Articles de revues sur le sujet « Subject GARCH model »

The article shows how the international capital asset pricing model (ICAPM) with Markov regime switching can model the asset returns in the emerging market of Mexico. For most assets, although significant, the international risk premium factor is not subject to regime switching, but the domestic factor is. The probabilities of regimes are correlated with the volatility of assets. A GARCH(1,1) Markov regime switching model offers better adjustment than a non-GARCH. JEL Classification: C58, F36, F65, G12, G15

AbstractThe Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series. The model allows for capturing more flexible dependence patterns than a multivariate GARCH model and also generalizes static copula dependence models. Nonetheless, the model is subject to a number of parameter constraints that ensure positivity of variances and covariance stationarity of the modeled stochastic processes. As such, the resulting distribution of parameters of interest is highly irregular, characterized by skewness, asymmetry, and truncation, hindering the applicability and accuracy of asymptotic inference. In this paper, we propose Bayesian analysis of the CMGARCH model based on Constrained Hamiltonian Monte Carlo (CHMC), which has been shown in other contexts to yield efficient inference on complicated constrained dependence structures. In the CMGARCH context, we contrast CHMC with traditional random-walk sampling used in the previous literature and highlight the benefits of CHMC for applied researchers. We estimate the posterior mean, median and Bayesian confidence intervals for the coefficients of tail dependence. The analysis is performed in an application to a recent portfolio of S&P500 financial asset returns.

The General Autoregressive Conditional Heteroskedastic (GARCH) model and 10 ordinary air quality monitoring stations in the entire air quality control district in Kaohsiung-Pingtung were used in this study. First, the factor analysis results within multivariate statistics were employed to select the main factor that affects air pollution, namely, the photochemical pollution factor. The characteristics of the GARCH model were discussed in terms of asymmetric volatility among the three air pollutants (PM10, NO2, and O3) within the factor. In addition, this study also combined the multiple time series model VARMA to explore changes in the time series of the three air pollutants and to discuss their predictability.The results showed that, although the coefficient of the GARCH model was negative when estimating the variance equation, the conditional variance would always be positive after taking the logarithm. The results also suggested that the GARCH model was quite capable of capturing the asymmetric volatility. In other words, if the condition that pollution factors might be subject to seasonal changes or outliers generated by the human contamination is not considered, the GARCH model had very good ability to verify the results and make predictions, regardless of whether it adopted any of the three risk concepts: normal distribution, t-distribution, and generalized error distribution. For example, under the trend of time series temporal and spatial distribution in various pollution concentrations of photochemical factors, the optimal model VARMA(2,0,0)-GARCH(1,1) selected in this study was used to conduct time series predictability after the verification procedure. After capturing the last 50 entries of data on O3 concentrations in the sequence, the results showed that the predictability correlation (r) was 0.812, the predictability of NO2 was 0.783 and the predictability of PM10 was 0.759. It can be learned from the results that under the sequence of the GARCH model with strong asymmetric volatility, the residual values of these three sequences as white noise were quite evident, and there was also a high degree of correlation in predictability.

The problem of multistage allocation is solved using the Target Date Fund (TDF) strategy subject to a set of restrictions which model the latest regulatory framework of the Mexican pension system. The investment trajectory or glide-path for a representative set of 14 assets of heterogeneous characteristics is studied during a 161 quarters long horizon. The expected returns are estimated by the GARCH(1,1), EGARCH(1,1), GJR-GARCH(1,1) models, and a stationary block bootstrap model is used as a benchmark for comparison. A fixed historical covariance matrix and a multi-period estimation of DCC-GARCH(1,1) are also considered as inputs of the objective function. Forecasts are evaluated through their asymmetric dependencies as quantified by the transfer entropy measure. In general, we find very similar glide-paths so that the overall structure of the investment is maintained and does not rely on the particular forecasting model. However, the GARCH(1,1) under a fixed historical covariance matrix exhibits the highest Sharpe ratio and in this sense represents the best trade-off between wealth and risk. As expected, the initial stages of the obtained glide-paths are initially dominated by risky assets and gradually transition into bonds towards the end oof the trajectory. Overall, the methodology proposed here is computationally efficient and displays the desired properties of a TDF strategy in realistic settings.

The International Grain Council (IGC) circulates two price indices which are the Grain and Oilseeds Index (GOI) and the Grain and Oilseeds Freight Market Index (GOFI). These two indices indicate the respective market prices. The GOI markets are affected by various factors like supply and demand, weather, freight markets, etc. This research article attempts to explore and analyse volatility in GOI and GOFI markets using various GARCH family models, that is Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) analysis. The multivariate Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model (DCC GARCH) is used to find the spillovers between the two markets and thereby explore the effect of GOFI on GOI markets from the year 2013. The research article consists of four sections after introducing the subject namely a literature review, research methodology and models, analysis and conclusions of the study.

Investigating the impact of volatility spillover among various markets has been the subject matter of numerous research. This study investigates the dynamic relationship between the Bombay Stock Exchange index (SENSEX) and the small and medium enterprises (SME) stock index (BSE SME) in India. The study uses univariate autoregressive conditional heteroskedasticity (ARCH)/generalised autoregressive conditional heteroskedasticity (GARCH) models to model the time-varying volatility of the BSE SME market and multivariate BEKK-GARCH analysis to model the volatility of the SENSEX and BSE SME Index considering the existence of some linkages between them. The study is based on the daily stock indices data ranging from 16 August 2012 to 31 March 2021. Furthermore, the study reveals statistically significant internal volatility spillovers in the SME stock market and the cross-volatility transmission between the two indices. It affirms statistically significant volatility and return spillover between the main market index, SENSEX and SME index, BSE SME. The findings of this research have important implications for the diversification process. It provides crucial signals to investors, portfolio managers and policymakers, especially when there has been much impetus and promotion from the Indian government and emerging foreign investments in India’s SMEs in recent years.

A whole host of researchers have modeled volatility as a non-constant stochastic process, based on the principle that volatility follows a stochastic process whose parameters are not directly observable in the market. The objective of this research paper is to empirically investigate the forecasting performance of three most dominant models of this species namely, Hull-White (1988), Heston�s (1993), and Heston-Nandi GARCH (2000) option pricing model. These three models have been collaterally compared and contrasted against Black-Scholes and market for pricing S&P CNX Nifty 50 index option of India. The Hull-White model not only warrants a range of stochastic volatility specifications but also incorporates correlation of volatility of asset return and its price changes. The closed form Heston�s (1993) model explicitly and elaborately communicates non-lognormal distribution of the assets return, leverage effect, and mean-reverting property of volatility. The model of Heston-Nandi, also in closed form, successfully incorporates variance of asset returns as a range of GARCH process. It strongly permits correlation between returns of the spot asset and variance and also technically accepts multiple lags in the dynamics of the GARCH process. To decide, determine, and delineate the effectiveness of stochastic models against the Black-Scholes and market, the current paper adopts a structured approach of relative error price, viz., percentage mean error (PME) and mean absolute percentage error (MAPE). The most turbulent period of the Indian economy � January 1, 2008 to December 31, 2008 — was considered appropriate for testiing the suggested model. It was a testing time for the Indian economy as well as a critical period questioning the sustainability of all financial products/models and challenging their fundamental platform depicted as equity market. How to safeguard investors� faith and at least protect their investments if not multiply returns in the face of such financial hardships remained a burning question for all thinkers and experts on the subject. Data pertaining to the specific period of such drastic disturbance was analysed with the help of the proposed models. After rigorous churning of specific data taken across various models, the Heston model was found to outperform and surpass other models.

Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.

The volatility of equity returns for two beverages traded on the Nigerian stock exchange is the subject of this study. The ARCH effect test demonstrated that the two beverages disprove the claim that there is no ARCH effect. According to the preliminary analysis, both beverages were volatile. CGARCH and EGARCH were chosen as the best volatility models for Guinness Nigeria Plc returns and Nigeria Breweries returns, respectively, based on model selection criteria. The EGARCH model, on the other hand, rejected the idea that Guinness Nigeria Plc’s equity returns respond equally to negative and positive shocks of similar magnitude. This study’s findings suggest that the government should be cautious about how it manages inflation and foreign direct investment because they affect the rising stock price. Financial stability will likely be a more direct and explicit part of the macroeconomic responsibilities of central banks in the coming years.

The paper examines the performance of hedging spot prices in crude oil and natural gas. The subject of the research are spot prices of West Texas Intermediate and Henry Hub. The risk protection is provided by the application of futures contracts of underlying assets. In our analysis three econometric models (OLS, Copula, GARCH) and a naive portfolio are applied to obtain the optimal hedge ratio. Afterwards, the calculated weights for futures are verified for the ability to reduce the spot price risk over twelve months. The success of each model in risk reduction is measured over the test period by a conventional tool and across the models by proper metric. The results of the analysis confirm high level of risk reduction by crude oil across models. On the contrary, the results of hedging in natural gas significantly lag in comparison to crude oil. In addition, the analysis confirms a strong variability over the tested period and models.

Indirect Inference (I‐I) estimation of structural parameters θ requires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parameters β. The estimators of the instrumental parameters will encapsulate the statistical information used for inference about the structural parameters. As such, artificially constraining these parameters may restrict the ability of the auxiliary model to accurately replicate features in the structural data, which may lead to a range of issues, such as a loss of identification. However, in certain situations the parameters β naturally come with a set of q restrictions. Examples include settings where β must be estimated subject to q possibly strict inequality constraints g( β)>0, such as, when I‐I is based on GARCH auxiliary models. In these settings, we propose a novel I‐I approach that uses appropriately modified unconstrained auxiliary statistics, which are simple to compute and always exists. We state the relevant asymptotic theory for this I‐I approach without constraints and show that it can be reinterpreted as a standard implementation of I‐I through a properly modified binding function. Several examples that have featured in the literature illustrate our approach.

The cryptocurrency market offers significant investment opportunities but also entails higher risks as compared to other asset classes. This article aims to analyse the financial risk characteristics of individual cryptocurrencies and of a broad cryptocurrency market portfolio. We construct a portfolio comprising the 20 largest cryptocurrencies, which cover 82.1% of the total cryptocurrency market. The returns are examined for extreme tail risks by the application of Extreme Value Theory. We utilise the GARCH-EVT approach in combination with a novel algorithm to automatically determine the optimal threshold to model the tail distribution. Furthermore, we aggregate the individual market risks with a t-Student Copula to investigate possible diversification effects on a portfolio level. The empirical analysis indicates that all examined cryptocurrencies show high volatility in their price movements, whereby Bitcoin acts as the most stable cryptocurrency. All return distributions are heavy-tailed and subject to extreme tail risks. We find strong, positive intra-market correlations, in particular with the two largest cryptocurrencies Bitcoin and Ethereum. No diversification effect can be achieved by aggregating market risks. On the contrary, a negligibly lower expected return and higher joint extreme returns can be observed. From this analysis, it can be concluded that investments in individual cryptocurrencies as well as in a portfolio show extreme risks of losses. From the investor’s point of view, a possible strategy of risk reduction through portfolio formation within cryptocurrencies is only promising to a limited extent and does not offer a satisfactory solution to significantly reduce the risk within this asset class.

Political instabilities and violent political conflict have in recent years risen substantially throughout the world. Especially in the Middle East and North Africa they have grown to decisive factors permanently challenging the livelihoods of millions. We assess whether and to what extent varying intensities of conflict impact economic activity in Palestine which has been subject to substantial violent political conflict for decades. In particular, we analyse the relationship between various intensity levels of political instability measured by conflict-caused fatalities and uncertainty of weekly food prices in the West Bank between 2004 and 2011 using a GARCH model. We consider four food commodities covering vegetables, fruits and animal products. Banana and milk prices are found not to show clustered volatility while onion and pear prices do. The impact of varying conflict intensities on weekly average prices appears to be modest. This might suggest that effects happen on a temporally and geographically more disaggregated scale.

redicting currency rates is important, for everyone who is trading and trying to build an investment portfolio from a range of crypto currencies. It is not subject to the same restrictions as fiat currencies. In this study, we seek to predict the exchange rate of BIT-COIN against the US dollar. The short-term data (365 observations) is processed using the LSTM model as one of the neural network models. Modeling is conducted by training a sample size of 67%, taking into account sharp fluctuations in the price of trade and a certain level of market efficiency. The GARCH model is used to select appropriate historical periods for how the LSTM model works and to test proficiency at the weak, semi-strong, and strong levels. The data series obtained from the website (Investing.com) have been processed. The researchers have found that the performance of the neural network improves as the EPOCH value increases with a training (research) period of 50 days before, which is consistent with the results of the proficiency test at the weak level. It agrees with the results of the sufficiency test at the weak level, which indicates that in the case under study (the Bitcoin market is effective at the weak level). It is advised that crypto-currency investors rely more on the historical trend of the price of the currency than on its current price, taking advantage of the artificial neural network model (LSTM) in dealing with little data of high volatility.

Despite a direct ban on charging interest, interest-based benchmarks are used as a pricing reference by a majority of Islamic banks, due in part to the absence of stable and widely- published alternatives. Benchmarking interest rate exposes Islamic banks to the problems of conventional banks, particularly the interest rate risk. Against this backdrop, the present study empirically examines the dynamic linkage between the interest rate volatility and the financing of Islamic banks. The empirical analysis is carried using evidence from the Islamic banking industry of Pakistan during the time period 2006–2020. The multivariate Johansen and Jusiles Co-integration test and Vector Error Correction Model (VECM) are used as the baseline econometric models. Moreover, the DCC-GARCH model is employed for robustness and ensuring the consistency of results. The results indicate that a significant long-term and short-term relationship exists between the interest rate volatility and the financing of Islamic banking industry providing significant evidence for co-movements and convergence. These findings suggest that paradoxical as it may seem, the financing of Islamic banks operating within a dual banking system is subject to interest rate risk, mainly due to benchmarking interest rate, which in-turn makes Islamic banks vulnerable to the rate of return risk and withdrawal risk. Moreover, corporate financing, in particular, is more vulnerable to interest rate risk.

The emergence of Bitcoin in 2009 has received considerable attention surrounding the validity of cryptocurrencies as a viable and, in some jurisdictions, a legal currency alternative. Despite widespread concern that these cryptocurrencies are fostering the environment within which a substantial bubble can occur, it is important to analyze whether these new assets are behaving similarly to major international currencies. This paper investigates the effects of international monetary policy changes on bitcoin returns using a GARCH (1.1) estimation model. The results indicate that monetary policy decisions based on interest rates taken by the Federal Open Market Committee in the United States significantly impact upon bitcoin returns. After controlling for international effects, we find significant evidence of volatility effects driven by United States, European Union, United Kingdom and Japanese quantitative easing announcements. These results show that, despite its nature and ideals, bitcoin seems to be subject to the same economic factors as traditional fiat currencies, and is not entirely unaffected by government policies. This result has implications for investors using bitcoin as a hedging or diversification tool. In addition, we contribute to the existing debate regarding the classification of bitcoin as an asset class, by illustrating that bitcoin volatility exhibits various reactions that bear resemblance to both currency pairs and store-of-value assets.

Renewable energy sources play a key role in decarbonizing the economy of the European Union (EU) and the world. The aim of this research is to present the development of the renewable energy sources (RES) sector in the European Union (EU), with particular emphasis on sustainable development. The EU guidelines, requirements, and directives were analyzed in order to meet the provisions of the energy policy to ensure energy and climate security. The potential of the RES was studied in the EU countries, and the possibility of its use in cogeneration with the use of local renewable resources. The results are presented in tabular, graphic, and descriptive forms. The results are presented based on the extensive literature on the subject and data from Eurostat. The data covered 2004–2019. We used different methods to evaluate the changes in the RES in the EU countries. First, we compiled descriptive statistics; second, we used the Augmented Dickey–Fuller test (ADF test); and, finally, we used the Generalized Autoregressive Conditional Heteroscedasticity model (GARCH model). Our analysis found that the EU increased the share of RES. The biggest share of energy from renewable energy sources was found in 2019 in Iceland (78%), Norway (74%), and Sweden (56%). The biggest increase in the share of RES in 2004–19 was found in Malta (8322%), Great Britain (1126%), and Luxemburg (784%). The results demonstrate the development of RES in the EU countries. However, not all countries achieved the planned goal in 2019.

The objective of the current paper is to explore the co-movements between domestic equity sectors in the Egyptian Exchange (EGX), using the dynamic conditional correlation (DCC) model, and to examine the time-varying causal links between the exchange rate volatility (EXVOL) and sector volatility (SVOL) using the bootstrap Granger non-causality tests in a bivariate VAR, where conditional volatility series are extracted from GARCH(1,1) model. We employ weekly data. Results show that all estimated DCCs are positive with a clear heterogeneity between the sector pairs. They do not exhibit stable correlation pattern for a prolong time, implying that DCC estimates change in response to price increment shocks to each sector in the pair. Hence, the assumption of static inter-sectoral correlations between domestic sector indexes is invalid when forming and periodically re-balancing portfolios. The global financial crash and the political instability in early 2011 have significantly increased the level of DCCs for four and ten out of fifteen pairs, respectively. Thus, the recent political turmoil in Egypt has widely affect diversification opportunities in the EGX whereas the global financial crash has not. The volatility transmission between SVOL and EXVOL is subject to structural breaks. The bootstrap rolling window estimations show that the casual relationship between SVOL and EXVOL varies across time. These findings would be of great importance to market participants in their hedging and investment decisions since investors and firms are more concerned with industrial sector exposure estimates.

COVID-19's negative externalities disrupted the supply of several commodities. Meanwhile, to combat the spread of COVID-19, various countries have implemented a lockdown policy, which impacts the economy's balance. Consumers' risks are increasing due to economic changes caused by the COVID-19 pandemic, which affects the higher level of volatility of various agricultural commodities in Indonesia, particularly strategic commodities. Thus, this research was designed to examine the experience shock of strategic food consumers during Covid-19 pandemic. The ARCH/GARCH model was used to describe the strategic food price movement pattern using a set of weekly consumer price data from March 2020 until August 2021. This study found price volatility of strategic food commodities is classified into three categories. For starters, the extreme volatility of commodities such as garlic and cayenne pepper indicates that future buyers will face more significant uncertainty and risk. Furthermore, these commodities will generate fluctuating price swings in the future, resulting in greater experience shock for consumers. Second, commodities with high price volatility, such as rice, chicken meat, eggs, shallots, and sugar, signal that these commodities will subject consumers to a huge shock due to their high price volatility. Third, minimal volatility, such as that found in meat, red chilli, and cooking oil, indicates that customers will face less uncertainty in the future.

Energy is considered as a commodity nowadays and continuous access along with price stability is of vital importance for every economic agent worldwide. The aim of the current review paper is to present in detail the two dominant hedging strategies relative to energy portfolios, the Minimum-Variance hedge ratio and the expected utility maximization methodology. The Minimum-Variance hedge ratio approach is by far the most popular in literature as it is less time consuming and computationally demanding; nevertheless by applying the appropriate multivariate model Garch family volatility model, it can provide a very reliable estimation of the optimal hedge ratio. However, this becomes possible at the cost of a rather restrictive assumption for infinite hedger’s risk aversion. Within an uncertain worldwide economic climate and a highly volatile energy market, energy producers, retailers and consumers had to become more adaptive and develop the necessary energy risk management and optimal hedging strategies. The estimation gap of an optimal hedge ratio that would be subject to the investor’s risk preferences through time is filled by the relatively more complex and sophisticated expected utility maximization methodology. Nevertheless, if hedgers share infinite risk aversion or if alternatively the expected futures price is approximately zero the two methodologies become equivalent. The current review shows that when evidence from the energy market during periods of extremely volatile economic climate is considered, both hypotheses can be violated, hence it becomes reasonable that especially for extended hedging horizons it would be wise for potential hedgers to take into consideration both methodologies in order to build a successful and profitable hedging strategy.

Purpose – The purpose of this paper is to test the long-term effects of price and volume with the help of Downward Sloping Demand Curve (DSDC) hypothesis, and also the short-term price and volume effects with the help of Price Pressure Hypothesis (PPH) for the index revisions on the S&P CNX Nifty 50 index. Design/methodology/approach – In order to report the long-term and short-term effects, the current study reviews two testable hypotheses, namely, DSDC hypothesis and PPH. The study has used the event study approach by including GARCH (1, 1) conditional variance in the market model. Findings – The results report that, the added stocks experienced a significant increase in price and volume on the effective date; whereas the deleted stocks experienced significant volume levels and insignificant price levels on the effective date. Accordingly, the study finds support in favor of PPH. Research limitations/implications – The study could not find evidence to support the most studied DSDC hypothesis. Practical implications – Index reorganization presumably affects the fund managers, domestic as well as international investors. As a result, studying the effect of index changes is a subject of attention to academicians and investors alike. Originality/value – The study contributes to the body of knowledge on index inclusion and exclusion effects by providing Indian evidence on long-term and short-term price and volume effects, and also documenting contrary results to the previous Indian and global research works.

Purpose – The purpose of this paper is to examine the price discovery and volatility spillovers in spot and futures prices of four currencies (namely, USD/INR, EURO/INR, GBP/INR and JPY/INR) and between futures prices of both stock exchanges namely, Multi-Commodity Stock Exchange (MCX-SX) and National Stock Exchange (NSE) in India. Design/methodology/approach – The study applies cointegration test of Johansen’s along with VECM to investigate the price discovery. GARCH-BEKK model is used to examine the volatility spillover between spot and futures and between futures prices. The other two models namely, constant conditional correlation and dynamic conditional correlation are used to demonstrate the constant and time-varying correlations. In order to confirm the volatility spillover results, the study also applies test of directional spillovers suggested by Diebold and Yilmaz (2009, 2012). Findings – The results of the study show that there is long-term equilibrium relationship between spot and futures and between futures markets. Between futures and spot prices, futures price appears to lead the spot price in the short-run. Volatility spillover results indicate that the movement of volatility spillover takes place from futures to spot in the short-run while spot to futures found in the long-run. However, the results of between futures markets exhibit the dominance of MCX-SX over NSE in terms of volatility spillovers. By and large, the findings of the study indicate the important role of futures market in price discovery as well as volatility spillovers in India’s currency market. Practical implications – The results highlight the role of futures market in the information transmission process as it appears to assimilate new information quicker than spot market. Hence, policymakers in emerging markets such as India should focus on the development of necessary institutional and fiscal architecture, as well as regulatory reforms, so that the currency market trading platforms can achieve greater liquidity and efficiency. Originality/value – Due to recent development of currency futures market, there is dearth of literature on this subject. With the apparent importance of currency market in recent time, this study attempts to study the efficient behavior of currency market by way of examining the price discovery and volatility spillovers between spot and futures and between futures prices of four currencies traded on two platforms. The study has strong implications for India’s stock market especially at the time when its currency is under great strain owing to the adverse impact of global financial crisis.

In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from January 1954 to September 2019, the properties of the models were assessed both within- and out-of-sample. We found strong evidence in favour of modelling both GARCH effects and non-Gaussianity. Accounting for these properties improves within-sample performance as well as point and density forecasts.

The subject of this research is to analyze and test the modified GARCH methodology in terms of quantifying the impact of inflation rates, interest rates on government bonds, reference interest rates, and exchange rates on daily rates of return on investment activities in the observed financial markets of North America, Serbia and Croatia. The aim of the research, i.e. a special focus in the research, is to compare the obtained results between the developed financial markets and the financial markets of developing countries, as well as to test the modified GARCH methodology in the observed financial markets. The key indicators in the research, presumed to affect the daily return rates, were the following: inflation rate, interest rates on government bonds, reference interest rate and exchange rate. The time period covered by the research is from 2005 to 2017, where the width of the research time horizon allows testing the modified GARCH methodology in the periods before, during and after the global financial crisis. In addition to the use of modified GARCH econometric models, the research methodology includes the use of AIC, SIC and HQC (Akaike, Schwarz and Hannan-Quinn) criteria for selecting the best models, as well as the appropriate tests that are suitable for and/or adapted to the specific characteristics of financial markets of both developed and developing countries. The research results confirm the role and importance of the modified GARCH methodology for effective investment risk quantification in developed financial markets versus the financial markets of developing countries. In this sense, the obtained research results will be useful to both the academic community and the professional public in the context of investment decision making.

As a new species in the financial ecosystem, internet finance has significantly impacted traditional finance and has improved the diversity and ended the long-term stability of the financial ecosystem. From the perspective of the interaction between the ecological subjects of the Internet and traditional finance, this study examines the influence of internet finance on the sustainability of the financial ecosystem in China. We tested the dynamic correlation and risk transmission at the volatility level between the ecological subjects of internet finance and the banking, securities, and insurance industries by establishing a dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (GARCH) model of Baba, Engle, Kraft, and Kroner (DCC-GARCH-BEKK). The result indicates a positive dynamic correlation between internet finance and traditional finance almost all of the time. The introduction of internet finance has changed the risk transmission effect among the ecological subjects of traditional finance. Based on empirical findings, this study provides suggestions to promote the sustainable development of internet finance and the whole financial ecosystem. Our research not only has strong practical significance but also contributes significantly to the literature on internet finance and sustainable development.

This research study analyses the role of size effect in detecting the pricing of risk, various volatility dynamics, and economic exposure of firm returns on the Pakistani stock market by employing monthly data for the period from 1998 to 2018. Three generalized autoregressive conditional heteroskedasticity models were applied: GARCH(1,1) for capturing different volatility dynamics, GARCH-M for pricing of risk, and EGARCH for asymmetric and leverage effect. The findings of the study are as follows: Firstly, the authors untie that pricing of risk is subject to considerable variations with respect to firm size. Secondly, in the process of detecting whether the firm size matters in the case of asymmetry and leverage effect, they find that it is indeed the case. Thirdly, size effect plays a substantial role in determining various volatility dynamics. Finally, they uncover that economic factors affect stock returns differently based on firm size, signifying the role of size effect.

In many competitive electricity markets around the world, the dynamic behavior of hourly electricity prices is subject to significant uncertainty and volatility due to electricity demand, availability of generation sources, fuel costs, and power plant availability. This work is devoted to describing and comparing the dynamics of electricity prices for some markets in Europe, selecting the five countries representing the largest economies in Western Europe (France, Germany, Italy, Spain, and the United Kingdom). Additionally, Denmark is included in the study to assess whether the size of the country is a determinant of price behavior. The six datasets of hourly price series, which exhibits a strong daily seasonality, are modelled using the most relevant well-known statistical models for time series analysis: ARIMA models and different versions of GARCH models. The comparison of the estimated models’ parameters, the analysis of outliers’ rate of appearance and the evaluation of out-of-sample one-day-ahead forecast let us draw some insightful similarities and dissimilarities between the analyzed countries.

The article presents an analysis of the impact of foreign currency dynamics on the fundamentals (basic indices of the economic performance) of the Czech Republic, Hungary and Poland during the financial crisis of 2007/2008 and its aftermath until 2017. The subject of the analysis are three currencies: the US dollar, the euro and the Swiss franc. The assessment of their impact on the fundamentals of the three above-mentioned economies is based on the joint volatilities of bond spreads and currencies. A series of copula-GARCH models was estimated. The research demonstrates that the impact of foreign currencies was the strongest in the case of Poland and Hungary, as these two countries were more dependent on loans in foreign currencies than the Czech Republic. Another finding shows that the impact decreased significantly in Hungary after its government introduced loan conversion.

One of the most important economic factors in food choice is the price. Food dynamics' value is a subject of controversies and opinions, especially price issues, and sensitivity is often peculiar to seasons and market forces. Price dynamics have the potential to introduce and change consumptions, thus affecting household welfare. This study examined the dynamics of food price volatility and households' welfare in Nigeria from 1990: Q1 to 2019: Q4. We sourced the study data from the Food and Agriculture Organization (FAO) and the World Bank (WB). We estimated the quadratic trend equation, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Auto-Regressive Distributed Lag (ARDL) models. Food prices and depth of food deficit had a significant short-run impact on the households' welfare. Policymakers should focus on the short-term benefits while formulating policies aimed at households' welfare because policies aimed at the household level are impactful in the short-run compared to the long-run.

Subject. The article addresses the stable functioning of the financial market and its protection against financial crises as the main indicator of financial system’s stability. It considers the history of the issue, enabling to conclude that financial markets are built mainly on the principle of unstable equilibrium in contrast to the more stable equilibrium underlying the commodity markets and industrial production. Objectives. The article attempts to compare well-known stochastic models with dynamic and chaotic systems. Methods. The study employs stochastic modeling (autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) models), investigates methods and approaches to solving some types of differential stochastic equations, in particular, the Ito and Fokker-Planck-Kolmogorov equations. Results. Financial markets are considered within the theory of dynamic systems as an example of a non-linear system. It is extremely difficult to predict the behavior of such a system, precisely because of the non-linearity, which is reduced to random and chaotic processes. Through mathematical transformations, the paper shows that solutions are reduced to multidimensional stochastic volatility models. Conclusions. Stochastic volatility models, despite their relative theoretical elaboration and practical applicability, can lead to dynamic chaos, when there is a vector of asset return, the conditional covariance matrix of which changes over time.

Text analysis with machine learning support can be implemented for studying experts’ relations to the Bank of Russia. To reach macroeconomic goals, the communication policy of the bank must be predictable and trustworthy. Surveys addressing this theme are still insufficient compare to the theoretical studies on the subject of other bank tools. The goal of this research is to analyze the perception of uncertainty by economic agents. For that purpose, we built an uncertainty indicator based on news sources from the Internet and on textual analysis. The dynamics of the indicator reflect unexpected statements of the Bank of Russia and events affecting monetary policy. Financial theory links monetary policy and stock prices, so we used this fact to examine the impact of the uncertainty indicator on the MOEX and RTS indices. We tested the hypothesis that our indicator is significant in GARCH models for chosen financial series. We found out several specifications in which our indicator is significant. Among the specifications considered, the uncertainty indicator contributes the most to explaining variances of the RTS index. The obtained uncertainty indicator can be used for forecasting of different macroeconomic variables.

Abstract Background In September 2020, China put forward the goal of carbon neutrality by 2060, which is of great and far-reaching significance for coping with climate change, achieving sustainable development and promoting the transformation of energy structure. In this context, the carbon market has become an important policy tool to realize China's vision of carbon neutralization. As can be seen from the stock market, the carbon neutralization industrial chain is evolving, and green and low-carbon has become the main investment trend. Therefore, in the context of carbon neutrality, whether there is a connection between China's carbon market and stock market, whether there is spillover effect between them, and what will be based on investors' emotional behavior are the main problems to be solved in this study. Subjects and Methods This paper takes China's carbon market, carbon neutralization concept stock market and thermal power stock market as the main research objects; This paper uses the price index and VaR BEKK GARCH model from January 1, 2019 to December 31, 2020 to study the interaction and spillover effect between the above markets. It also reveals the reasons from the perspective of investment psychology. In order to better study the changes of investors' emotional perception and behavior, this study uses the self rating Depression Scale (SDS) system William W K. Zung compiled the self rating scale in 1965 to measure the severity of depression and its changes in treatment. In 1972, Zung added the corresponding examiner's book and changed the self-evaluation to other evaluation, which is called Depression Status Inventory (DSI). The assessment time span is the latest week. Content: SDS and DSI are composed of 20 declarative sentences and corresponding question entries respectively. Each item is equivalent to a relevant symptom, which is scored on a scale of 1-4. 20 items reflect depression, and four groups of specific symptoms: 1 Psychotic affective symptoms, including depression and crying; 2. Somatic disorders, including eight items: daytime differences in mood, sleep disorders, anorexia, decreased libido, weight loss, constipation, tachycardia and fatigue; 3. Psychomotor disorders, including psychomotor retardation and agitation; 4. Psychological disorders of depression include eight items: confusion of thinking, hopelessness, irritability, indecision, self depreciation, emptiness, repeated thinking, suicide and dissatisfaction. From the scoring method: each item is scored according to four levels: 1, 2, 3 and 4. Ask the subject to read each statement carefully, or the examiner to ask questions one by one, and circle 1 (from none or occasionally), or 2 (sometimes), or 3 (often), or 4 (always) according to the time and frequency most suitable for the subject's situation. Among the 20 items, 10 items (items 2, 5, 6, 11, 12, 14, 16, 17, 18 and 20) are stated with positive words, which are scored in reverse order, and the remaining 10 items are stated with negative words, which are scored in the order of 1-4 above. The depression severity index assessed by SDS and DSI is calculated according to the following formula: depression severity index - cumulative score of each item / 80 (maximum total score). The index range is 0.25-1.0. The higher the index, the heavier the degree of depression. Results 2019 coronavirus disease has a significant impact on the relationship between carbon market and stock market. (1) Affected by 2019 coronavirus disease, CTI first affects cni300 and then TPI; Investors' expectations of earnings are distorted by the extension of the current trend. (2) The two-way average spillover effect between TPI and Guoxin 300 in 2019 has become the one-way average spillover effect in 2020. Investors' psychological bias and thinking shortcuts make them choose to invest in familiar fields. These areas cannot spread investment risks, but will damage their long-term wealth. (3) The amplitudes of cni300 and TPI will change dramatically with historical fluctuations, which confirms that most people's judgment on the profit and loss mentioned in the prospect theory is usually determined according to the reference point. The results showed that impulsive investment was negatively correlated with economic support ability (P < 0.01), and its internal consistency was satisfactory: odd and even items split half correlation: 0.73 (1973) and 0.92. There was a high and moderate correlation between the scores of “d” subscale of MMPI. Zung et al. Also compared the scores of SDS and dis with CG1 score and proposed that those with SDS and dis score index below 0.5 were non depressed; 0.50-0.59 is mild to mild depression; 0.60-0.69 is moderate to severe depression; more than 0.70 is severe depression. Conclusion This shows the confidence of Chinese investors in the national sustainable development initiative. Carbon market performance has become an important reference for investing in carbon neutral concept stocks. Therefore, the government should constantly promote the innovation of carbon trading system while stabilizing the expected return on green and low-carbon investment. In addition, the government should take multiple measures to send sufficient and accurate signals on specific measures to promote carbon neutralization, eliminate unreasonable investment to the greatest extent, and guide the industrial layout through the capital market. Acknowledgements This manuscript was supported by the National Natural Science Foundation of China (71473230) (71803180), and the Philosophy and Social Sciences Planning Project of the Ministry of Education in China (18YJCZH140) (17YJCZH048).

Fluctuations in financial markets have always been the subject of concern for academicians and practioners. Wide price fluctuations are a daily occurrence on the world's stock markets as investors react to economic, business, and political events. The soaring stock prices around the globe have taken valuations of stocks to unreachable heights. The present study is an attempt to analyse the volatility present in stock markets of Asia Pacific region and other developed countries. Data for the time period 2005-2013 has been collected to analyse the same. Coefficient of variation, Ttest and GARCH (1, 1) model has been used to investigate the variations in the stock markets. The results support the fact that there is no significant difference in volatility between stock exchanges within specific except between Germany stock market and US stock market. GARCH (1,1) provides that US stock market is the most volatile followed by India.

The most common approach in fitting option pricing models to market data is first to make an assumption about the underlying asset’s returns process and then develop an option pricing model for that process that is tested against market option prices. The returns process is estimated from historical data, option values are computed, and then compared against a cross-section of prices from the options market. Unfortunately, this often does not work well, and plainly it is inefficient in its use of the data. However, efforts to combine returns data from the asset market and prices from the options market into a single estimation have also not had much success. In this article, Chang, Cheng, and Fuh propose a new procedure to combine data from both markets in the estimation, in which options are assumed to be subject to random pricing noise relative to model values. The additional slack gives the estimator better ability to match prices in both markets. The article contrasts the performance of the full model approach with an approach that only uses stock prices or options prices to fit an option pricing model based on an underlying GARCH process. The value of the combined approach is demonstrated both theoretically as an asymptotic result in the model and also in a Monte Carlo simulation.

AbstractConvertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides, embedded options, such as conversion, call, and put provisions are often restricted to certain periods, may vary over time, and are subject to additional path-dependent features of the state variables. Moreover, the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties. In this paper, we propose DeepPricing, a novel data-driven convertible bonds pricing model, which is inspired by the recent success of generative adversarial networks (GAN), to address the above challenges. The method introduces a new financial time-series generative adversarial networks (FinGAN), which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions, the long-range dependence, and the asymmetry structure etc., and then transit to its risk-neutral distribution. Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models. The experiments on the Chinese convertible bond market demonstrate the effectiveness of DeepPricing model. Compared with the convertible bond market prices, our model has a better convertible bonds pricing performance than both model-driven models, i.e. Black-Scholes, the constant elasticity of variance, GARCH, and the state-of-the-art GAN-based models, i.e. FinGAN-MLP, FinGAN-LSTM. Moreover, our model has a better fitting capacity for higher-volatility convertible bonds and the overall convertible bond market implied volatility smirk, especially for equity-liked convertible bonds, convertible bonds trading in the bull market, and out-of-the-money convertible bonds. Furthermore, the Long-Short and Long-Only investment strategies based on our model earn a significant annualized return with 41.16% and 31.06%, respectively, for the equally-weighted portfolio during the sample period.

AbstractConvertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides, embedded options, such as conversion, call, and put provisions are often restricted to certain periods, may vary over time, and are subject to additional path-dependent features of the state variables. Moreover, the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties. In this paper, we propose DeepPricing, a novel data-driven convertible bonds pricing model, which is inspired by the recent success of generative adversarial networks (GAN), to address the above challenges. The method introduces a new financial time-series generative adversarial networks (FinGAN), which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions, the long-range dependence, and the asymmetry structure etc., and then transit to its risk-neutral distribution. Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models. The experiments on the Chinese convertible bond market demonstrate the effectiveness of DeepPricing model. Compared with the convertible bond market prices, our model has a better convertible bonds pricing performance than both model-driven models, i.e. Black-Scholes, the constant elasticity of variance, GARCH, and the state-of-the-art GAN-based models, i.e. FinGAN-MLP, FinGAN-LSTM. Moreover, our model has a better fitting capacity for higher-volatility convertible bonds and the overall convertible bond market implied volatility smirk, especially for equity-liked convertible bonds, convertible bonds trading in the bull market, and out-of-the-money convertible bonds. Furthermore, the Long-Short and Long-Only investment strategies based on our model earn a significant annualized return with 41.16% and 31.06%, respectively, for the equally-weighted portfolio during the sample period.

Abstract Under the European Union’s Solvency II regulations, insurance firms are required to use a one-year VaR (Value at Risk) approach. This involves a one-year projection of the balance sheet and requires sufficient capital to be solvent in 99.5% of outcomes. The Solvency II Internal Model risk calibrations require annual changes in market indices/term structure for the estimation of risk distribution for each of the Internal Model risk drivers. This presents a significant challenge for calibrators in terms of: Robustness of the calibration that is relevant to the current market regimes and at the same time able to represent the historically observed worst crisis; Stability of the calibration model year on year with arrival of new information. The above points need careful consideration to avoid credibility issues with the Solvency Capital Requirement (SCR) calculation, in that the results are subject to high levels of uncertainty. For market risks, common industry practice to compensate for the limited number of historic annual data points is to use overlapping annual changes. Overlapping changes are dependent on each other, and this dependence can cause issues in estimation, statistical testing, and communication of uncertainty levels around risk calibrations. This paper discusses the issues with the use of overlapping data when producing risk calibrations for an Internal Model. A comparison of the overlapping data approach with the alternative non-overlapping data approach is presented. A comparison is made of the bias and mean squared error of the first four cumulants under four different statistical models. For some statistical models it is found that overlapping data can be used with bias corrections to obtain similarly unbiased results as non-overlapping data, but with significantly lower mean squared errors. For more complex statistical models (e.g. GARCH) it is found that published bias corrections for non-overlapping and overlapping datasets do not result in unbiased cumulant estimates and/or lead to increased variance of the process. In order to test the goodness of fit of probability distributions to the datasets, it is common to use statistical tests. Most of these tests do not function when using overlapping data, as overlapping data breach the independence assumption underlying most statistical tests. We present and test an adjustment to one of the statistical tests (the Kolmogorov Smirnov goodness-of-fit test) to allow for overlapping data. Finally, we explore the methods of converting “high”-frequency (e.g. monthly data) to “low”-frequency data (e.g. annual data). This is an alternative methodology to using overlapping data, and the approach of fitting a statistical model to monthly data and then using the monthly model aggregated over 12 time steps to model annual returns is explored. There are a number of methods available for this approach. We explore two of the widely used approaches for aggregating the time series.

Volatility and the trade-off between risk and return in stock markets is an important subject in financial theory which play significant role in investment decision making, portfolio selection, options pricing, financial stability, hedging and pair trading strategy among others. This study estimates stock return volatility and analyses the risk-return trade-off in the Nigerian stock market using symmetric GARCH (1,1)-in-mean, asymmetric CGARCH (1,1)-in-mean and EGARCH (1,1)-in-mean models with Generalized Error Distribution and Student-t innovation. Data on daily closing all share prices of the Nigerian stock exchange for the period 2nd January, 1998 to 9th January, 2018 are utilised. The data is further divided into three sub-periods of pre-crisis, global financial crisis and post crisis periods to allow volatility behaviour and the risk-return trade-off to be investigated across the sub-periods. Results showed evidence of volatility clustering, leptokurtosis, high persistence of shocks to volatility and asymmetry without leverage effects across the study periods. The persistence of shocks to volatility increased during the global financial crisis period with delayed reactions of volatility to market changes. However, by incorporating the exogenous breaks into the volatility models for the full study period, the shock persistence drastically reduced with faster reactions of volatility to market changes. The results of this study also found supportive evidence for a significant positive risk-return relationship in Nigerian stock market across various study sub-periods and model specifications meaning that investors in Nigerian stock market should be compensated for holding risky assets. The empirical findings of this study further suggest that the recent global financial crisis have not altered the market dynamics to distort the risk-return trade-off in Nigerian stock market indicating that expected returns are not driven by changes in the stock market volatility. The study provides some policy recommendations for investors and policy makers in the Nigerian stock market.

AbstractOver the past three decades, there has been an increasing focus on the subject of global tourism in Iran’s economy. This article examines the most important economic factors affecting this industry in this country, especially economic policy uncertainty. For this purpose, three models specify the number of tourists entering the country as a dependent variable and Consumer Price Index, Tehran Exchange Price Index, market exchange rate, semi-annual dummy variable, and exports as explanatory variables. To investigate the uncertainty of the government’s economic policies, three variables liquidity fluctuations, tax revenue fluctuations, and government expenditures fluctuations have been added along with the above variables. To obtain the fluctuations, the GARCH function is used then the relations are estimated by the GMM method. The estimation of models using monthly data from March 2011 to August 2018 shows that explanatory variables are significant. The results indicate that economic policy uncertainty has negatively affected the arrival of the tourist. An increase in exchange rate, consumer price index, exports, and stock market price index have a positive effect on the arrival of tourists. Therefore, due to inbound tourism sensitivity to shocks, the growth and survival of tourism depend on economic and political stability.

In this study, it was analysed to determine whether the coronavirus, which became a global epidemic by affecting the whole world in a short time, caused any changes in the volatility and liquidity of stock market indices in the USA, Germany, China, Japan, Egypt, and Turkey. In this context, the effects of the coronavirus epidemic on DOW30 Index in USA, DAX Index in Germany, SSE Composite Shanghai Index in China, NIKKEI 225 Index in Japan, EGX30 Index in Egypt and BIST100 Index in Turkey were investigated. The results and estimations of the study were limited to the relevant countries, and this was the limitation of the study. Selected countries for the analysis were determined by their locational and financial market properties among developed and developing countries which were the most representative ones. The date of the first case for each country announced by WHO was taken as a basis date. A data set was prepared for the period from the first case had been seen to 18 November 2020 for each country. In order to determine the pre-pandemic and post-pandemic differentiation, a pre-pandemic period data set was created as well as the same amount of data. Thus, it was tried to determine whether there was a differentiation for the period before and after the pandemic. The return and liquidity series of the indices were estimated with GARCH(1,1), one of the conditional variance models, and it was observed that there were changes in the volatility and liquidity of the relevant stock market indices after COVID-19. In addition, volatility clusters were observed. Return series of all country stock market indices which were the subject of the research had determined to have thick tail and skewness features like classical financial time series.

IntroductionThe death of Chris Cornell in the spring of 2017 shook me. As the lead singer of Soundgarden and a pioneer of early 1990s grunge music, his voice revealed an unbridled pain and joy backed up by the raw, guitar-driven rock emanating from the Seattle, Washington music scene. I remember thinking, there’s only one left, referring to Eddie Vedder, lead singer for Pearl Jam, and lone survivor of the four seminal grunge bands that rose to fame in the early 1990s whose lead singers passed away much too soon. Alice in Chains singer Layne Staley died in 2002 at the age of 35, and Nirvana front man Kurt Cobain’s death in 1994 had resonated around the globe. I thought about when Cornell and Staley said goodbye to their friend Andy Wood, lead singer of Mother Love Bone, after he overdosed on heroine in 1990. Wood’s untimely death at the age of 24, only days before his band’s debut album release, shook the close-knit Seattle music scene and remained a source of angst and inspiration for a genre of music that shaped youth culture of the 1990s.When grunge first exploded on the pop culture scene, I was a college student flailing around in pursuit of an English degree I had less passion for than I did for music. I grew up listening to The Beatles and Prince; Led Zeppelin and Miles Davis; David Bowie and Willie Nelson, along with a litany of other artists and musicians crafting the kind of meaningful music I responded to. I didn’t just listen to music, I devoured stories about the musicians, their often hedonistic lifestyles; their processes and epiphanies. The music spoke to my being in the world more than the promise of any college degree. I ran with friends who shared this love of music, often turning me on to new bands or suggesting some obscure song from the past to track down. I picked up my first guitar when John Lennon died on the eve of my eleventh birthday and have played for the past 37 years. I rely on music to relocate my sense of self. Rhythm and melody play out like characters in my life, colluding to make me feel something apart from the mundane, moving me from within. So, when I took notice of grunge music in the fall of 1991, it was love at first listen. As a pop cultural phenomenon, grunge ruptured the music and fashion industries caught off guard by its sudden commercial appeal while the media struggled to galvanize its relevance. As a subculture, grunge rallied around a set of attitudes and values that set the movement apart from mainstream (Latysheva). The grunge sound drew from the nihilism of punk and the head banging gospel of heavy metal, tinged with the swagger of 1970s FM rock running counter to the sleek production of pop radio and hair metal bands. Grunge artists wrote emotionally-laden songs that spoke to a particular generation of youth who identified with lyrics about isolation, anger, and death. Grunge set off new fashion trends in favor of dressing down and sporting the latest in second-hand, thrift store apparel, ripping away the Reagan-era starched white-collared working-class aesthetic of the 1980’s corporate culture. Like their punk forbearers who railed against the status quo and the trappings of success incurred through the mass appeal of their art, Kurt Cobain, Eddie Vedder, and the rest of the grunge cohort often wrestled with the momentum of their success. Fortunes rained down and the media ordained them rock stars.This auto-ethnography revisits some of the cultural impacts of grunge during its rise to cultural relevance and includes my own reflexive interpretation positioned as a fan of grunge music. I use a particular auto-ethnographic orientation called “interpretive-humanistic autoethnography” (Manning and Adams 192) where, along with archival research (i.e. media articles and journal articles), I will use my own reflexive voice to interpret and describe my personal experiences as a fan of grunge music during its peak of popularity from 1991 up to the death of Cobain in 1994. It is a methodology that works to bridge the personal and popular where “the individual story leaves traces of at least one path through a shifting, transforming, and disappearing cultural landscape” (Neumann 183). Grunge RootsThere are many conflicting stories as to when the word “grunge” was first used to describe the sound of a particular style of alternative music seeping from the dank basements and shoddy rehearsal spaces in towns like Olympia, Aberdeen, and Seattle. Lester Bangs, the preeminent cultural writer and critic of all things punk, pop, and rock in the 1970s was said to have used the word at one time (Yarm), and several musicians lay claim to their use of the word in the 1980s. But it was a small Seattle record label founded in 1988 called Sub Pop Records that first included grunge in their marketing materials to describe “the grittiness of the music and the energy” (Yarm 195).This particular sound grew out of the Pacific Northwest blue-collar environment of logging towns, coastal fisheries, and airplane manufacturing. Seattle’s alternative music scene unfolded as a community of musicians responding to the tucked away isolation of their musty surroundings, apart from the outside world, free to submerge themselves in their own cultural milieu of rock music, rain, and youthful rebellion.Where Seattle stood as a major metropolitan city soaked in rainclouds for much of the year, I was soaking up the desert sun in a rural college town when grunge first leapt into the mainstream. Cattle ranches and cotton fields spread across the open plains of West Texas, painted with pickup trucks, starched Wrangler Jeans, and cowboy hats. This was not my world. I’d arrived the year prior from Houston, Texas, an urban sprawl of four million people, but I found the wide-open landscape a welcome change from the concrete jungle of the big city. Along with cowboy boots and western shirts came country music, and lots of it. Garth Brooks, Reba McEntire, George Straight; some of the voices that captured the lifestyle of my small rural town, twangy guitars and fiddles blaring on local radio. While popular country artists recorded for behemoth record labels like Warner Brothers and Sony, the tiny Sub Pop Records championed the grunge sound coming out of the Seattle music scene. Sub Pop became a playground for those who cared about their music and little else. The label cultivated an early following through their Sub Pop Singles Club, mailing seven-inch records to subscribers on a monthly basis promoting new releases from up-and-coming bands. Sub Pop’s stark, black and white logo showed up on records sleeves, posters, and t-shirts, reflecting a no-nonsense DIY-attitude rooted in in the production of loud guitars and heavy drums.Like the bands it represented, Sub Pop did not take itself too seriously when one of their best-selling t-shirts simply read “Loser” embracing the slacker mood of newly minted Generation X’ers born between 1961 and 1981. A July 1990 Time Magazine article described this twenty-something demographic as having “few heroes, no anthems, no style to call their own” suggesting they “possess only a hazy sense of their own identity” (Gross & Scott). As a member of this generation, I purchased and wore my “Loser” t-shirt with pride, especially in ironic response to the local cowboy way of life. I didn’t hold anything personal against the Wrangler wearing Garth Brooks fan but as a twenty-one-year-old reluctant college student, I wanted to rage with contempt for the status quo of my environment with an ambivalent snarl.Grunge in the MainstreamIn 1991, the Seattle sound exploded onto the international music scene with the release of four seminal grunge-era albums over a six-month period. The first arrived in April, Temple of the Dog, a tribute album of sorts to the late Andy Wood, led by his close friend, Soundgarden singer/songwriter, Chris Cornell. In August, Pearl Jam released their debut album, Ten, with its “surprising and refreshing, melodic restraint” (Fricke). The following month, Nirvana’s Nevermind landed in stores. Now on a major record label, DGC Records, the band had arrived “at the crossroads—scrappy garageland warriors setting their sights on a land of giants” (Robbins). October saw the release of Soundgarden’s Badmotorfinger as “a runaway train ride of stammering guitar and psycho-jungle telegraph rhythms” (Fricke). These four albums sent grunge culture into the ether with a wall of sound that would upend the music charts and galvanize a depressed concert ticket market.In fall of 1991, grunge landed like a hammer when I witnessed Nirvana’s video for “Smells Like Teen Spirit” on MTV for the first time. Sonically, the song rang like an anthem for the Gen Xers with its jangly four-chord opening guitar riff signaling the arrival of a youth-oriented call to arms, “here we are now, entertain us” (Nirvana). It was the visual power of seeing a skinny white kid with stringy hair wearing baggy jeans, a striped T-shirt and tennis shoes belting out choruses with a ferociousness typically reserved for black-clad heavy metal headbangers. Cobain’s sound and look didn’t match up. I felt discombobulated, turned sideways, as if vertigo had taken hold and I couldn’t right myself. Stopped in the middle of my tracks on that day, frozen in front of the TV, the subculture of grunge music slammed into my world while I was on my way to the fridge.Suddenly, grunge was everywhere, As Soundgarden, Nirvana, and Pearl Jam albums and performances infiltrated radio, television, and concert halls, there was no shortage of media coverage. From 1992 through 1994, grunge bands were mentioned or featured on the cover of Rolling Stone 33 times (Hillburn). That same year, The New York Times ran the article “Grunge: A Success Story” featuring a short history of the Seattle sound, along with a “lexicon of grunge speak” (Marin), a joke perpetrated by a former 25-year-old Sub Pop employee, Megan Jasper, who never imagined her list of made-up vocabulary given to a New York Times reporter would grace the front page of the style section (Yarm). In their rush to keep up with pervasiveness of grunge culture, even The New York Times fell prey to Gen Xer’s comical cynicism.The circle of friends I ran with were split down the middle between Nirvana and Pearl Jam, a preference for one over the other, as the two bands and their respective front men garnered much of the media attention. Nirvana seemed to appeal to people’s sense of authenticity, perhaps more relatable in their aloofness to mainstream popularity, backed up with Cobain’s simple-yet-brilliant song arrangements and revealing lyrics. Lawrence Grossberg suggests that music fans recognise the difference between authentic and homogenised rock, interpreting and aligning these differences with rock and roll’s association with “resistance, refusal, alienation, marginality, and so on” (62). I tended to gravitate toward Nirvana’s sound, mostly for technical reasons. Nevermind sparkled with aggressive guitar tones while capturing the power and fragility of Cobain’s voice. For many critics, the brilliance of Pearl Jam’s first album suffered from too much echo and reverb muddling the overall production value, but twenty years later they would remix and re-release Ten, correcting these production issues.Grunge FashionAs the music carved out a huge section of the charts, the grunge look was appropriated on fashion runways. When Cobain appeared on MTV wearing a ragged olive green cardigan he’d created a style simply by rummaging through his closet. Vedder and Cornell sported army boots, cargo shorts, and flannel shirts, suitable attire for the overcast climate of the Pacific Northwest, but their everyday garb turned into a fashion trend for Gen Xers that was then milked by designers. In 1992, the editor of Details magazine, James Truman, called grunge “un fashion” (Marin) as stepping out in second-hand clothes ran “counter to the shellacked, flashy aesthetic of 1980s” (Nnadi) for those who preferred “the waif-like look of put-on poverty” (Brady). But it was MTV’s relentless airing of Nirvana, Pearl Jam, and Soundgarden videos that sent Gen Xers flocking to malls and thrift-stores in search grunge-like apparel. I purchased a pair of giant, heavyweight Red Wing boots that looked like small cars on my feet, making it difficult to walk, but at least I was prepared for any terrain in all types of weather. The flannel came next; I still wear flannos. Despite its association with dark, murky musical themes, grunge kept me warm and dry.Much of grunge’s appeal to the masses was that it was not gender-specific; men and women dressed to appear unimpressed, sharing a taste for shapeless garments and muted colors without reference to stereotypical masculine or feminine styles. Cobain “allowed his own sexuality to be called into question by often wearing dresses and/or makeup on stage, in film clips, and on photo shoots, and wrote explicitly feminist songs, such as ‘Sappy’ or ‘Been a Son’” (Strong 403). I remember watching Pearl Jam’s 1992 performance on MTV Unplugged, seeing Eddie Vedder scrawl the words “Pro Choice” in black marker on his arm in support of women’s rights while his lyrics in songs like “Daughter”, “Better Man”, and “Why Go” reflected an equitable, humanistic if somewhat tragic perspective. Females and males moshed alongside one another, sharing the same spaces while experiencing and voicing their own response to grunge’s aggressive sound. Unlike the hypersexualised hair-metal bands of the 1980s whose aesthetic motifs often portrayed women as conquests or as powerless décor, the message of grunge rock avoided gender exploitation. As the ‘90s unfolded, underground feminist punk bands of the riot grrrl movement like Bikini Kill, L7, and Babes in Toyland expressed female empowerment with raging vocals and buzz-saw guitars that paved the way for Hole, Sleater-Kinney and other successful female-fronted grunge-era bands. The Decline of GrungeIn 1994, Kurt Cobain appeared on the cover of Newsweek magazine in memoriam after committing suicide in the greenhouse of his Seattle home. Mass media quickly spread the news of his passing internationally. Two days after his death, 7,000 fans gathered at Seattle Center to listen to a taped recording of Courtney Love, Cobain’s wife, a rock star in her own right, reading the suicide note he left behind.A few days after Cobain’s suicide, I found myself rolling down the highway with a carload of friends, one of my favorite Nirvana tunes, “Come As You Are” fighting through static. I fiddled with the radio to clear up the signal. The conversation turned to Cobain as we cobbled together the details of his death. I remember the chatter quieting down, Cobain’s voice fading as we gazed out the window at the empty terrain passing. In that reflective moment, I felt like I had experienced an intense, emotional relationship that came to an abrupt end. This “illusion of intimacy” (Horton and Wohl 217) between myself and Cobain elevated the loss I felt with his passing even though I had no intimate, personal ties to him. I counted this person as a friend (Giles 284) because I so closely identified with his words and music. I could not help but feel sad, even angry that he’d decided to end his life.Fueled by depression and a heroin addiction, Cobain’s death signaled an end to grunge’s collective appeal while shining a spotlight on one of the more dangerous aspects of its ethos. A 1992 Rolling Stone article mentioned that several of Seattle’s now-famous international musicians used heroin and “The feeling around town is, the drug is a disaster waiting to happen” (Azzerad). In 2002, eight years to the day of Cobain’s death, Layne Staley, lead singer of Alice In Chains, another seminal grunge outfit, was found dead of a suspected heroin overdose (Wiederhorn). When Cornell took his own life in 2017 after a long battle with depression, The Washington Post said, “The story of grunge is also one of death” (Andrews). The article included a Tweet from a grieving fan that read “The voices I grew up with: Andy Wood, Layne Staley, Chris Cornell, Kurt Cobain…only Eddie Vedder is left. Let that sink in” (@ThatEricAlper).ConclusionThe grunge movement of the early 1990s emerged out of musical friendships content to be on their own, on the outside, reflecting a sense of isolation and alienation in the music they made. As Cornell said, “We’ve always been fairly reclusive and damaged” (Foege). I felt much the same way in those days, sequestered in the desert, planting my grunge flag in the middle of country music territory, doing what I could to resist the status quo. Cobain, Cornell, Staley, and Vedder wrote about their own anxieties in a way that felt intimate and relatable, forging a bond with their fan base. Christopher Perricone suggests, “the relationship of an artist and audience is a collaborative one, a love relationship in the sense, a friendship” (200). In this way, grunge would become a shared memory among friends who rode the wave of this cultural phenomenon all the way through to its tragic consequences. But the music has survived. Along with my flannel shirts and Red Wing boots.References@ThatEricAlper (Eric Alper). “The voices I grew up with: Andy Wood, Layne Staley, Chris Cornell, Kurt Cobain…only Eddie Vedder is left. Let that sink in.” Twitter, 18 May 2017, 02:41. 15 Sep. 2018 <https://twitter.com/ThatEricAlper/status/865140400704675840?ref_src>.Andrews, Travis M. “After Chris Cornell’s Death: ‘Only Eddie Vedder Is Left. 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