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Purpose – This chapter aimed to investigate hedge funds market risk. One aims to go further the traditional measures of risk that underestimates it by introducing a more appropriate method to hedge funds. One demonstrates that daily hedge fund return distributions are asymmetric and leptokurtic. Furthermore, volatility clustering phenomenon and the existence of ARCH effects demonstrate that hedge funds volatility varies through time. These features suggest the modelisation of their volatility using symmetric (GARCH) and asymmetric (EGARCH and TGARCH) models used to evaluate a 1-day-ahead value at risk (VaR).

Methodology/Approach – The conditional variances were estimated under the assumption that residuals t follow the normal and the student law. The knowledge of the conditional variance was used to forecast 1-day-ahead VaR. The estimations are compared with the Gaussian, the student and the modified VaR. To sum up, 12 VaRs are computed; those based on standard deviation and computed with normal, student and cornish fisher quantile and those based on conditional volatility models (GARCH, TGARCH and EGARCH) computed with the same quantiles.

Findings – The results demonstrate that VaR models based on normal quantile underestimate risk while those based on student and cornish fisher quantiles seem to be more relevant measurements. GARCH-type VaRs are very sensitive to changes in the return process. Back-testing results show that the choice of the model used to forecast volatility has an importance. Indeed, the VaR based on standard deviation is not relevant to measure hedge funds risks as it fails the appropriate tests. On the opposite side, GARCH-, TGARCH- and EGARCH-type VaRs are accurate as they pass most of the time successfully the back-testing tests. But, the quantile used has a more significant impact on the relevance of the VaR models considered. GARCH-type VaR computed with the student and especially cornish fisher quantiles lead to better results, which is consistent with Monteiro (2004) and Pochon and Teïletche (2006).

Originality/Value of chapter – A large set of GARCH-type models are considered to estimate hedge funds volatility leading to numerous evaluation of VaRs. These estimations are very helpful. Indeed, public savings under institutional investors management then delegate to hedge funds are concerned. Therefore, an adequate risk management is required. Another contribution of this chapter is the use of daily data to measure all hedge fund strategies risks.

The purpose of this paper is to examine volatility and the weak-form efficient market hypothesis (random walk) of world spot crude oil market.

The study uses the generalized autoregressive conditional heteroskedasticity (GARCH-M), exponential generalized autoregressive conditional heteroskedasticity (EGARCH), and threshold GARCH (TGARCH) models. The data are selected from three markets: Dubai Vetch (DV), West Texas Intermediate, and Europe Brent Spot Price.

The weak-form efficient market (random walk) hypothesis was rejected for all estimated GARCH-M, EGARCH, and TGARCH models, indicating that these markets are inefficient and predictable. For daily data, the empirical results showed the presence of asymmetric effects, and the conditional variance process was found to be highly persistent.

This study is unique in its nature as it examines three markets on three continents. In addition, one of these markets (DV) was not carried out by the previous study. This work takes into account the market location.

The purpose of this paper is to examine the existence of the day-of-the-week effect in the Indian stock market.

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1), Exponential GARCH (EGARCH) (1, 1) and Threshold GARCH (TGARCH) (1, 1) models are employed to examine the day-of-the-week effect in the Indian stock market for the period of 28 years from 3rd July, 1990 to 31st March, 2022.

The empirical results derived from the GARCH models indicate the existence of day-of-the-week effects on stock returns and volatility of the Indian stock market. The study reveals that all the days of the week are positive and significant in National Stock Exchange (NSE)-Nifty market returns. The findings confirm the persistence of ARCH and GARCH effects in the daily return series. Moreover, the asymmetric GARCH models show that the daily stock returns exhibit significant asymmetric (leverage) effects.

The results of this study established that the Indian stock market is not efficient and there exists an opportunity to the traders for predicting the future prices and earning abnormal profits in the Indian stock market. The findings of the study are important for traders, investors and portfolio managers to earn abnormal returns by cross-border diversification.

First, to the best of the authors' knowledge, this paper is the first to study the day-of-the-week effect in Indian stock market considering the most recent and longer time period (1990–2022). Second, unlike previous research, this study used GARCH models (GARCH, EGARCH and TGARCH) to capture the volatility clustering in the data.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

This study aims to examine the impact of the stringency of COVID-19 protocols on the volatility of sectoral indices during the period 03:2020–05:2021. Specifically, this study investigates the role of economic disturbances on sectoral volatility by applying a range of conditional volatility techniques.

For this analysis, two approaches were adopted. The first approach considers COVID stringency as a factor in the conditional variance equation of sectoral indices. In contrast, the second approach considers the stringency indicator as a possible determinant of their estimated conditional volatility.

Results show that the stringency of the protocols throughout the pandemic phase led to an instantaneous spike followed by a gradual decrease in estimated volatility of all the sectoral indices except pharma and health care. Specific sectors such as bank, FMCG, consumer durables, financial services, IT, media and private banks respond to protocols expeditiously compared to other sectors.

The key contribution of this study to the existing literature is the innovative approach. The inclusion of the COVID stringency index as a regressor in the variance equation of the conditional volatility techniques was a distinctive approach for assessing the volatility dynamics with the stringency of COVID protocols. Furthermore, this study also adopts an alternative approach that estimates the conditional volatility of the indices and then tests the effect of the stringencies on estimated volatility in a regression framework.

Clean energy stocks are exhibiting signs of increasing volatility reflecting the varied and conflicting strategies employed by nations to pursue energy security objectives. In this regard, this paper aims to examine the response of NASDAQ clean energy stock returns volatility to the influences of external energy security elements including oil price, natural gas price, coal price, carbon price and green information technology stock price.

The paper uses symmetric and asymmetric generalised autoregressive conditional heteroskedasticity models (GARCH and TGARCH, respectively), which incorporate external energy security elements as exogenous variables, to estimate volatility models for clean energy stock returns.

Although, prices of oil, coal and natural gas are negatively associated with NASDAQ clean energy returns volatility, only the effect of natural gas price is significant. While carbon price affects NASDAQ clean energy returns volatility positively, green information technology price affects the volatility negatively. These results are robust to exponential GARCH and lead-and-lag robust ordinary least-squares as alternative estimation methods.

The study lumps the effects of all other external and internal factors, including internal energy security elements, in the autoregressive conditional heteroscedasticity (ARCH) term to predict NASDAQ clean energy returns conditional variance. GARCH method does not disentangle individual roles of the factors captured in the ARCH term in predicting volatility.

Results documented imply that natural gas appears a closer substitute for renewable energy sources than crude oil and coal, such that its price rise is perceived as good news in the NASDAQ clean energy financial market, while a fall is considered bad news. Furthermore, both an increase in carbon price and a decrease in green information technology stock performance are perceived as negative shocks.

In assessing risks associated with clean energy stocks, investors and fund managers should carefully consider the effects of external energy security elements.

To the best of the author’s knowledge, the paper is the first to identify external energy security elements and examine their effects on clean energy stock volatility.

The purpose of this paper is to examine the US real estate investment trusts (REITs) for the 2000‐2012 period using GARCH models that include the day‐of‐the‐week effect and the stock‐market index as explanatory variables. This technique documents the return and volatility of equity, mortgage and hybrid REITs.

The study starts with a CAPM model and continues with GARCH(1,1), TGARCH(1,1) and EGARCH(1,1) models for each of the REIT subcategories with and without the days of the week as dummy variables.

The results show that the best‐fitted model is EGARCH except the equity REIT series without the dummy variables that is better described with the GARCH. The stock market has a significant impact on REIT returns but no remarkable significance in respect of the day‐of‐the‐week effect.

The findings suggest that there is not a significant risk diversification potential between REITs and common stocks. In the scope of the credit crisis which originated in the real estate market it must be taken seriously into consideration that REITs, except of the equity REITs, are more sensitive to bad news.

This paper uses daily returns for each of the three main REIT subcategories opposed to the monthly that are commonly used. We point out the evidence of asymmetric responses, suggesting the leverage effect and differential financial risk depending on the direction of price change movements.

The purpose of this paper is to examine fine wine’s safe-haven status with respect to US equity movements.

We use a generalized autoregressive conditional heteroscedasticity model and its variant to measure the asymmetric reaction to positive and negative shocks.

Our empirical results show an inverted asymmetric volatility in the wine market; positive shocks increase the conditional volatility more than negative shocks. That is the opposite reaction in the volatility of equity returns occurs in the wine market. As leverage effect and volatility feedback effect do not adequately explain this reaction, we follow the work of Baur (2012) and propose the safe haven effect. Several robustness tests largely confirm the empirical findings, with major implications for wine investors. Finally, we provide further evidence on the benefits of adding wine investments to an equity portfolio through an increase in risk reduction effectiveness.

Based on the results of the robustness analysis, the recommendations in terms of including fine wines in portfolios must be issued with caution.

Our findings are crucial to the needs of market participants who are interested in including wine assets in their equity portfolio.

No previous study investigates the safe haven property of fine wine return, and accounts for risk reduction effectiveness when adding wine assets to a portfolio of US equities.

The aim of this paper is to examine the relationship between weather (temperature) and stock market returns using daily data from Portugal; also, to examine whether the temperature is driven by calendar‐related anomalies such as the January and trading month effects.

Daily financial and weather data from Lisbon Stock Exchange (PSI 20 index) and Lisbon capital for the period 1995‐2007 are considered. The paper employs an AR(1)‐TGARCH(1,1) model under several distributional assumptions (Normal, Student's‐t and GED) for the errors.

Empirical results show that temperature affects negatively the PSI20 stock returns in Portugal. Moreover, temperature is dependent of both January and trading month effects. Stock returns were found to be positive in January and higher over the first fortnight of the month. Lower temperature in January leads to higher stock returns due to investors' aggressive risk taking.

Further research should investigate the impact of other meteorological variables (humidity, amount of sunshine) and other calendar anomalies on the course and behaviour of major international stock indices using data before and after the recent crisis.

The findings are helpful to financial managers, investors and traders dealing with the Portuguese stock market.

The contribution of this paper is to provide evidence on the empirical linkages between temperature and stock market returns using GARCH models. To better understand the relationship between the temperature and stock market returns, the paper also examines whether the returns are higher in winter (January effect) and during the first or second fortnight of the month (trading month effect). To the best of the author's knowledge, this is the first empirical investigation on weather and stock market returns relationship for Portugal.

Emotion plays a significant role in both institutional and individual investors’ decision-making process. Emotions affect the perception of risk and the assessment of monetary value. However, there is a lack of empirical evidence available that addresses how investors’ emotions affect commodity market returns. The purpose of this paper is to investigate whether media-based emotions can be used to predict future commodity returns.

The authors examine the short-term predictive power of media-based emotion indices on the following five days’ commodity returns. The research adopts a proprietary data set of commodity-specific market emotions, which is computed based on a comprehensive textual analysis of sources from newswires, internet news sources and social media. Time series econometrics models (threshold generalized autoregressive conditional heteroskedasticity and vector autoregressive) are employed to analyze 14 years (January 1998-December 2011) of daily observations of the CRB commodity market index, crude oil and gold returns, and the market-level sentiments and emotions (optimism, fear and joy).

The empirical results suggest that the commodity-specific emotions (optimism, fear and joy) have significant influence on individual commodity returns, but not on commodity market index returns. Additionally, the research findings support the short-term predictability of the commodity-specific emotions on the following five days’ individual commodity returns. Compared to the previous studies of news sentiment on commodity returns (Borovkova, 2011; Borovkova and Mahakena, 2015; Smales, 2014), this research provides further evidence of the effects of news and social media-based emotions (optimism, fear and joy) in the commodity market. Additionally, this work proposes that market emotion incorporates both a sentimental effect and appraisal effect on commodity returns. Empirical results are shown to support both the sentimental effect and appraisal effect when market sentiment is controlled in crude oil and gold spot markets.

This paper adopts the valence-arousal approach and cognitive appraisal approach to explain financial anomalies caused by investors’ emotions. Additionally, this is the first paper to explore the predictive power of investors’ emotions (optimism, fear and joy) on commodity returns.