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This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.

This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).

In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).

In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.

It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.

This study aims to measure the degree of volatility comovement between stock market liquidity and informational efficiency across Asia, Europe, North-South America, Africa, and the Pacific Ocean over three decades. In particular, the authors analyze the extent of the time-varying nexus between different aspects of stock market liquidity and multifractal scaling properties of the stock return series across various regions and diversified market conditions. This study further investigates several factors altering the degree of dynamic conditional correlations (DCCs) between the efficiency and liquidity of the domestic stock markets.

The study measures five aspects of stock market liquidity – tightness, depth, breadth, immediacy, and adjusted immediacy. The authors evaluate the multifractal scaling properties of the stock return series to measure the level of stock market efficiency across the regions and diversified market conditions. The study uses the dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity framework to quantify the degree of volatility comovement between liquidity and efficiency over the period.

The study finds the presence of stronger volatility comovement between inefficiency and illiquidity due to the price impact characteristics of the stock markets irrespective of different regions and diversified market conditions. The extent of time-variation increased following the shock periods, indicating the significant role of the financial crisis in increasing the volatility comovement between inefficiency and illiquidity. The highest degree of time-varying correlation is observed in the developed stock markets of Northwestern and Northern Europe compared to the regional and emerging counterparts. On the other hand, weak DCCs are observed in the emerging stock markets of Europe.

The output of the present study assists investors in identifying diversification opportunities across the regions. Additionally, the study has significant implications for market regulators, aiding in predicting future troughs and peaks. The prediction, in turn, helps formulate capital market development plans during dynamic economic situations.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

This study examines the determinants of idiosyncratic risk (IR) or unsystematic risk. The study also examines the determinants of IR by dividing the firms into different categories: beta-based firms, liquid and illiquid firms and financially constrained (FC) and unconstrained (FUC) firms.

The fixed effects static panel data model specifications are formulated based on Hausman (1978) test for BRICS (Brazil, Russia, India, China, and South Africa) member countries over the period 2000–2019. Moreover, the t-test is applied to see whether the returns of different types of portfolios are significantly different.

The portfolio analysis results show that, on average, high IR firms tend to be small in size, highly leveraged, have low competitiveness, low profitability, less dividend yield and low returns for all the sampled countries. The sample paired t-test also confirms that a significant difference exists between extreme portfolios: small and large size and low IR and high IR portfolios. The panel regression results show that firm size, market power, price-to-earnings ratio, return on equity (ROE) and dividend yield negatively relates to IR. Yet, both leverage and liquidity are positively related to IR. However, the sign of momentum returns is mostly positive for the entire sample. The coefficient values for high-beta, FC and illiquid firms are more significant and large than the firms' counterparts for all BRICS member countries. These results support the hypothesis of an under-diversified portfolio and suggest that the above-mentioned firm-specific variables are the significant determinants of unsystematic risk.

The securities exchange commission, as the supervisor of the public limited companies, needs to increase its role in investor protection related to the uncertainty of investment in the capital market. Accordingly, in making investment decisions in a stock exchange, investors can use the information that captures unsystematic risk for investment decision-making.

This study is the first to explore the determinants of IR in top emerging countries. Second, none of the existing studies has focused on the determinants of the IR based on different categories of firms.

By examining the impact of the day of the week during the COVID-19 pandemic and the subsequent economic recession, it is possible to provide insights into market behaviour during volatile times that can be furnished to investors and policymakers for informed decisions.

This study investigates the day-of-the-week effect on the Colombo Stock Exchange (CSE), with particular emphasis on the variations in this effect during the COVID-19 pandemic and the subsequent economic crisis.

The study applies the Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model, allowing for the evaluation of asymmetric responses to positive and negative shocks. The data span from January 2006 to December 2022 and are segmented into different periods: the entire sample, war and post-war periods, the COVID-19 pandemic and the economic crisis period, each reflecting distinct market conditions.

The study uncovers a significant day-of-the-week effect on the CSE. Mondays and Tuesdays typically show a negative effect, while Thursdays and Fridays display a positive impact. However, this pattern shifts notably during the COVID-19 pandemic, with all weekdays exhibiting significant positive impact, and varies further across different waves of the pandemic. The economic crisis period also shows unique weekday effects, particularly before and after an important political event.

The volatility of exchange rate has generally been sighted as a primary cause for various shocks and instability in international trade of Ghana as witnessed over the years and most especially in recent times. Hence, owing to the increasing trade levels between Ghana and Ghana's global trading partners, the study aims to investigate if the trade–exchange rate volatility nexus in Ghana supports the positive, negative or ambiguous hypotheses?

The study investigates the effects of Ghana's exchange rate volatility on international trade by designing import and export equations to estimate both short- and long-run specifications of the effect and employing the multivariate generalized autoregressive conditional heteroskedasticity (GARCH) with Baba, Engle, Kraft and Kroner (BEKK) specification developed by Engle and Kroner (1995) as a further check for the robustness of the findings. Monthly data between 1993 and 2017 on the real effective exchange rates of Ghana's trade with 143 trading partners were taken as the series for modeling the volatility using GARCH andexponential generalized autoregressive conditional heteroskedastic (EGARCH) models.

The empirical results show that the volatility of exchange rate negatively impact export performances in the Ghanian economy. On the other hand, there was no sufficient evidence to support the observed positive effect of exchange rate volatility on imports, as the effects were only significant at 10% level in the long run. Thus, it is concluded that the finding cannot confirm a relationship between volatility and import. Thus, the results present differences in the direction of the effect of exchange rate volatility on imports and exports in the context of the Ghanaian economy.

Considering the fragility of the Ghanaian economy and Ghana's macro-economic indicators, the study points at the crucial need for more integration of well-informed trade policies within the country's macro-economic policy framework to contain the impacts of exchange rate volatility on trade performances.

The study contributes to literature by scope and method. More specifically, empirical studies have failed or provided little evidence uniquely on the Ghanaian economy's reaction to exchange rate volatility on the country's imports and exports. Additionally, most of the existing empirical studies measure exchange rate volatility using the standard deviation of the moving averages of the logarithmic transformation of exchange rates. This method is criticized because the method is unsuccessful in capturing the effects of potential booms and bursts of the exchange rate. The authors' study circumvents for these highlighted pitfalls.

The study contributes to literature by scope and method. More specifically, empirical studies have failed or provided little evidence uniquely on the Ghanaian economy's reaction to exchange rate volatility on the country's imports and exports. Thus, the study chat a course for socio-economic dynamic of Ghanaian economy.

The study contributes to literature by its scope and method, as extant empirical studies have provided little evidence specifically on the Ghanaian economy's reaction to exchange rate volatility. Additionally, most of the existing empirical studies measure exchange rate volatility using the standard deviation of the moving averages of the logarithmic transformation of exchange rates. This method is criticized because of the method's inadequacies in capturing the effects of potential booms and bursts of the exchange rate. The study thereby essentially circumvents for these highlighted pitfalls.

Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility.

Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data.

The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered.

To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.