GARCH Research Articles

Cybersecurity strategies for protecting big data in business intelligence systems: Implication for operational efficiency and profitability

The alarming increase in consumers goods and services with concurrent devaluations of naira have necessitated the need to examine the effects of NUSX(Nigerian naira/1 US dollar), NFX (Nigerian naira/1 France franc) and NCX (Nigerian naira/1 Chinese yuan) rates on inflation(measured by CPI) and its volatility in Nigeria using data sets spanning from 2008 to 2022. The auxiliary autoregressive AAR(3) order of integration test specify that all the variables are stationary at first difference. Adopting multiple regression model with least square method of estimation and generalized autoregressive conditional heteroskedasticity (GARCH(1, 1)) as measure of volatility, the results indicate that NCX (Nigerian naira/1 Chinese yuan), NFX (Nigerian naira/1 France franc) and NUSX(Nigerian naira/1 US dollar) have no influence on both inflation and its volatility in Nigeria. This implies that variation in exchange rates system is not sufficient to explain the sudden increase in the prices of goods and services in Nigeria. Therefore, the government should look beyond exchange rates in finding solution to rising inflation phenomenon in Nigeria’s economic space.

Bitemark analysis comparing the use of digital scans and 3D resin casts.

Although dental patterns are unique, the use of bitemark analysis in personal identification remains controversial. To accurately reproduce and compare three-dimensional models of bitemarks and dental arches, intraoral three-dimensional scans, commonly utilized in clinical dental practice for precise and stable digital impressions, are recommended. This study aims to compare two different techniques for bitemark analysis: a digital method based on the superimposition of digital scans of dental patterns and lesions, and a visual method based on the physical superimposition of impressions and resin casts produced by 3D printing. A sample of 12 volunteers (6 males and 6 females) with a mean age of 26 years was collected as biters. Each subject was asked to bite on custom supports made from semi-rigid water bottles covered with imprintable dental wax. The dental arches and bitemarks were then recorded using an intraoral scanner and dental impressions. Scan superimposition analysis was conducted using CloudCompare software, while resin casts were printed using a 3D printer and physically superimposed on the bitemark impressions by a blind operator, who was not involved in sample collection, bite test execution, prior cast acquisition, or CloudCompare analysis. Both superimposition techniques relied on the selection of 10 corresponding landmarks (on canines and central and lateral incisors of the upper and lower arches) between the dental arches and impressions. The digital superimposition showed an average concordance of 92.5% for the upper arch landmarks and 85% for the lower arch landmarks, with an overall average concordance of 88.8% for both arches combined. In contrast, the visual analysis of resin casts showed an average concordance of 77.5% for the upper arch and 76.7% for the lower arch, with an overall average of 77.1% for both arches combined. In the analysis performed using CloudCompare, the maxillary arch demonstrated the best superimposition, with 4 landmarks (R0, R1, R2, R5) consistently overlapping. The digital analysis outperformed the visual analysis in all four quadrants, particularly in the upper right arch compared to the lower left arch, thereby supporting the integration of digital techniques in forensic applications. Further studies are necessary to validate the digital technique on a larger sample, including subjects with different dental characteristics, bite dynamics, and varying types of supports and substrates.

Applications of GARCH Models in Forecasting Financial Market Volatility: Insights from Leading Global Stock Indexes

This study investigates the volatility dynamics of major global stock indexes, including the FTSE 100, Hang Seng Index, NIKKEI 225, and S&P 500, using a range of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The analysis spans a comprehensive 20-year period from January 1, 2004, to December 31, 2023, encompassing diverse market conditions such as bull and bear markets, the 2008 financial crisis, and the COVID-19 pandemic. The methodology includes preprocessing steps such as calculating daily log returns, performing descriptive statistics, and conducting stationarity and ARCH effect tests to ensure data suitability for volatility modelling. The study evaluates several GARCH models, including GARCH, EGARCH, NGARCH, APARCH, GJR-GARCH, and TGARCH, to forecast volatility and address both symmetric and asymmetric effects. The TGARCH model exhibits strong performance in capturing leverage effects and asymmetries, particularly for the FTSE 100 and Hang Seng Index. The APARCH model performs well for the S&P 500, demonstrating sensitivity to past shocks. Overall, the findings underscore the importance of advanced GARCH models in accurately predicting volatility in global financial markets, highlighting the TGARCH model's effectiveness in addressing asymmetries and providing insights into selecting appropriate models for enhanced financial analysis and risk management.

Volatility in the Equity Stock Market in India from 2019 to 2024: An Empirical Analysis

This research investigates the volatility and fluctuations in the Indian equity stock market from 2019 to 2024, a period characterized by unprecedented global and domestic challenges. The Indian stock market, like many emerging markets, experienced heightened volatility due to a confluence of events, including the COVID-19 pandemic, geopolitical tensions, economic reforms, and shifts in global financial conditions. The study employs advanced econometric models, including GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and event study analyses, to capture the time-varying nature of market volatility and to identify the key factors driving these fluctuations. Our findings reveal that the COVID-19 pandemic triggered the most significant spikes in volatility, particularly during the early months of 2020, as uncertainty regarding the pandemic's impact on the economy led to sharp market corrections and investor panic. Additionally, the study highlights the influence of macroeconomic variables such as GDP growth, inflation, and monetary policy changes on market behavior. The paper also explores the role of investor sentiment and market liquidity in exacerbating volatility during crisis periods. The results provide valuable insights into the dynamics of the Indian equity market, offering implications for investors seeking to manage risk and for policymakers aiming to enhance market stability. The study underscores the need for robust risk management strategies and proactive regulatory measures to mitigate the adverse effects of such volatility, especially in an increasingly interconnected global financial environment. Keywords: Equity Stock Market, Volatility, India, COVID-19, Economic Reforms, 2019-2024

IMPACT OF THE RUSSIA-UKRAINE CONFLICT ON INDONESIAN COMMODITY STOCKS: A SYSTEMATIC LITERATURE REVIEW

The Russia-Ukraine conflict has significantly impacted Indonesian commodity stocks, as revealed by a comprehensive literature review. The conflict has introduced volatility in global markets, directly affecting Indonesian stock commodity-based prices, exchange rates, and investor sentiment. While initial commodity price rises benefited Indonesian stocks, persistent volatility and market interconnectedness pose challenges. This paper employs a systematic literature review methodology, collecting, synthesizing, and analyzing numerous papers published in the last decade on a variety of topics closely related to volatility transmission issues. Based on our review of twenty-seven articles, the result of this paper's analysis underscores the importance of diversifying investments and enhancing resilience against global disruptions. The GARCH models and the Geopolitical Risk Index further emphasize Indonesian commodity stocks' susceptibility to global price fluctuations and geopolitical risks. The research concludes that the Russia-Ukraine conflict has far-reaching consequences for Indonesian stock commodity-based markets, underscoring the need for risk mitigation strategies and enhanced resilience against future geopolitical shocks.

Analytical model of three-dimensional concentric ellipsoidal soil arching in geosynthetic-reinforced pile-supported embankments

The geosynthetic-reinforced pile-supported (GRPS) embankment is an effective method for improving soft ground, widely adopted in engineering applications. In this paper, a concentric ellipsoidal soil arching model was proposed to describe the stress distribution within the GRPS embankment. An analytical solution for soil arching efficacy was derived by solving the loads acting on the pile caps and geosynthetics under piles arranged in a squared pattern. Subsequently, finite difference models were established to verify the accuracy of the derived analytical solution. Meanwhile, four field tests were introduced to validate the analytical model. Finally, parametric studies were conducted on the concentric ellipsoidal soil arching model, considering parameters such as the embankment height, the pile spacing, the pile cap width, the unit weight, and the friction angle of fill soil.

Evaluating the Efficacy of GARCH Models in Forecasting Volatility Dynamics Across Major Global Financial Indices: A Decade-long Analysis

This study investigates the volatility dynamics of five major global financial indices—FTSE 100, Hang Seng, NIKKEI 225, NSE 50, and S&P 500—using a range of GARCH models over a ten-year period from January 1, 2014, to December 31, 2023. The analysis involves preprocessing the data to ensure stationarity, calculating log returns, and conducting stationarity and ARCH effect LM tests. Various GARCH models, including GARCH (0,1), GARCH (1,1), GARCH (1,2), and GARCH (2,2), are applied to capture and forecast volatility. The study aims to determine the most effective model for accurately reflecting volatility dynamics while accounting for significant market events such as the COVID-19 pandemic. The findings reveal that the GARCH (1,1) model generally provides a robust balance between model simplicity and statistical significance, effectively capturing the time-varying volatility of the indices. Despite some complex models offering better fit measures according to the Akaike Information Criterion (AIC) and Schwarz Criterion (SC), the GARCH (1,1) model consistently demonstrates significant parameter estimates and reliable predictive performance, as evidenced by consistent Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This suggests that the GARCH (1,1) model is a preferred choice for volatility forecasting due to its effectiveness and parsimony, although future research might explore more advanced GARCH model variations for further refinement.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)<sub>12</sub> model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

Harmony in diversity: Exploring connectedness and portfolio strategies among crude oil, gold, traditional and sustainable index

The research investigates dynamic connectedness and portfolio strategies among crude oil, gold, global traditional (DJGI) and sustainable (DJSI) indices. The time-varying parameter vector autoregression (TVP-VAR) findings reveal that the average total connectedness among the assets is 33.03%, with the total dynamic connectedness index exhibiting a notable surge during two crises and one stability period. The upsurge exhibited a predominant prevalence of short-run connectedness during crisis periods, notably amidst the COVID-19 pandemic and the Russia-Ukraine war. Conversely, the last and first quarters of 2017 and 2018 manifested as periods of stability, wherein long-term interconnectedness prevailed. Crude oil and gold emerge as the primary recipients of systemic shocks, while traditional and sustainable stock indices act as net transmitters of shocks. The BEKK and DCC GARCH models indicate short- and long-run persistence among the assets. Further, the study calculated hedge ratios and portfolio weights. Our empirical findings indicate that gold serves as a cost-effective hedging asset. Investors should invest more in the traditional stock index than in gold and crude oil to form an optimal portfolio. Notably, the global sustainable index offers results similar to those of the traditional index. Investors in the sustainable index would invest a similar amount for diversification and hedging with other traditional assets as they would for the traditional index.

Application of Vector Autoregressive (VAR) Model on the Interaction of Inflation Rates and Public Debt in Kenya from 2011 to 2021

This study examines the relationship between public debt and inflation rates in Kenya from 2011 to 2021 using the Vector Autoregressive (VAR) model. Despite the models likeAutoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) gaining popularity in time series analysis, the Vector Autoregressive model, being multivariate, is relevant in analyzing two or more time series variables simultaneously, benefiting from the bi-directional causality and providing a better outlook into the flow of the dynamic interaction between inflation and public debt. The main objectives are modelling the Vector Autoregressive model and forecasting future trends to provide insights for policymakers. Additionally, the methodological approach comprises descriptive statistics, stationarity tests, normality tests, and the Vector Autoregressive model. Descriptive statistics reveal significant variations, with public debt increasing from 1.35 trillion KES to a peak of 8.2 trillion KES, and inflation rates ranging from 3.2% to 19.72% for the period from 2011 to 2021. The Augmented Dickey-Fuller (ADF) test confirmed that both time series were stationary at their levels. The Vector Autoregressive model, chosen for its ability to analyze dynamic interactions, indicated a significant relationship between the variables, with inflation showing strong self-persistence (coefficient of 0.8731, <i>p</i> < 2 × 10<sup>−16</sup>), though public debt did not significantly impact inflation in the model (<i>p</i> = 0.5592). The models R-squared values, 95.82% for public debt and 84.74% for inflation, highlight its strong explanatory power. Moreover, findings indicate that while public debt does not directly affect inflation within the model lag structure, inflation exhibits a strong self-persistence. The model R-squared values are 95.82% for public debt and 84.74% for inflation, demonstrating high explanatory power. Recommendations include the implementation of a robust debt management strategy, emphasizing sustainable borrowing and enhancing revenue generation to mitigate inflationary pressures. Further research is recommended to explore the broader macroeconomic impacts of public debt on economic growth and employment in Kenya.

Mimicking crypto portfolios in sustainable investment

In this paper, considering the difference in the energy demand level, we utilize the daily pricing data from 10/01/2019 to 06/30/2023 to construct mimicking crypto portfolios with 12 clean cryptocurrencies to replace the dirty cryptocurrency, Bitcoin (BTC). With a monthly rebalancing strategy, the mimicking portfolio closely matches the exposures to the risk factors of the BTC but with fewer specific risks. Furthermore, relying on the bivariate dynamic conditional correlation (DCC-) GARCH model, we compare the hedging capability of BTC and the corresponding mimicking crypto portfolio against movements of returns of sustainable assets. The empirical results show that the mimicking crypto portfolio provides ESG investors with a cheaper hedge tool of higher hedge effectiveness compared to the BTC. Moreover, we find that the mimicking crypto portfolio can act as a strong safe haven for the S&P Global Clean Energy Index and S&P Latin America Emerging LargeMidCap ESG Index during periods of market stress. Therefore, the mimicking crypto portfolio is a more attractive option for ESG investors due to its superior hedging efficiency and the added environmental advantages.

Persistent and transient variance components in option pricing models with variance-dependent Kernel

This paper examines theoretically and empirically a variance-dependent pricing kernel in the continuous-time two-factor stochastic volatility (SV) model. We investigate the relevance of such a kernel in the joint modeling of index returns and option prices. We contrast the pricing performance of this model in capturing the term structure effects and smile/smirk patterns to discrete-time GARCH models with similar variance-dependent kernels. We find negative and significant risk premium for both volatility factors, implying that investors are willing to pay for insurance against increases in volatility risk, even if it has little persistence. In-sample, the component GARCH model exhibits a slightly better fit overall and across all maturity buckets than the two-factor SV model. However, the two-factor SV model reduces strike price bias, giving rise to the model’s ability in reconciling the physical and risk-neutral distribution. Out-of-sample, the two-factor SV model has better fit to data.

Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models

PurposeThe goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023.Design/methodology/approachThe data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test.FindingsUsing the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1).Originality/valueThe study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

An Entropic Analysis of Efficiency in the West Texas Intermediate Crude Oil Futures Market

For the last 50 years or so, the efficient market hypothesis (EMH) has been the central pillar of economic thought and the building block of portfolio theory. However, the validity of the EMH still remains controversial, and the methods of testing for market efficiency have been criticised. This study utilises entropy analysis combined with generalised autoregressive conditional heteroscedasticity (GARCH) regressions to provide extensive empirical evidence on informational efficiency. This study develops an innovative method for studying market efficiency by investigating the complexity of information saturated in the market. To this end, we have used daily West Texas Intermediate (WTI) crude oil futures contracts from June 1983 to February 2016, giving a total data set of 282,427 observations. The results of the Jensen–Shannon informational criteria indicate that the informational efficiency measure has never reached the maximum value of 1 which indicates weak-form efficiency. Our study has further found that the informational efficiency measure tends to decrease consistently during recessionary periods and to increase in subsequent periods. We have also found that the entropy measure tends to increase over time, supporting the adaptive market hypothesis over the EMH.

A combined framework to explore cryptocurrency volatility and dependence using multivariate GARCH and Copula modeling

During the last years, cryptocurrencies have been increasingly becoming a relevant subject of academic researchers and investors. This paper adopts a novel framework that combines a multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and Copula modeling in a two-stage approach to analyze the cryptocurrency volatility dynamics. By combining the aforementioned techniques, on top of showing that price movements in one cryptocurrency can significantly influence others, the use of copulas highlight how these effects can vary across different parts of distributions and thus for different types of events with respect to their extreme nature. The interconnectedness complexity should be taken into consideration when managing risk in portfolio and constructing relevant models.

DISAPPEARING THE DAY OF THE WEEK EFFECT ON RETURN TO INVESTORS: NOVEL EVIDENCE FROM INDIAN STOCK MARKET

The efficiency in emerging markets is becoming more important as the trend of investment in these markets is accelerating nowadays. The level of market efficiency influences an investor's investment strategy because of its improper valuation. The improper valuation of securities may lead to abnormal gains for the investor. On the other hand, abnormal gain in a perfectly efficient market is impossible as the share price absorbs all the information available in the public domain. However, efficiency in its finest sense is a distant reality. Many researchers have pointed out different levels of efficiencies, including the presence of calendar anomalies. The purpose of the study is to capture the effect of the day of the week on the stock return and volatility in India. Daily time series data of Sensex and Nifty were collected from the two prominent exchanges of India, i.e. Bombay Stock Exchange and National Stock Exchange websites, for a period of four years. After validating stationarity with ADF and Phillip-Perron tests, the study used GARCH, EGARCH and TGARCH models to capture day impacts on stock return and volatility. The study reveals that the Tuesday effect persisted in the Indian stock market during the study period. Moreover, the TGARCH model was found to be the most suitable model for describing volatility behaviour in the Indian stock market. As other days in the week were less significant, the study's findings suggest that the gradual disappearance of day affects the Indian capital market. Another study finding is the significance of the leverage effect, which implies the increased role of bad news over good news while capturing the day-of-the-week effect. Considering the above facts, the present study's findings contribute to a deeper understanding of stock market dynamics and the challenge of the day-of-the-week effect on market efficiency.

Volatilitas Nilai Tukar dan Harga Komoditas Global selama Krisis Laut Merah

Purpose: This study aims to determine the most suitable model for assessing the volatility of currency exchange rates for Indonesia, Malaysia, and Singapore, as well as global commodity prices, specifically gold and crude oil. Methodology: The research was conducted by performing various tests using the SPSS version 25 application, followed by a volatility analysis employing the GARCH method with the E-Views version 9 application. Results: The study reveals that the Red Sea crisis significantly impacts global investment volatility. The EGARCH(1,1) model best describes the volatility of USD/IDR, USD/SGD, and crude oil prices, while the GJR-GARCH(1,1) model is most suitable for USD/MYR, and the GARCH(1,1) model fits gold price volatility. Limitations: This study is limited by its focus on only three countries (Indonesia, Malaysia, and Singapore) and two commodities (gold and crude oil). Additionally, the analysis only covers the period following the first attack in the Red Sea crisis. Contribution: This research provides valuable insights for decision-makers and investors by highlighting the impact of global events on market volatility.

Pricing VIX Futures and Options With Good and Bad Volatility of Volatility

ABSTRACTThis article studies the pricing of VIX futures and options by directly modeling the dynamics of VIX, based on realized semivariances computed from high‐frequency data of VIX. We derive the closed‐form pricing formula for both the VIX futures and options. The empirical results show that the new model provides superior pricing performance compared with the model based on conventional unsigned realized variance and the classic Heston‐Nandi GARCH model, both in sample and out of sample. Our study confirms that the decomposition of realized variance into upside and downside components helps to improve the pricing performance for VIX futures and options.

Heterogeneous Volatility Information Content for the Realized GARCH Modeling and Forecasting Volatility

Abstract As the demand for accuracy in volatility modeling and forecasting increases, the literature tends to incorporate different volatility measures with heterogeneous information content to construct the hybrid volatility model. This study focuses on one of the popular hybrid volatility models: the Realized Generalized Autoregressive Heteroskedasticity (Realized GARCH) and embeds various volatility measures, including the CBOE VIX, VIX1D, Realized Volatility, and Daily Range to examine their heterogeneous impact on the conditional volatility estimation and forecasting. To evaluate the impact of the volatility measures, we first construct a volatility response function. This involves calculating the difference in one-step-ahead conditional volatility forecasts that incorporate information from both return and volatility measures against the forecasts based on return innovations only. Subsequently, the variance share is calculated to evaluate its role in explaining future variations in the Realized GARCH. Our results show that among these four volatility measures, VIX is the most informative volatility. Although VIX1D is overemphasized by the literature, its significance in volatility forecasting remains substantial, confirming that risk-neutral volatility measures are generally more informative than physical measures. Finally, we also find that incorporating multiple risk-neutral volatility measures does not improve forecasting performance compared to using a single measure due to overlapping information.

Stock Returns And Inflation In Fiji

The relationship between stock returns and inflation has important implications for monetary policy and stock market investment and has attracted growing attention from scholars over recent years. Despite this, the empirical evidence so far has been inconclusive. The main objective of this paper is to investigate the impact of inflation on stock returns in the developing stock market of Fiji. We examine the impact of inflation on stock market returns within the GARCH and EGARCH modelling framework using monthly data from 2000:02 to 2018:06 and find that inflation negatively affects stock market returns. Our results indicate the importance of ensuring price stability and suggest that stock market investment will not help hedge against inflation in Fiji. The results suggest that Fiji’s stock market is likely to react more negatively to inflation in response to countercyclical monetary policy and emphasize the significance of portfolio diversification.