Expected market returns: SVIX,realized volatility,and the role of dividends

This note provides a replication of Martin's (Quarterly Journal of Economics, 2017, 132(1), 367–433) finding that the implied volatility measure SVIX predicts US stock market returns up to 12‐month horizons. I find that this result holds for both S&P 500 and CRSP market returns, regardless of whether returns include or exclude dividends. The predictability largely disappears after the SVIX index is replaced by an exponentially weighted moving average measure of realized volatility, suggesting that SVIX holds incremental forward‐looking information compared to realized volatility, despite the high correlation between the two volatility measures.     

The role of mutual funds in the sustainable energy sector

The integration of renewable energy criteria in mutual fund investment decisions could channel private resources into the funding of environmentally related projects implemented by firms contributing to sustainable development. This paper examines the performance of European renewable energy funds that invest globally by comparing their risk‐adjusted returns with those achieved by black energy and conventional mutual funds. It uses Carhart's model on a sample of 81 renewable energy funds, 125 black energy funds, and 4,337 conventional mutual funds. The results indicate that 32.1% of renewable mutual funds—most of which adopt energy producers, renewable energy technology, and energy efficiency‐focused criteria—perform significantly better than the S&P Clean Energy market benchmark, this percentage being affected by the different states of the economy. However, none of them are able to beat the fossil fuel energy (S&P Global 1200 Energy Index) or conventional market benchmarks (S&P Global 1200 Index). Furthermore, 37.04% of renewable energy funds significantly underperform the S&P Global 1200 benchmark. Therefore, the investment in renewable energy funds has a financial cost for investors in relation to conventional fund investors.     

The day of the week effect on stock market volatility

This study tests the presence of the day of the week effect on stock market volatility by using the S&P 500 market index during the period of January 1973 and October 1997. The findings shown that the day of the week effect is present in both volatility and return equations. While the highest and lowest returns are observed on Wednesday and Monday, the highest and the lowest volatility are observed on Friday and Wednesday, respectively. Further investigation of sub-periods reinforces our findings that the volatility pattern across the days of the week is statistically different.(JEL G10, G12, C22)     

Evolving United States stock market volatility: The role of conventional and unconventional monetary policies

Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogeneous vector autoregressive (SHVAR) model with identified structural breaks to analyse the impact of both conventional and unconventional monetary policies on U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock market periods) are also corroborated by analyses conducted at the daily frequency based on an augmented heterogeneous autoregressive model of realised volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework. Our results have important implications both for investors and policymakers.     

Dynamic connectedness of China’s green bonds and asset classes

This paper quantifies the co-movement and time-varying integration between China's green bonds and other asset classes across different time domains using the wavelet coherence and time-frequency connectedness model based on the time-varying parameter VAR (TVP-VAR). First, we predominantly detect a strong positive co-movement of green and conventional bonds, especially in the medium and long term. Second, strong bidirectional spillovers exist between green bonds and treasury, corporate, and financial bonds regardless of the time horizon. Lastly, cross-market spillovers between the green bonds and the stock, energy, low-carbon stock market were quite limited in the short-run but strengthened towards the long-term except during the 2015 China stock market crash and the COVID-19 recession when short-term integration rose sharply. The results document some practical enlightenment for investors and policymakers with various time horizons.     

Time-frequency co-movement and network connectedness between green bond and financial asset markets: Evidence from multiscale TVP-VAR analysis

This study examines the time-frequency co-movement and network connectedness between green bonds and other financial assets in China. We propose wavelet coherence and multiscale TVP-VAR to explore the time-frequency co-movement and spillover connectedness. The empirical results are as follows. First, green bonds positively co-move with conventional bonds across time scales and negatively co-move with stocks and commodities. Second, there is a significant network connectedness of green bonds with conventional bonds in the short term, and the connectedness with stocks and commodities gradually strengthens with the increase in time scales. Third, the dynamic spillover between green bonds and other assets is much greater in the long and medium terms than in the short term. Finally, under crisis shocks, the spillovers spike temporarily in the short term, while they are persistent and at a high level in the long term. Overall, some practical implications are proposed for investors and policymakers.     

Is bitcoin a safe haven or a hedging asset? Evidence from China

Based on daily data about Bitcoin and six other major financial assets (stocks, commodity futures (commodities), gold, foreign exchange (FX), monetary assets, and bonds) in China from 2013 to 2017, we use a VAR-GARCH-BEKK model to investigate mean and volatility spillover effects between Bitcoin and other major assets and explore whether Bitcoin can be used either as a hedging asset or a safe haven. Our empirical results show that (i) only the monetary market, i.e., the Shanghai Interbank Offered Rate (SHIIBOR) has a mean spillover effect on Bitcoin and (ii) gold, monetary, and bond markets have volatility spillover effects on Bitcoin, while Bitcoin has a volatility spillover effect only on the gold market. We further find that Bitcoin can be hedged against stocks, bonds and SHIBOR and is a safe haven when extreme price changes occur in the monetary market. Our findings provide useful information for investors and portfolio risk managers who have invested or hedged with Bitcoin.     

An application of nonparametric volatility estimators to option pricing

We discuss the impact of volatility estimates from high frequency data on derivative pricing. The principal purpose is to estimate the diffusion coefficient of an Itô process using a nonparametric Nadaraya–Watson kernel approach based on selective estimators of spot volatility proposed in the econometric literature, which are based on high frequency data. The accuracy of different spot volatility estimates is measured in terms of how accurately they can reproduce market option prices. To this aim, we fit a diffusion model to S&P 500 data, and successively, we use the calibrated model to price European call options written on the S&P 500 index. The estimation results are compared to well-known parametric alternatives available in the literature. Empirical results not only show that using intra-day data rather than daily provides better volatility estimates and hence smaller pricing errors, but also highlight that the choice of the spot volatility estimator has effective impact on pricing.     

Option pricing with an illiquid underlying asset market

We examine how price impact in the underlying asset market affects the replication of a European contingent claim. We obtain a generalized Black–Scholes pricing PDE and establish the existence and uniqueness of a classical solution to this PDE. Unlike the case with transaction costs, we prove that replication with price impact is always cheaper than superreplication. Compared to the Black–Scholes case, a trader generally buys more stock and borrows more (shorts and lends more) to replicate a call (put). Furthermore, price impact implies endogenous stochastic volatility and an out-of-money option has lower implied volatility than an in-the-money option. This finding has important implications for empirical analysis on volatility smile.     

Volatility forecasting in European government bond markets

In this paper we examine the predictive power of the heterogeneous autoregressive (HAR) model for the return volatility of major European government bond markets. The results from HAR-type volatility forecasting models show that past short- and medium-term volatility are significant predictors of the term structure of the intraday volatility of European bonds with maturities ranging from 1 year up to 30 years. When we decompose bond market volatility into its continuous and discontinuous (jump) component, we find that the jump component is a significant predictor. Moreover, we show that feedback from past short-term volatility to forecasts of future volatility is stronger in the days that precede monetary policy announcements.     

Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday

We examine the volatility spillovers among various industries during the COVID-19 pandemic period. We measure volatility spillovers by defining the volatility of each sector in the S&P 500 index and implement a static and rolling-window analysis following the Diebold and Yilmaz (2012) approach. We find that the pandemic enhanced volatility spillovers, which reveals the financial contagion effects on the US stock market. Second, there were sudden, large changes in the dynamic volatility spillovers on Black Monday (March 9, 2020), much of it due to the energy sector shock. These findings have important implications for portfolio managers and policymakers.     

Risk spillover and network connectedness analysis of China’s green bond and financial markets: Evidence from financial events of 2015–2020

This study investigated the dynamic return and volatility spillovers, together with the network connectedness analysis between China’s green bond and main financial markets. Based on a multidimensional DCC-GJRGARCH model and the spillover index method, we found significant two-way risk spillovers between the green bond market and traditional bond markets. Moreover, the green bond market was subject to one-way risk spillover from the stock and commodities markets. Meanwhile, risk spillovers between the green bond market, forex market, and monetary market were not significant. Finally, network connectedness analysis provided specific information about connectivity and strength during different subperiods corresponding to financial events. The analysis indicated that under the influence of emergencies, China’s financial market will enhance the risk-spillover level by transforming the same type of market’s internal spillover into cross-market spillover.     

Sustainable financing practices through green bonds: What affects the funding size?

Financing through the supply‐driven green bonds market has significantly surged in recent years. In this paper, we examine the factors influencing the size of financing though green bond supply, using cross‐section OLS regressions on a global dataset for 8 years (2010–2017) sourced from Bloomberg. We consider a set of tridimensional factors: bond characteristics, issuer characteristics, and market characteristics and examine their effects on issue size. Alongside whole sample estimation, we produce year‐wise estimations to realize the evolution and persistence of the effects over time. We then produce estimates across rating grades of the bonds. Finally, we carry Blinder–Oaxaca decomposition to see if average issue size has significantly changed over time and whether the factors considered can explain the difference. We find a large number of factors affecting issue size asymmetrically; however, many of the effects do not persist over time and are heterogeneous across rating grades. In contrast to the aggregate market trend, we find no evidence of increases in average issue size in the recent year. Furthermore, the average financing size is found significantly lower for high‐grade bonds. The paper provides a basis for encouraging green bond supply, particularly considering the rating of the bonds and the issuers.     

Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

This paper investigates the factors that drove the U.S. equity market returns from 2007 to early 2010. The period was highlighted by volatile energy and commodity prices, the collapse of insurance and banking firms, extreme implied volatility and a subsequent rally in the overall market. To extract the driving factors, we decompose the returns of the S&P500 sector ETFs into statistically independent signals using independent component analysis. We find that the generated factors have interesting financial interpretations and are consistent with the major economic themes of the period. We find that there are two sets of general market betas during the period along with a dominant factor for energy and materials sector. In addition, we find that the EGARCH model which accommodates asymmetric responses between returns and volatility can plausibly fit the high levels of variance during the crash. Finally, estimated correlations dropped when commodity prices moved higher, but then spiked when the S&P500 crashed in late 2008.     

How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF

In this paper, we empirically analyse infra-second datasets of the SPDR S&P 500 ETF (specifically, the ETF of the S&P 500 exchanged on BATS, named SPY.Z) in order to explain how high-frequency trading (HFT) activities (aggressive and passive) impact market volatility and the bid-ask spread before and after an exogenous shock (i.e., the 2016 US presidential election). Using SPDR S&P 500 ETF datasets as a proxy for the market on regular volume trading days (November 3, 2016) and on high-volume trading days (November 9, 2016), we show that HFT, on average, has a disturbing action mainly on regular volume trading days, whereas on high-volume trading days, it appears to have a stabilizing effect by balancing both the volatility and bid-ask spread. That is, HFT as a whole has a more neutral impact on the market’s volatility and bid-ask spread than the single aggressive and passive components. In fact, aggressive HFT has a consistent negative effect that increases, on average, both the volatility and bid-ask spread, whereas passive HFT displays a positive effect that decreases, on average, the volatility and bid-ask spread.     

What does the financial market pricing do? A simulation analysis with a view to systemic volatility,exuberance and vagary

Biondi et al. (Phys A 391(22):5532–5545, 2012) develop an analytical model to examine the emergent dynamic properties of share market price formation over time, capable to capture important stylized facts. These latter properties prove to be sensitive to regulatory regimes for fundamental information provision, as well as to market confidence conditions among actual and potential investors. We comparatively assess accounting models belonging to two main families: historical cost accounting and mark-to-market (fair value) accounting regimes. Regimes based upon mark-to-market measurement of traded security, while generating higher linear correlation between market prices and fundamental signals, also involve higher market instability and volatility. These regimes also incur more relevant episodes of market exuberance and vagary in some regions of the market confidence space, where lower market liquidity further occurs.     

Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock market

This paper studies the asymmetric spillover effect of important economic policy uncertainty (EPU) on the S&P500 index. We use monthly EPU indexes from Australia, Canada, China, Japan, the U.K. and the U.S. and the realized volatility of the U.S. stock market to study the asymmetric pairwise directional spillovers on the U.S. stock market from 2000 to 2019. We find that S&P500 index volatility is a net recipient of spillovers from important EPU indexes. Japanese EPU has the strongest spillover effect on the U.S. stock markets, while EPU from the U.K. plays a very limited role. By decomposing the volatility into good and bad volatility, we find that the relationship between bad stock market volatility and EPU is stronger than between good volatility and EPU. Time-varying spillover characteristics show that bad volatility reacts more strongly to shocks in EPU following the debt crisis and trade negotiations. Several robustness checks are provided to verify the novelty of these findings.     

Monetary integration in the Southern Cone

The effect of exchange-rate volatility on the domestic economy depends in part on the importance of trade in total economic activity. Unlike the European Union (EU), trade among the Mercosur countries is less highly integrated, so that movements in intra-area exchange rates are less important than exchange rates vis-à-vis the dollar and the euro. This paper analyzes the impact of exchange-rate and interest-rate volatility on investment and labor markets in the Southern Cone and finds that both volatility against the dollar and the euro and variability of interest rates have significant dampening effects on employment and investment.     

Foreign exchange market volatility in EU accession countries in the run-up to Euro adoption: weathering uncharted waters

The paper analyzes foreign exchange market volatility in four Central European EU accession countries in 2001–2003. By using a Markov regime-switching model, it identifies two regimes representing high- and low-volatility periods. The estimation results show not only that volatilities are different between the two regimes, but also that some of the cross-correlations differ. Notably, cross-correlations increase substantially for two pairs of currencies (the Hungarian forint–Polish zloty and the Czech koruna–Slovak koruna) in the high-volatility period. The paper concludes by discussing the policy implications of these findings.     

Contagion testing in frontier markets under alternative stressful S&P 500 market scenarios

We use alternative approaches to identify stable and stressful scenarios in the S&P 500 market, to offer a new perspective for constructing contagion tests in recipient frontier markets vulnerable to disturbances from this source market. The S&P 500 market is decomposed into discrete conditions of: (1) tranquil versus turbulent volatility; (2) bull versus bear market phases; (3) normal periods versus asset bubbles and crashes. Based on these identified scenarios, we use various co-moment contagion tests to analyse the changing relationship between the S&P 500 market and major frontier markets in the Caribbean region that have prominent trade related exposure to the US. Our findings show that, outside of the events of the Great Recession, the Caribbean stock exchanges are largely independent of the S&P 500 market.