Asymmetry Of Returns Research Articles

ON THE SOLUTION UNIQUENESS IN PORTFOLIO OPTIMIZATION AND RISK ANALYSIS

In this paper, we consider the issue of solution uniqueness of the mean-deviation portfolio optimization problem and its inverse for asset returns distributed over a finite number of scenarios. Due to the asymmetry of returns, the risk is assessed by a general deviation measure introduced by Rockafellar et al. [(2006b) Optimality conditions in portfolio analysis with general deviation measures, Mathematical Programming 108, 515–540] instead of the standard deviation as in the classical Markowitz optimization problem. We demonstrate that, in general, one cannot expect the uniqueness of Pareto-optimal profit sharing in cooperative investment and the uniqueness of solutions in the mean-deviation Black–Litterman asset allocation model. For a large class of deviation measures, we provide a resolution of the above nonuniqueness issues based on the principle of law-invariance. We provide several examples illustrating the nonuniqueness and the law-invariant solution.

Extreme downside risk connectedness between green energy and stock markets

This study addresses the need to understand the transmission of tail risk across financial markets, especially in the context of green energy and stock markets. Utilizing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology combined with the Conditional Autoregressive Value-at-Risk (CAViaR) framework, the model analyzes data from September 30, 2013, to October 11, 2023. This study focuses on five major stock indices (S&P500, CSI300, Nikkei225, STOXX50, and FTSE100), West Texas Intermediate (WTI) crude oil, and three sectors within green energy markets (Green bond, Global Clean Energy, and Renewable Energy and Clean Technology), highlighting the significant role of these sectors in risk propagation. The model can capture dynamic changes and asymmetries in financial returns, thus providing a precise estimation of extreme downside risks. Key findings show that three stock indices (S&P500, STOXX50, and FTSE100) and one green energy sector (Renewable Energy and Clean Technology sectors) are the predominant sources of risk, with significant connectedness around events, such as the COVID-19 pandemic. These insights are crucial for developing effective risk-management strategies and supporting the transition to a sustainable energy sector. This study concludes that understanding these risk dynamics is essential for strategic planning and market stability.

Asymmetry in returns and volatility between green financial assets, sustainable investments, clean energy, and international stock markets

This paper presents empirical evidence on the asymmetric relationship between green investments and international stock markets. We employ the asymmetric versions of Diebold and Yilmaz (2012) and Barunik and Krehlik (2018) for time-frequency connectedness, analyzing daily returns and volatilities from June 23, 2009, to June 23, 2022. Our study reveals significant time-frequency asymmetries in returns and volatility spillovers between green investments and developed equity markets in the short and long term. Regarding net directional spillovers, the equity markets in the United States, the United Kingdom, Italy, Germany, and France emerge as net transmitters of shocks. In contrast, green investments, notably those in sustainability and the environment, act primarily as net emitters of shocks. China and Japan are the primary recipients of these shocks. Meanwhile, green bonds generally function as net receivers of shocks, with occasional exceptions.

Exploring asymmetries in cryptocurrency intraday returns and implied volatility: New evidence for high-frequency traders

This paper aims to analyze the return-volatility relationship of Bitcoin and Ethereum across different return frequencies and all conditional quantiles of implied volatility, based on a unique 6.5 million observations. We employ the newly constructed Model-Free Implied Volatility (MFIV) of Bitcoin (BitVol) and Ethereum (EthVol) and use an asymmetric Quantile Regression Model (QRM) to capture the intraday asymmetric return-volatility relationship at different quantiles of the distribution of the dependent variable. Our findings show that the estimated coefficient using daily data is significant only at medium- to high-volatility regimes, while the estimated coefficients using high-frequency data are highly significant across all volatility regimes. Moreover, our results indicate that the asymmetry varies across frequencies and quantiles, with weak asymmetric effects at low quantiles and high frequencies, and strong asymmetric effects at high quantiles and low frequencies. This study provides new insight, especially for high-frequency traders.

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets.

The asymmetry in day and night option returns: Evidence from an emerging market

AbstractDelta‐hedged option selling strategies typically yield positive returns, owing to the volatility risk premium embedded in the option price. Recent research based on S&P 500 options has found a day–night asymmetry in option returns. We find a similar disparity in the returns for short Nifty option strategies. Positive and significant overnight option returns are accompanied by negative intraday returns. The day–night asymmetry is robust across option categories and subsamples but weaker on days with significant jumps in the underlying. We confirm that the variance risk premium earned by option sellers is mainly a reward for overnight risk.

Global Stock Market Volatility and Its Spillover on the Indian Stock Market: A Study Before and During the COVID-19 Period

This study investigates the impact of global stock market volatility on the Indian stock markets before and during the COVID-19 pandemic period. The study focuses on 11 stock markets, including Brazil, Canada, China, France, Hong Kong, India, Japan, Russia, Turkey, the UK, and the US, and applies the threshold generalized autoregressive conditional heteroskedasticity (TGARCH) model to capture the current asymmetry in returns influenced by past negative/positive shocks, and the diagonal Baba Engle Kraft Kroner (BEKK) model to examine the cohesion of the Indian equity market with global markets. The importance of the Indian stock market lies in its ability to provide capital to companies, attract foreign investment, and provide investment opportunities for both domestic and international investors. Data for the study was sourced from https://www.investing.com for the period September 2019 to September 2021 and Stata software was used for data analysis. The study finds that Brazil, Canada, France, Russia, UK, and the USA are the primary sources of financial weight on India’s stock returns. The results suggest that Indian investors can diversify their funds into other asset classes while restricting investments in these markets, particularly during downturns. Investors can make informed decisions to diversify their portfolio and minimize risk. The results can also benefit society by promoting a more stable and resilient financial system. The study can also be expanded to include other financial and economic indicators such as inflation and interest rates to provide a more comprehensive analysis of the impact of global market volatility on Indian equity markets.

Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management

Unlike volatility, the skewness and kurtosis of asset returns are often neglected in the analysis of spillovers and risk management, although they capture the return asymmetry and fat-tailedness, respectively, arising from the non-normality of returns. In this paper, we provide evidence of the relevance and utility of considering spillovers in volatility and higher-order moments (skewness, and kurtosis) and co-moments (covariance, co-skewness, and co-kurtosis), and their implications for hedging. Using high-frequency data on the US stock, crude oil, and gold markets, a time-varying spillover approach and portfolio analysis, we reveal the following results. Firstly, besides volatility and covariance, co-skewness and co-kurtosis are relevant spillover transmitters across the stock, crude oil, and gold markets. Secondly, the level of total spillover increases when including not only covariance but also co-skewness and co-kurtosis, suggesting the relevance of considering higher order co-moments beyond volatility when studying spillovers. Thirdly, the inclusion of co-moments in the spillover analysis generates a significant improvement in hedging for all pairs, which is reflected in the significant increase in the utility function when co-skewness and co-kurtosis are considered. This result is noted when the COVID-19 sub-period is considered separately, except for oil‑gold. Overall, the findings matter for the system of interconnectivity across various assets and emphasize the implications and contributions of higher-order moments and co-moments to portfolio allocation and financial risk management.

Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management

This paper examines energy and agricultural commodities' short-run and long-run connectedness by using the Time-varying parameter vector autoregressions (TVP-VAR). It applies the frequency version of the TVP-VAR model, which is a modified version of the dynamic TVP-VAR model. The frequency decomposition definition also decomposes into short-run and long-run connectedness. We further the analysis by investigating the effect of asymmetry in returns on connectedness. It also examines how portfolio management strategies would lead to a maximization of profits with minimal risks. Empirical evidence indicates that only 32.52% and 31.38% of connectedness in oil and gas, respectively, are transmitted to agricultural commodities, which suggests their weak tendencies in influencing agricultural commodities; the total connectedness index hovers around 40–60% in the 2018–2019 period; however, it dropped below 40% in 2020–2021 when the COVID-19 pandemic contributed to disintegrate the connectedness between energy and agricultural commodities but increased further during the 2022 Russia-Ukraine saga. The findings also indicate that corn, wheat, and flour are net transmitters of risks to oil and natural gas in the long and short-run, and wheat-flour pairwise connectedness is the strongest in the connectedness. Asymmetry is also pronounced in the network of connectedness. Portfolio analyses indicate that investors require a low proportion of energy in a portfolio of energy-agricultural commodities to achieve an optimum profit. The findings will offer exciting insights into the connectedness of agricultural and energy commodities, particularly during periods of high price uncertainty.

The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns

The impact of Bitcoin futures introduction on the underlying Bitcoin volatility has been a controversial topic. Conflicting results had been obtained from different sample periods and methodologies. To address this debate, this study examines the impact of futures trading on volatility and volatility asymmetry of Bitcoin returns in the short and long run. Using exponential GARCH models, we introduce a dummy in the variance equation to capture the changes in the volatility after the introduction of Bitcoin futures. We find that after the introduction, spot return volatility decreases in the short run, but increases in the long run. Besides, in the short run, there exists an inverse leverage effect before and after the introduction; in the long run, the inverse leverage effect before the introduction changes to a usual level effect after the introduction. Finally, we examine whether greater futures trading activity, proxied by trading volume and open interest, is associated with greater Bitcoin volatility. To do so, we decompose each proxy into expected and unexpected components and document that, in the long run, Bitcoin volatility covaries positively with unexpected futures trading volume, but negatively with unexpected futures open interest.

Resource redeployment in corporate acquisitions: Going beyond horizontal acquisitions

Resource redeployment between merging firms is an important way in which corporate acquisitions create value. Such redeployment can occur in horizontal deals when the acquirer and the target are from the same industry, or in non-horizontal deals when the acquirer and the target come from different industries. Although existing research focuses on the former scenario of resource deployment in horizontal acquisitions, resource deployment as a potential source of value in non-horizontal acquisitions has been understudied. This study uses a formal model to develop a theory of resource redeployment that both embraces and contrasts both types of acquisitions to offer new insights. The study concludes that the focus on horizontal acquisitions misses M&A contexts in which resource redeployment can create the highest value. Results from the model also demonstrate that acquisition performance has an inverse U-shaped relationship with relatedness between the merging firms. Finally, the effect of relatedness critically depends on the asymmetry in returns between the merging firms. These results are important for future empirical studies of acquisition performance and of target selection and are instructive to corporate managers seeking targets and managing potential redeployment of resources across organizations via M&A.

Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain.

Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.

Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models

The ever-growing volume of cryptocurrency transactions indicates the importance to understand the new cryptocurrency market. Many research works have demonstrated the unique features of cryptocurrency market compared to other asset markets. Under the impact of Covid-19, the cryptocurrency market may display more differential features. We analyse these differential features of the cryptocurrency market by studying their return persistence, return asymmetry, interdependency, and volatility spillover. The vector autoregressive moving average model with variance gamma innovations is proposed to capture these features before and during the pandemic outbreak. We consider four cryptocurrencies, namely Bitcoin, Ripple, Dash, and Litecoin which have top market capitalisation. For model estimation, we apply the computational efficient expectation/conditional maximisation algorithm. We interpret the results concerning their technological setups.

Modeling realized volatility with implied volatility for the EUR/GBP exchange rate

This paper concerns the application of implied volatility in modeling realized volatility in the daily, weekly and monthly horizon using high-frequency data for the EUR/GBP exchange rate. The EUR/GBP rate was chosen because it is under stress triggered by the uncertainty related to Brexit. The heterogeneous autoregression (HAR) model of realized volatility and its extensions were used for the study: the HAR model extended by the measurement error component and by the components of the asymmetry of returns. The introduction of implied volatility into the HAR forecast model improves the predictions in the periods considered irrespective of the period chosen. In particular, we find new evidence that implied volatility obtained from options with a weekly expiration date improves the quality of volatility forecasts. The best prediction for daily and weekly volatility provides the model with asymmetry of returns. For monthly volatility, the model corrected for measurement errors has the best fit.

Two Sided Efficient Frontiers at Multiple Time Horizons

Two price economy principles motivate measuring risk by the cost of acquiring the opposite of the centered or pure risk position at its upper price. Asymmetry in returns leads to differences in risk charges for short and long positions. Short risk charges dominate long ones when the upper tail dominates the comparable lower tail for charges based on distorted expectations. Positive mean return targets acquire long positions with negative mean return targets taking short positions. In each case the appropriate risk charge is minimized to construct two frontiers, one for the positive, and the other for negative, mean return targets. Multivariate return distributions reflect limit laws given by Q self-decomposable laws displaying decay rates in skewness and excess kurtosis slower than those for processes of independent and identically distributed returns. Frontiers at longer horizons display greater efficiency reflected by lower risk charges for comparable mean return targets. The short side frontiers also display greater risk charges than their long side counterparts. All efficient portfolios deliver asset pricing equations whereby required returns in excess of a reference rate are a market price of risk times a risk gradient evaluated at the efficient portfolio. Variations in frontiers and points on the frontier induce differences in reference rates, risk gradients, and the market prices of risk that can yet lead to comparable required returns.

Using conditional asymmetry to predict commodity futures prices

Despite decades of studies, there is still no consensus on what type of serial dependence, if any, might be present in risky asset returns. This manuscript provides an empirical study of the prices of energy commodities, gold and copper in the futures markets and demonstrates that, for these assets, the level of asymmetry of asset returns varies through time and can be forecast using past returns. A regime switching model is used to construct a managed futures trading strategy that provides returns that are statistically significant. It is also demonstrated how such model can be used to make probabilistic predictions of commodity prices in futures markets, which can be used to drive value-at-risk and potential future exposure metrics or guide dynamic hedging strategies of commodity price risk.

Conditional Skewness in Currency Markets

In this paper, I introduce the modeling of currency returns conditional on the interest rate differential and the real exchange rate under the assumption that FX returns are skew-t distributed. Beyond the well-known relationship between the currency risk premium and its risk factors, I document a negative effect of the interest rate differential and the real exchange rate on idiosyncratic skewness in a short-term horizon for a set of developed and emerging market currencies. The out-of-sample results reveal the evidence of the predictability of idiosyncratic skewness and the ability of conditional skewness to forecast currency risk premia at the country level. The risk factor based on conditional skewness is priced across carry trade portfolios and improves the understanding of the carry trade strategy returns. Flexible modeling of the return asymmetry and fat tails provides an accurate forecast of the value-at-risk and expected shortfall and contributes to the analysis of downside risk in the FX market.

Investigating risk-return relationship: An empirical study in Iraq stock market

The objective of current research is to detect correlation between risk and return as there are inconclusive results regarding this issue. The study used daily closing prices of six banks from Iraq Stock Exchange (ISE) over period 1st Jan 2015 to 31 Dec 2017. The paper employed both symmetric and asymmetric models of different properties of the Generalized Autoregressive Conditional Heteroscedastic (GARCH). Findings obtained by study could not give proof of presence of a positive risk premium in marketplace. Furthermore, asymmetric model of Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) showed asymmetry in stock returns which refers to occurrence of power impact in takings sequences. However, results indicated that good news is more destabilizing than bad news in ISE.

Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model

We develop a factor stochastic volatility model that incorporates leverage effects, return asymmetry, and heavy tails across all systematic and idiosyncratic model components. Our model leads to a flexible high-dimensional dependence structure that allows for time-varying correlations, tail dependence, and volatility response to both systematic and idiosyncratic return shocks. We develop an efficient Markov chain Monte Carlo algorithm for posterior estimation based on the particle Gibbs, ancestor sampling, particle efficient importance sampling methods, and interweaving strategy. To obtain parsimonious specifications in practice, we build computationally efficient model selection directly into our estimation algorithm. We validate the performance of our proposed estimation method via simulation studies with different model specifications. An empirical study for a sample of U.S. stocks shows that return asymmetry is a systematic phenomenon and our model outperforms other factor models for value-at-risk evaluation.

The Good and Bad Volatility: A New Class of Asymmetric Heteroskedastic Models

AbstractThis paper introduces a new class of tractable asymmetric heteroskedastic models, the good and bad volatility (GBV). Asymmetry is recognized in the dynamics of GBV components that correspond to positive and negative shocks respectively. The GBV model allows both conditional semivariances to evolve according to two separate functional forms with different semi‐definite distributions. An empirical application to six major index returns shows a fitting improvement over well‐known asymmetric volatility models in the financial literature. The model further leads to significant improvements in forecasting performance. The derived nontrivial news impact curves convey the dichotomy that asymmetry in financial returns has different dynamics for positive and negative shocks.