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We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

The paper seeks to explain volatility and risk (VaR) modelling using data from international financial markets, and particularly to evaluate the performance of minimum capital risk requirements (MCRR) estimates in an out‐of‐sample period using the bootstrapping approach.

This paper captures financial time series characteristics by employing the GARCH(p,q) model, and its EGARCH, threshold GARCH (TGARCH), asymmetric component (AGARCH) and component GARCH (CGARCH) extensions. Furthermore, under the bootstrapping approach, the MCRR for long and short positions over five‐day, ten‐day and 15‐day horizon periods is calculated. This paper uses daily data from the USA (Dow Jones, NASDAQ) and European (ASE, Greece; DAX, Germany; FTSE‐100, UK) financial markets.

The results show that higher capital requirements are necessary for a short position since a loss is more likely than for a long position.

Future research should examine the performance of multivariate time series models when using daily and monthly returns of international mature and emerging markets. Consequently, it is of interest to consider multivariate models to describe the volatility and market risk of several time series jointly, to exploit possible linkages that exist.

The findings are strongly recommended to risk managers and modellers dealing with US and European financial markets.

The contribution of this paper is to provide new evidence from international equity markets to the modelling of financial time series by explaining volatility and VaR (MCRR) estimates in the US and European markets. This paper explains the functioning of financial markets and the process by which financing decisions are reached through risk modelling.

The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.

The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.

Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.

The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.

To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.

In this paper weekly volatility forecasts are considered with applications to risk management; in particular hedge ratios and VaR calculations, with the aim of identifying the most appropriate model for risk management practice.

The study considers a variety of models, including those typically employed within the risk management industry, such as averaging and smoothing techniques, as well as those favored in academic circles, such as the GARCH genre of models, and a more recent realized volatility approach which incorporates both the simplicity in construction favored by the finance industry and the flexibility and theoretical underpinnings recommended by academics.

The results support the view that this realized volatility measure provides not only superior volatility forecasts per se, but also allows for improved hedge ratio and VaR calculations.

The research findings carry practical implications for the conduct of risk management, namely that volatility forecasts are best obtained using the realized volatility approach.

It is therefore proposed that a future direction for risk management practice may be to utilize such measures, while more generally it is hoped that such approaches may improve the cross‐fertilization of ideas and practice between the academic and practitioner communities.

In September 1999, the Central Bank of Chile eliminated the floating band for the nominal exchange rate, which operated since 1984, and established a free float. This lasted until the burst of the last Argentinean economic crisis in July 2001. Since then, the Central Bank has smoothed out the exchange rate path by selling U.S. dollars and/or issuing U.S. dollar-denominated bonds. We examine the free float period by assessing whether the increase in exchange rate volatility was as sharp as expected. We show that volatility went up, but only slightly.

Small- and medium-sized enterprises can be highly affected by losses caused by exchange rate changes. The aim of this paper was to find the optimal Value-at-Risk (VaR) method for estimating future exchange rate losses within one year.

The analysis focuses on five VaR methods, some of them traditional and some of them more up to date with integrated EVT or GARCH. The analysis of VaR methods was concentrated on a time horizon (1–12 months), overestimation predictions and six scenarios based on trends and variability of exchange rates. This study used three currency pairs EUR/CZK, EUR/USD and EUR/JPY for backtesting.

In compliance with the backtesting results, the parametric VaR with random walk has been chosen, despite its shortcomings, as the most accurate for estimating future losses in a medium-term period. The Nonparametric VaR confirmed insensitivity to the current exchange rate development. The EVT-based methods showed overconservatism (overestimation predictions). Every parametric or semiparametric method revealed a severe increase of liberality with increasing time.

This research is limited to the analysis of suitable VaR models in a long- and short-run period without using artificial intelligence.

The result of this paper is the choice of a proper VaR method for the online application for estimating the future exchange rate for enterprises.

The orientation of medium-term period makes the research original and useful for small- and medium-sized enterprises.

Investors aim for returns when investing in stocks, making return volatility a crucial concern. This study compares symmetric and asymmetric GARCH models to forecast volatility in emerging nations like the G4 countries. Accurate volatility forecasting is vital for investors to make well-informed investment decisions, forming the core purpose of this study.

From January 1993 to May 2021, the study spans four periods, focusing on the global economic crisis of 2008, the Russian crisis of 2015 and the COVID-19 pandemic. Standard generalized autoregressive conditional heteroscedasticity (GARCH), exponential GARCH (E-GARCH) and Glosten-Jagannathan-Runkle GARCH models were employed to analyse the data. Robustness was assessed using the Akaike information criterion, Schwarz information criterion and maximum log-likelihood criteria.

The study's findings show that the E-GARCH model is the best model for forecasting volatility in emerging nations. This is because the E-GARCH model is able to capture the asymmetric effects of positive and negative shocks on volatility.

This unique study compares symmetric and asymmetric GARCH models for forecasting volatility in emerging nations, a novel approach not explored in prior research. The insights gained can aid investors in constructing more effective risk-adjusted international portfolios, offering a better understanding of stock market volatility to inform strategic investment decisions.

The purpose of this paper is to model the volatility of treasury bill (T-bill) rates in five Sub-Saharan capital markets to investigate whether or not differences in capital mobility affect volatility.

Primary data was collected from weekly T-bill auctions in five Sub-Saharan countries and was analysed using a range of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models in order to determine the volatility characteristics of each of these instruments. Differences in the institutional arrangements for each market are used to interpret the results of the econometric analysis.

Evidence is presented that indicates that the size and financial liberalisation of capital markets affect volatility. While the markets with the greatest exposure to international investors exhibit greater volatility in the long-run, the presence of non-residents in the market appears to contribute to more efficient pricing of these instruments.

The limited sample restricts the ability to generalise these findings, however, the finding that differences exist in the volatility of these markets even though they are geographically similar indicates the value of this methodological approach.

The finding that greater capital mobility may result in increased volatility and greater efficiency has significant policy implications for governments and market regulators who have to weigh the costs and benefits of financial liberalisation.

The paper employs a unique data set to model the volatility characteristics of the selected T-bills to improve the understanding of the behaviour of these important instruments in Sub-Saharan frontier markets. More specifically the study provides a novel empirical approach to addressing the question of whether capital mobility is linked to increased volatility. The finding that capital mobility is linked to greater market efficiency offers a fresh insight to this debate.