country Risk Guide database. The analysis period ranged from 2004 to 2013, excluding missing data, variables considered as outliers, besides the exclusion of data from companies that presented negative equity.

It was observed that the financial variables presented signs consistent with the literature, except for the price-to-book variable and the asset change variable. The inclusion of variables related to the accounting informational environment offered evidence that the more opaque the accounting environment in the country, the lesser the ability of the profits to portray the variations of stock returns. The variable that captured the adoption of international standards was consistent with expectations, i.e. the adoption of international standards would increase the quality of accounting information, showing a positive signal. Moreover, the variable aggressiveness of the earnings was statistically significant and negative, consistent with the literature.

The variables earnings smoothing and aversion to losses did not show the expected behaviour though, highlighting the possible limitations of these proxies used to capture the opacity of the earnings.

When institutional moderators were included, it was observed that the adoption of the IFRS standards positively affected the relationship, which is more relevant when the accounting figures were under its aegis. Recently, countless nations’ transition to international accounting standards has been justified by the need to use high-quality reporting standards. The research sought to contribute to strengthen this dimension, presenting evidence that the dummy variable included to capture the adoption of international standards had a positive effect on the relationship.

The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations.

The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation.

Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI.

FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

The purpose of this paper is to analyze the changes in wealth and consumption inequality in Spain and estimate the consumption effects of housing and financial wealth.

The estimations are made using micro-data from the Spanish Survey of Household Finances (2002–2014) applying cross-section, panel and interquartile techniques.

The findings of this paper suggest that there was an increase in wealth inequality during the period under analysis and a reduction in consumption inequality. Also, the authors find a significant positive effect of wealth on consumer expenditure. Disaggregating by asset type, the value of the main residence is the category with the highest estimated effect on consumption, whereas the remaining types of assets, although still positive and generally significant, have more modest effects on consumption. However, the estimated coefficients and their significance can change substantially depending on the phase of the economic cycle and the position of the household in the income distribution.

These results provide new empirical evidence on the effects of household wealth changes on their consumption behavior, the differences depending on the household's position in the distribution and the fluctuations of these estimated coefficients throughout a period of profound economic upheavals.