Variance risk premium and equity returns

PurposeThe purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.Design/methodology/approachThe authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.FindingsThere are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.Practical implicationsCommodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.Originality/valueThe empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.

Variance trading and market price of variance risk

Introducing the BITIX: The Bitcoin Fear Gauge

Variance risk in aggregate stock returns and time-varying return predictability

Liquidity reflects the quality of the market. When the market is short of liquidity, it often causes investors’ trading difficulties and stock price volatility, expanding the investment risk. As a risk management tool, options attract more informed investors to trade because of their flexible design. To explore whether the implied information based on the formation of option price can predict the liquidity of stock market, we take SSE 50ETF options from February 9, 2015, to December 31, 2020, as the research sample. Based on the idea of data-driven approach, we extract the implied information contained in option price, including implied volatility, implied volatility spread, and variance risk premium. Through the regression analysis method, we examine the ability to predict the liquidity of the stock market. The results show that implied volatility spread has the strongest ability to predict the liquidity of the stock market, and it is more significant within 270 days. Implied volatility contains the information about the short-term (120 days) liquidity of the stock market in the future. It shows that implied volatility and implied volatility spread are good indicators to predict stock market liquidity. In contrast, variance risk premium cannot predict the liquidity of stock market. The research conclusion verifies the role of option-implied information in predicting the stock market’s liquidity. By extracting the information of options price, investors and financial regulators can scientifically participate in the financial market under data guidance.

Credit default swap spreads and variance risk premia

Cryptocurrency and Stock Markets: Complements or Substitutes? Evidence from Gulf countries

The intertemporal CAPM model of Merton (Econometrica, 41:867–887, 1973) demonstrates that the aggregate market risk is determined by the uncertainty of the underlying return, quantified by the return variance. When holding the market portfolio, however, an investor is also bearing the uncertainty of the variance itself. Just like the equity premium demanded by investors is the result of fear to the uncertainty of future returns, a variance risk premium, defined as the difference between risk-neutral and physical expected variances is also required to compensate for the randomness of the unknown variance. Many recent studies document the link between risk preference and variance risk premium. In particular, Bakshi and Madan (Manag Sci, 52:1945–1956, 2006) argue that the variance risk premium is approximately determined by the risk aversion parameter and the higher order moments of underlying returns. In a similar vein, Bollerslev (J Financ Quant Anal, 2013) assume an affine version of the stochastic volatility model as in Heston (Rev Financ Stud, 6:327–343, 1993) and show that the variance risk premium is linearly related to the risk aversion parameter. Other investigations in this area includes Bekaert and Engstrom (Asset return dynamics under bad environment-good environment fundamentals, Working Paper, NBER, 2010), Todorov (Rev Financ Stud, 23:345–383, 2010), and Gabaix (Q J Econ, 127:645–700, 2012).

PurposeConsidering the relationship between the central bank balance sheet and unconventional monetary policy after the 2008 financial crisis, it is crucial to see how the unconventional monetary policy, given near-zero interest rates, affects future stock market performance. This paper analyzes the impact of the Fed's balance sheet size on stock market performance.Design/methodology/approachTo analyze the Fed's balance sheet size's long-term stock market implications, this paper uses the asset pricing framework of market return predictability such as Ordinary least squares (OLS) and Generalized method of moments (GMM) analysis.FindingsFindings in this paper suggest that the Fed's balance sheet size, deflated by asset market wealth, presents evidence of return predictability during 1926–2015 that is robust against standard controls. These results can be explained through the redistribution of risk and the wealth channels of monetary policy transmission. The changing balance sheet size of a central bank (1) affects systemic risk, yields and expectations and (2) signals the future direction of monetary policy and thus economic outlook.Research limitations/implicationsThe main implication of these findings is that policymakers should avoid a severe imbalance between a central bank's balance sheet size and assets market wealth.Originality/valueThe empirical evidence in this paper documents a century-old relation between the Fed's balance sheet size and US stock market return using the Fed's balance sheet data for the last 100 years and stock market returns from the Center for research in security prices (CRSP) database.

Moment spreads in the energy market

The aim of this paper is an examination of the co-movement between OPEC oil price and Tehran Exchange Market returns. To analyze the relationship between two variables, applied the wavelet coherence approach and utilized daily data during 2009-2020. According to the wavelet approach, a dynamic causal relationship between oil price and the stock market return provides for the causality between the two variables and the type of causal relationship in the time-frequency analysis. Findings showed there is a positive correlation between oil price and stock market earnings. Comparison of the data in annual time-frequency scale indicated that the oil price and stock market returns are in phase from 2009 to 2011, and observed a positive relationship between them. From late 2011 to mid- 2015, both variables are in phase, and oil prices are the leading factor in the stock market. During the period 2015 to 2021, both variables are in phase, but co-movements of oil price and stock market returns not observed. The non-causal effect of oil price on stock market earnings at some times does not mean that oil prices do not affect the stock market. As mentioned, in most periods, oil price and stock market profits are in phase. Factors such as imposing economic sanctions on Iran such as oil sanctions, fluctuations in the value of the dollar, volatilities in oil prices, changes in global demand for exports and imports from Iran, privatization, increasing the gap between the official and the market exchange rate, financial markets shocks, Middle East price tensions and the financing process affected the relationship between the stock market and oil price.

PurposeThe study aims to analyze the interaction between macroeconomic uncertainty and stock market return and volatility for China and USA and tries to draw some invaluable inferences for the investors, portfolio managers and policy analysts.Design/methodology/approachEmpirically the study uses GARCH family models to capture the time-varying volatility of stock market and macroeconomic risk factors by using monthly data ranging from 1995:M7 to 2018:M6. Then, these volatility series are further used in the multivariate VAR model to analyze the feedback interaction between stock market and macroeconomic risk factors for China and USA. The study also incorporates the impact of Asian financial crisis of 1997–1998 and the global financial crisis of 2007–2008 by using dummy variables in the GARCH model analysis.FindingsThe empirical results of GARCH models indicate volatility persistence in the stock markets and the macroeconomic variables of both countries. The study finds relatively weak and inconsistent unidirectional causality for China mainly running from the stock market to the macroeconomic variables; however, the volatility spillover transmission reciprocates when the impact of Asian financial crisis and Global financial crisis is incorporated. For USA, the contemporaneous relationship between stock market and macroeconomic risk factors is quite strong and bidirectional both at first and second moment level.Originality/valueThis study investigates the interaction between stock market and macroeconomic uncertainty for China and USA. The researchers believe that none of the prior studies has made such rigorous comparison of two of the big and diverse economies (China and USA) which are quite contrasting in terms of political, economic and social background. Therefore, this study also tries to test the presumed conception that macroeconomic uncertainty in China may have different impact on the stock market return and volatility than in USA.

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

Objective: Due to the importance of studing the behavior of asset markets, the risk aversion term and its operational calculation has attracted many researchers. The present study intends to examine this issue by considering a portfolio with three assets in the three markets of stock, currency and gold. Therefore, by examining the fluctuations of previous years in the portfolio consisting of three macroeconomic assets , stocks, currency and gold, we can assess the risk aversion of investors in these markets and the causal relationship between these markets and take steps to construct long-term policy goals. Method: To investigate the issue, using a mathematical equilibrium model and dynamic econometric methods, the monthly data of stock markets, currency and gold during the period 1995 to 2018 has been analyzed. Results: Based on the research findings, the amount of risk aversion for the sample size shows that the average risk aversion index of investors in the stock market is higher than the other two markets and this index is the lowest in the gold market. Also, the value-at-risk index of return has a one-way causality and a significant relationship with the degree of risk aversion of investors in all three asset markets. Conclusion: The results of this study of risk aversion and risk value in these markets can help policymakers to better understand the interactions of these markets, control them and eliminat the destabilizing conditions of these markets. Specifically in the case of the Iranian stock market, it can be said that investors are relatively more risk averse to the both gold and foreign exchange markets in the conditions of economic recession and increasing political instability, in other words, stock market investors have less confidence in the exchange and gold markets. The reason is the newness of the stock market compared to the global stock markets and also the longer existence of gold and foreign exchange markets than the emerging stock market in Iran. Also since gold assets have an intrinsic value and the dollar market is supported by a large global economy, Therefore to strengthen the support of the Iranian stock market, it seems improving economic infrastructure, promotion of the conditions of companies in the stock market, increasing government support for competitive condition and prohibition of direct intervention in the stock market are necessary. In addition, due to the complex nature of the stock market and the lack of sufficient public information , the need to inform and increase public awareness for a long-term presence in the stock market seems necessary.

Do government policies during the COVID-19 pandemic affect investors’ risk aversion, as proxied by the variance premium? To answer this question, this study examines data regarding government responses from thirteen countries. The empirical analysis indicates that government interventions were not able to substantially reduce variance risk premium in international equity markets. The results also show that economic support policies, containment, and closure regulations, and health system interventions all played a significant role in shaping equity variance risk price.