Conditional Heteroskedasticity Research Articles

Food insecurity, environment, institutional quality, and health outcomes: evidence from South Asia

BackgroundFood insecurity and environmental degradation pose significant threats to health outcomes in South Asia, necessitating effective policy interventions. Therefore, this study aims to examine the impact of food insecurity and environmental degradation on health outcome indicators amidst global inflationary shocks and institutional quality arrangements. Additionally, it aims to explore the intricate moderating role of institutional quality on the relationship between food insecurity, endogenous variables, and external shocks.MethodIn alignment with the study’s objectives, a set of panel data spanning from 2000 to 2021 is compiled for South Asia. The study introduces a novel variable representing inflationary shock, crafted through the integration of inflation datapoints and the application of the generalized autoregressive conditional heteroskedasticity model. Additionally, a distinctive aggregate institutional quality index is formulated, drawing from six key measures of the Worldwide Governance indicators. To scrutinize the effects of food insecurity, environmental degradation, and other explanatory variables, the study employs the two-step system generalized method of moment technique, offering a robust analytical approach to uncover complex relationships and dynamics in the region.ResultsThe results indicate that the prevalence of undernourishment, inequality in per capita calorie intake, and CO2 emissions significantly reduce life expectancy and increase mortality rates. Additionally, it shows that per capita kilocalorie supply, per capita GDP, per capita health expenditures, and urbanization are statistically significant for increasing life expectancy and decreasing mortality rates. The findings reveal that inflationary shocks severely affect food insecurity and environmental factors, exerting further pressure on contemporary life expectancy and mortality rates. In rebuttal, the institutional quality index is found to have significant effects on increasing and decreasing life expectancy and mortality rates, respectively. Furthermore, the institutional quality index is effective in moderating the nexus between food insecurity, environmental degradation, and health outcomes while also neutralizing the negative impact of inflationary shocks on the subject.ConclusionThe results verify triple health constraints such as food insecurity, environmental factors, and economic vulnerability to global shocks, which impose severe effects on life expectancy and mortality rates. Furthermore, poor institutional quality is identified as a hindrance to health outcomes in South Asia. The findings suggest specific policy implications that are explicitly discussed.

From wars to waves: geopolitical risks and environmental investment behaviour

ABSTRACT This study investigates the impact of geopolitical risk (GPR) on sustainable investments, focusing on five global environmental indices and two global GPR indices. Using Corrected Dynamic Conditional Correlation Generalised Autoregressive Conditional Heteroskedasticity (cDCC-GARCH) model and Diebold and Yilmaz’s spillover analysis, we use daily data from January 2009 to October 2022, covering various market phases, including the European sovereign debt crisis, the COVID-19 pandemic, and the war in Ukraine. Results from the cDCC-GARCH model reveal high dynamic conditional correlations. During periods of high volatility, environmental indices displayed simultaneous and more intense responses, limiting investment diversification alternatives when considering only the environmental side. Diebold and Yilmaz’s static analysis demonstrates that environmental segments are more influenced by systemic shocks than specific causes, with GPR’s influence proving relatively weak. In the dynamic analysis, the spillover effects of GPR in environmental segments intensified during the pandemic crisis and the invasion of Ukraine, affecting market conditions.

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.

THE IMPACT OF THE COVID-19 PANDEMIC AND THE RUSSIAN INVASION OF UKRAINE ON GOLD MARKETS

Purpose – The study examines global Gold market performance and correlations between COVID-19, the Russian invasion, inflation, investors’ fear, asymmetric shocks, and the VIX (volatility index) impact on volatility. Research Methodology – This research uses an econometric approach to analyse the impact of COVID-19 and the Russian invasion on Gold market performance – specifically the ARCH (Autoregressive Conditional Heteroskedasticity) – GARCH (Generalized Autoregressive Conditional Heteroskedasticity) Model and the Threshold-Asymmetric ARCH Model. Findings – The study reveals that the COVID-19 pandemic and the Russian invasion of Ukraine have significantly impacted Gold market dynamics. Inflation significantly impacts volatility, emphasising the need for monitoring inflation rates. Investor fear increases demand during uncertainty periods, while negative shocks have a higher asymmetric effect. The VIX positively affects Gold market volatility, suggesting a perceived safe-haven status during increased volatility. Research Limitations – Gaps in the data related to COVID-19 cases, geopolitical events, and Gold market performance could potentially introduce inaccuracies, biases or limitations to the analysis. Practical Implications – The study’s findings have practical implications for investors, analysts, and policymakers involved in the financial investment of Gold markets. Originality/Value – This study provides new insights into the relationships between inflation, investor fear, asymmetric shocks, and the impact of the VIX on Gold market dynamics during economic and geopolitical uncertainty.

The Influence of Defense Industry Development Act on the Smooth Transition Dynamics of Stock Volatilities of Defense Industry

Abstract This study aims to investigate the impacts of the Defense Industry Development Act on the volatility of the defense industry as geopolitical risk is raised. Applying the smooth transition generalized autoregressive conditional heteroskedasticity (ST-GARCH) model for daily defense stocks, we demonstrate that the structure breaks in the volatility dynamics process of all defense stocks for Taiwan. The empirical findings show that most defense stocks started the adjustment process more than one year before the date of launch of Defense Industry Development Act except Magnate Technology Corporation (MTC) and China Ship Building Corporation Taiwan (CSBC). The model specification tests suggest two types of transition functions including U-shaped and Z-shaped for all defense stocks. The estimated parameters indicate that the volatilities of returns in defense stocks for Taiwan have inverted U-shaped and inverted Z-shaped patterns of structure breaks. The volatilities of defense enterprise stock return shift by the event of Defense Industry Development Act. JEL classification numbers: G00, G14, G18, L52. Keywords: Geopolitical, Defense industry, Volatility, Structure change, Defense Industry Development Act

The Role of Macroeconomic Variables in Forecasting Equity Market Volatility in the East African Community Using Garch-Midas Model

This study delves into the dynamic relationship between macroeconomic variables and equity market volatility in the East African Community. The research employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model coupled with the Mixed Data Sampling (MIDAS) approach. Through a comparative process, it is found that the different macroeconomic variables exhibit heterogeneous effects on the different countries in the East African community that is macroeconomic factors significantly explain the variation in stock market volatility in Uganda and including these factors in the GARCH-MIDAS model improved its forecasting ability, however, in Kenya it was found that majority of the macroeconomic variables had insignificant effects on stock market volatility and didn’t improve the forecasting ability of the GARCH-MIDAS model.

Are there other fish in the sea? Exploring the hedge, diversifier and safe-haven features of ESG investments

Purpose This study aims to conduct an empirical investigation to assess the hedge, diversifier and safe-haven properties of different environmental, social and governance (ESG) assets (i.e. green bonds and ESG equity index) vis-à-vis conventional investments (namely, equity index, gold and commodities). Design/methodology/approach The authors examine the sample period 2007–2021 using the bivariate cross-quantilogram (CQG) analysis and a dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity (GARCH) experiment with several extensions. Findings The evidence shows that the analyzed ESG investments exhibit mainly diversifying features depending on the asset class taken as a reference, with some potential hedging/safe-haven qualities (for the green bond) in peculiar timespans. Therefore, the results suggest that investors might consider sustainable investing as a new measure of risk reduction, which has interesting implications for both portfolio allocation and policy design. Originality/value To the best of the authors’ knowledge, this study is the first that empirically investigates at once the dependence between different ESG investments (i.e. equity and green bond) with different conventional investments such as gold, equity and commodity market indices over a large sample period (2007–2021). Well-suited methodologies like the bivariate CQG and the DCC multivariate GARCH are used to capture the spillover effect and the hedging/diversifying nature, even in temporary contexts. Finally, a global perspective is used.

Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models.

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

Identifying dynamic risk spillovers between crude oil and downstream industries: China's futures market perspective.

Interactions between crude oil and its downstream products are crucial but complex. The main purpose of this study is to examine the risk spillover relationships between the crude oil futures market and the petrochemical downstream futures market in the context of the COVID-19 epidemic in China. By combining the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model and the Diebold-Yilmaz spillover index based on time-varying parameter-vector autoregression (TVP-VAR-DY), we investigate the dynamic correlations between Shanghai crude oil futures (INE) and the downstream futures in China's petrochemical industry chain. At the same time, we also incorporate the representative global crude oil futures (BRENT and WTI) in our study as a comparative analysis. Our results show a significant positive correlation between three crude oil futures and China's downstream future products, with a more pronounced link observed between INE and the downstream futures market. Moreover, the correlation between crude oil futures and various downstream products exhibits heterogeneity; that is, direct derivatives of crude oil show higher sensitivity to price fluctuations compared to products with longer production chains. Furthermore, the spillover results indicate that the international crude oil futures, particularly BRENT, primarily function as spillover transmitters, while INE mainly serves as the recipient. In the post-pandemic period, the international crude oil market still exhibits a high spillover effect, and the spillover effect of INE to polyvinyl chloride, pure terephthalic acid, and bitumen futures increased, reflecting market recovery in China to some extent. These results provide potential insights for policymakers, financial institutions, industry participants, and investors, emphasizing the importance of enhanced risk management, diversified investment strategies, and attention to market dynamics.

Tail risk forecasting and its application to margin requirements in the commodity futures market

AbstractThis study presents a dynamic analysis framework called autoregressive conditional extreme value (AEV), designed for modeling the daily maximum drawdowns of commodity futures markets, using steel rebar futures as an illustrative example. The research demonstrates that AEV outperforms AR or generalized autoregressive conditional heteroskedasticity (GARCH)‐type benchmark models in terms of in‐sample fitting and out‐of‐sample forecasting accuracy. Notably, AEV's time‐varying shape parameter (tail index) sensitively captures the clustering nature of tail risk and differentiates between long‐ and short‐side markets. The study also presents theoretical findings regarding AEV‐based value at risk (VaR) and expected shortfall (ES), and empirically measures and predicts the tail risk of the steel rebar futures market. Moreover, the research extends the methodology to create a dynamic margin model for Chinese commodity futures, showing that the AEV‐based model effectively achieves the specified risk coverage targets and significantly reduces current exchange margin requirements.

Forecasting the high‐frequency volatility based on the LSTM‐HIT model

AbstractVolatility forecasting from high‐frequency data plays a crucial role in many financial fields, such as risk management, option pricing, and portfolio management. Many existing statistical models could better describe and forecast the characteristics of volatility, whereas they do not simultaneously account for the long‐term memory of volatility, the nonlinear characteristics of high‐frequency data, and technical index information during the modeling phase. The purpose of this paper is to use the prediction advantage of deep learning long short‐term memory (LSTM) model to predict the volatility fusing three classes of information, that is, high frequency realized volatility (H), technical indicators (I), and the parameters of generalized autoregression conditional heteroskedasticity(GARCH), heterogeneous autoregressive (HAR), and c, resulting in a novel LSTM‐HIT model to forecast realized volatility. We employ the extreme value theory (EVT) of a semiparametric method to estimate the quantile of standardized return and construct the LSTM‐HIT‐EVT model to forecast the value at risk (VaR). Empirical results show that the LSTM‐HIT model provides the most accurate volatility forecast among the various considered models and that the LSTM‐HIT‐EVT model yields forecasts more accurate than other VaR models.

Predicting coal workers' pneumoconiosis trends: Leveraging historical data with the GARCH model in a Chinese Miner Cohort.

Coal workers' pneumoconiosis (CWP) is one of the most common and severe occupational diseases worldwide. The main risk factor of CWP is exposure to respirable mine dust. Prediction theory was widely applied in the prediction of the epidemic. Here, it was used to identify the characteristics of CWP today and the incidence trends of CWP in the future. Eight thousand nine hundred twenty-eight coal workers from a state-owned coal mine were included during the observation period from 1963 to 2014. In observations, the dust concentration gradually decreased over time, and the incidence of tunnels and mine, transportation, and assistance workers showed an overall downward trend. We choose a better prediction model by comparing the prediction effect of the Auto Regression Integrate Moving Average model and Generalized Autoregressive Conditional Heteroskedasticity model. Compared with the Auto Regression Integrate Moving Average model, the Generalized Autoregressive Conditional Heteroskedasticity model has a better prediction effect. Furthermore, the status quo and future trend of coal miners' CWP are still at a high level.

Chaos, Fractionality, Nonlinear Contagion, and Causality Dynamics of the Metaverse, Energy Consumption, and Environmental Pollution: Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity Copula and Causality Methods

Metaverse (MV) technology introduces new tools for users each day. MV companies have a significant share in the total stock markets today, and their size is increasing. However, MV technologies are questioned as to whether they contribute to environmental pollution with their increasing energy consumption (EC). This study explores complex nonlinear contagion with tail dependence and causality between MV stocks, EC, and environmental pollution proxied with carbon dioxide emissions (CO2) with a decade-long daily dataset covering 18 May 2012–16 March 2023. The Mandelbrot–Wallis and Lo’s rescaled range (R/S) tests confirm long-term dependence and fractionality, and the largest Lyapunov exponents, Shannon and Havrda, Charvât, and Tsallis (HCT) entropy tests followed by the Kolmogorov–Sinai (KS) complexity measure confirm chaos, entropy, and complexity. The Brock, Dechert, and Scheinkman (BDS) test of independence test confirms nonlinearity, and White‘s test of heteroskedasticity of nonlinear forms and Engle’s autoregressive conditional heteroskedasticity test confirm heteroskedasticity, in addition to fractionality and chaos. In modeling, the marginal distributions are modeled with Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity Copula (MS-GARCH–Copula) processes with two regimes for low and high volatility and asymmetric tail dependence between MV, EC, and CO2 in all regimes. The findings indicate relatively higher contagion with larger copula parameters in high-volatility regimes. Nonlinear causality is modeled under regime-switching heteroskedasticity, and the results indicate unidirectional causality from MV to EC, from MV to CO2, and from EC to CO2, in addition to bidirectional causality among MV and EC, which amplifies the effects on air pollution. The findings of this paper offer vital insights into the MV, EC, and CO2 nexus under chaos, fractionality, and nonlinearity. Important policy recommendations are generated.

Herding behaviour and sentiment: evidence from emerging markets

PurposeBased on the theoretical framework, this paper analyzes the sentiment-herding relationship in emerging stock markets (ESMs). First, it aims to examine the effect of investor sentiment on herding. Second, it seeks the direction of causality between sentiment and herding time series.Design/methodology/approachThe present study applies the Exponential Generalized Auto_Regressive Conditional Heteroskedasticity (EGARCH) model to capture the volatility clustering of herding on the financial market and to investigate the role of the investor sentiment on herding behaviour. Then the vector autoregression (VAR) estimation uses the Granger causality test to determine the direction of causality between the investor sentiment and herding. This study uses a sample consisting of stocks listed on the Shanghai Composite index (SSE) (348 stocks), the Jakarta composite index (JKSE) (118 stocks), the Mexico IPC index (14 stocks), the Russian Trading System index (RTS) (12 stocks), the Warsaw stock exchange General index (WGI) (106 stocks) and the FTSE/JSE Africa all-share index (76 stocks). The sample includes 5,020 daily observations from February 1, 2002, to March 31, 2021.FindingsThe research findings show that the sentiment has a significant negative impact on the herding behaviour pointing out that the higher the investor sentiment, the lower the herding. However, the results of the present study indicate that a higher investor sentiment conducts a higher herding behaviour during market downturns. Then the outcomes suggest that during the crisis period, the direction is one-way, from the investor sentiment to the herding behaviour.Practical implicationsThe findings may have implications for universal policies of financial regulators in EMs. We have found evidence that the Emerging investor sentiment contributes to the investor herding behaviour. Therefore, the irrational investor herding behaviour can increase the stock market volatility, and in extreme cases, it may lead to bubbles and crashes. Market regulators could implement mechanisms that can supervise the investor sentiment and predict the investor herding behaviour, so they make policies helping stabilise stock markets.Originality/valueThe originality of this paper lies in investigate the sentiment-herding relationship during the Surprime crisis and the Covid-19 epidemic in the EMs.

Sequential management of energy and low-carbon portfolios

This study explores the ability of clean energy and European Union Allowance (EUA) assets to diminish portfolio risk when mixed with unclean energy assets. We use a family of Asymmetric Dynamic Conditional Correlation-Generalized AutoRegressive Conditional Heteroskedastic (ADCC-GARCH) models and provide a flexible and adaptive estimation and model selection framework based on a sequential strategy with differently sized estimation and validation windows, as well as different model update frequencies. Through this procedure, we obtain accurate estimations of the conditional covariance matrices of day-to-day asset returns and build adequate optimal minimum variance portfolios. The analyzed period (Jan. 2010–May. 2022) includes the latest crisis episodes (Sovereign debt crisis, Brexit, COVID-19, and the Russian–Ukrainian war). Our findings show that since the 2015 Paris Agreement (the only exception being the pandemic period), investing in clean energy companies and EUAs is an attractive investment in terms of return-risk. These results should provide investors with more incentives to decarbonize their portfolios.

Exchange Rate Dynamics and Agricultural Sector Performance Nexus: The Nigerian Experience

The main objective of the study is to investigate the impact of exchange rate dynamics on agricultural sector performance in Nigeria. The study employed time series data obtained from the Central Bank of Nigeria statistical bulletin, World Development Indicators and National Bureau of Statistics. Agricultural sector performance was disaggregated into the overall agricultural, crop, livestock and fishery output. Autoregressive Distributive Lag (ARDL) and Generalized Autoregressive Conditional heteroskedasticity (GARCH) estimation techniques were used to establish the long run relationship among the variables, and the responsiveness of overall agricultural output, crop, livestock and fishery production to changes in exchange rate. It was revealed that long run relationship exists among the variables in all the estimated models. The result of the Error Correction Mechanism (ECM) within the framework of the ARDL shows that exchange rate has significant impact on agricultural sector performance. The GARCH results revealed that the responsiveness of aggregate agricultural output, crop, livestock and fishery production to changes in exchange rate is negative and statistically significant. This study concludes that the government must consciously direct policy actions towards the agricultural sector to achieve its full potentials in order to place the Nigerian economy on the path of self- sufficiency in agricultural production. The study recommends that; the government should implement appropriate exchange rate policy that will ensure sufficient crop production for both domestic consumption and exports. The movement in the market determined exchange rate should be strictly monitored by the apex bank, in order to ensure that the deregulation in exchange rate is not counterproductive through distortionary prices on agricultural production.

Vine Copula Approach to Understand the Financial Dependence of the Istanbul Stock Exchange Index

AbstractRecently, the complex dependence patterns among various stocks gained more importance. Measuring the dependency structure is critical for investors to manage their portfolio risks. Since the global financial crisis, researchers have been more interested in studying the dynamics of dependency within stock markets by using novel methodologies. This study aims to investigate a Regular-Vine copula approach to estimate the interdependence structure of the Istanbul Stock Exchange index (ISE100). For this purpose, we consider 32 stocks related to 6 sectors belonging to ISE100. To reflect the time-varying impacts of the 2008–2009 global financial crisis, the dependence analysis is conducted over pre-, during-, and post-global financial crisis periods. Portfolio analysis is considered via a rolling window approach to capture the changes in the dependence. We compare the Regular-Vine-based generalized autoregressive conditional heteroskedasticity (GARCH) against the conventional GARCH model with different innovations. Value at risk and expected shortfall risk measures are used to validate the models. Additionally, for the constructed portfolios, return performance is summarized using both Sharpe and Sortino ratios. To test the ability of the considered Regular-Vine approach on ISE100, another evaluation has been done during the COVID-19 pandemic crisis with various parameter settings. The main findings across different risky periods illustrate the suitability of using the Regular-vine GARCH approach to model the complex dependence among stocks in emerging market conditions.

Can the Sell in May effect be enhanced by a size tilt?

PurposeThe authors evaluate the Sell in May effect in the Canadian context to comprehensively explore the Sell in May effect as well as its interactions with the size effect and risk and with multiple indices.Design/methodology/approachThe authors use ordinary least squares (OLS) regressions to examine the Sell in May effect and Huber M-estimation to handle potential outliers. They also use the generalized autoregressive conditional heteroskedasticity (GARCH) models to explore the role of risk in the Sell in May effect.FindingsThe results demonstrate that the Sell in May effect is present in all three main Canadian stock market indices. More telling, the anomaly is strongest in small cap indices and in indices that give equal weighting to small and large cap stocks. They do not find that the effect is driven by risk.Originality/valueWhile several papers have explored the Sell in May phenomenon in several countries, little scholarly attention has been paid to this effect in Canada and to its interaction with the size effect. The authors contribute to the literature by examining of the interactions between Sell in May and the size effect in Canada. They examine the Sell in May effect using CFMRC value-weighted and equally weighted indices of all Canadian companies. They also incorporate in their analysis the role of risk.

An EM/MCMC Markov-Switching GARCH Behavioral Algorithm for Random-Length Lumber Futures Trading

This paper tests using two-regime Markov-switching models with asymmetric, time-varying exponential generalized autoregressive conditional heteroskedasticity (MS-EGARCH) variances in random-length lumber futures trading. By assuming a two-regime context (a low s=1 and high s=2 volatility), a trading algorithm was simulated with the following trading rule: invest in lumber futures if the probability of being in the high-volatility regime s=2 is lower or equal to 50%, or invest in the 3-month U.S. Treasury bills (TBills) otherwise. The rationale tested in this paper was that using a two-regime Markov-switching (MS) algorithm leads to an overperformance against a buy-and-hold strategy in lumber futures. To extend the current literature in MS trading algorithms, two location parameter scenarios were simulated. The first uses an unconditional mean or expected value (no factors), and the second incorporates market and behavioral factors. With weekly simulations form 2 January 1994 to 28 July 2023, the results suggest that using MS-EGARCH models in a no-factors scenario is appropriate for active lumber futures trading with an accumulated return of 158.33%. Also, the results suggest that it is not useful to add market and behavioral factors in the MS-GARCH estimation because it leads to a lower performance.

BALANCE OF TRADE, EXCHANGE RATE AND ECONOMIC GROWTH IN NIGERIA

This study delved into the relationship between the balance of trade, exchange rates, and economic growth in Nigeria. Utilizing annual time series data spanning from 1981 to 2021 sourced from the Central Bank of Nigeria (CBN) statistical bulletin and the National Bureau of Statistics (NBS), the investigation employed a Heteroskedasticity Model. Within this framework, GARCH, an expanded version of ARCH, and the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGACH) were applied. The findings revealed that both trade balance and exchange rates exhibited heightened sensitivity to adverse news, indicating that uncertainties surrounding exchange rates significantly contributed to fluctuations. Moreover, the ARCH effects initially observed in the balance of trade and exchange rates were completely mitigated by the considered heteroskedastic models. Furthermore, the study identified a negative and statistically significant impact of the balance of trade on economic growth. Similarly, exchange rates were found to exert an undesirable significant influence on economic growth in Nigeria. This suggests that unfavorable changes in the Nigerian naira tend to exacerbate its volatility. Notably, while the relationship between trade balance and economic growth was positive, it did not reach statistical significance. In conclusion, the study underscores the critical role of external demand in driving economic growth in Nigeria, particularly highlighting the stimulative effect of a strong depreciation of the domestic currency. As a recommendation, the government is urged to adopt export-driven policies aimed at bolstering trade balance, fostering export, particularly of primary products, to attract foreign exchange inflows and foreign investment, thereby spurring economic growth in Nigeria.