Correlations and volatility spillovers across commodity and stock markets: Linking energies,food, and gold

This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets.     

Score-driven Markov-switching EGARCH models: an application to systematic risk analysis

We introduce new Markov-switching (MS) dynamic conditional score (DCS) exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models, to be used by practitioners for forecasting value-at-risk (VaR) and expected shortfall (ES) in systematic risk analysis. We use daily log-return data from the Standard & Poor’s 500 (S&P 500) index for the period 1950–2016. The analysis of the S&P 500 is useful, for example, for investors of (i) well-diversified US equity portfolios; (ii) S&P 500 futures and options traded at Chicago Mercantile Exchange Globex; (iii) exchange traded funds (ETFs) related to the S&P 500. The new MS DCS-EGARCH models are alternatives to of the recent MS Beta-t-EGARCH model that uses the symmetric Student’s t distribution for the error term. For the new models, we use more flexible asymmetric probability distributions for the error term: Skew-Gen-t (skewed generalized t), EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverse Gaussian) distributions. For all MS DCS-EGARCH models, we identify high- and low-volatility periods for the S&P 500. We find that the statistical performance of the new MS DCS-EGARCH models is superior to that of the MS Beta-t-EGARCH model. As a practical application, we perform systematic risk analysis by forecasting VaR and ES.

Abbreviation Single regime (SR); Markov-switching (MS); dynamic conditional score (DCS); exponential generalized autoregressive conditional heteroscedasticity (EGARCH); value-at-risk (VaR); expected shortfall (ES); Standard & Poor's 500 (S&P 500); exchange traded funds (ETFs); Skew-Gen-t (skewed generalized t); EGB2 (exponential generalized beta of the second kind); NIG (normal-inverse Gaussian); log-likelihood (LL); standard deviation (SD); partial autocorrelation function (PACF); likelihood-ratio (LR); ordinary least squares (OLS); heteroscedasticity and autocorrelation consistent (HAC); Akaike information criterion (AIC); Bayesian information criterion (BIC); Hannan-Quinn criterion (HQC).     

Determinants of the rate of return on S&;L assets: 1970–97

This study identifies determinants of the rate of return on U.S. Saving and Loan (S&L) assets during the 1970–97 period. The Instrumental Variables (IV) estimation reveals that this rate of return is an increasing function of the spread between the S&L mortgage interest rate and the S&L cost of funds, the regulatory S&L capital-asset ratio, and the percentage growth rate of real GDP. It is negatively affected by the Tax Reform Act of 1986 and positively affected by the Federal Deposit Insurance Corporation Improvement Act of 1991. Based on these findings, certain policy implications and general conclusions are suggested.     

Determinants of bank failures in the US revisited

This study investigates factors influencing the bank failure rate in the United States over the period 1970 to 2007. The bank failure rate was found to be an increasing function of the unemployment rate, the average cost of funds, volatility of the S&P 500 Stock Index, and charge-offs as a percentage of outstanding loans and a decreasing function of the mortgage rate on new 30-year fixed-rate mortgages. The evidence implies also that the Federal Deposit Insurance Corporation Improvement Act acted to reduce bank failures whereas the Riegle–Neal Interstate Banking Act of 1994 may inadvertently have (by increasing competition and/or increasing costs through branch bank expansion) induced increased bank failures.     

Why don’t you trade only four days a year? An empirical study into the abnormal returns of quarters first trading day

In this research I examined a calendar anomaly that occurs at the beginning of each quarter. Through an examination of 34 years of daily and annual returns for the S&P500 and 13 years of returns for popular ETFs, I have demonstrated the existence of the First Day of Quarter (FDQ) effect. By trading only four days a year from the beginning of 2000 until the end of 2013, an investor could have gained 113.1% of the S&P500 returns for that period, while being exposed to stock risk for only 56 days. Moreover, for 11 of those 14 years of trading, the FDQ was responsible for more than 10% of the annual returns. Only for two years since 2000 (2001, 2005) has the FDQ yielded a negative return. The biggest beneficiary of the FDQ is the financial sector, which for the last 13 years of investing has been non-fertile, showing −6.12% total return. Investing only at the beginning of each quarter for a total of 52 days would have yielded a return of 40.17%. The next beneficiary of the FDQ is the technological sector. The 82.5% of total return gained in this sector over the last 13 years could have been gained in only 52 days of trading.     

Sustainable competitive advantage and stock performance: the case for wide moat stocks

‘In business, I look for economic castles protected by unbreachable “Moats”’. Warren Buffett Companies that have sustainable competitive advantages should be able to create a barrier (Moat) to prevent or lessen competition from other firms. The wider the Moat the greater the barrier and the more secure the company’s profitability. Using the Morningstar classification of ‘Wide Moat’ stocks, we construct annually rebalanced equal- and value-weighted portfolios to analyse their performance in order to determine if they deliver superior performance relative to standard benchmark portfolios. The period for our analysis extends from June 2002 through May 2014. We find that the ‘Wide Moat’ portfolios outperform both the S&P 500 and Russell 3000 indices generating higher average monthly and annualized returns, Sharpe Ratio, Sortino Ratio, Treynor Ratio, Omega Ratio, Upside Potential Ratio, M², M² Alpha, and cumulative returns. When we compute alpha using Carhart four-factor and Fama–French five-factor models, we find that ‘Wide Moat’ portfolios had significantly positive risk-adjusted alphas with both the models. ‘Wide Moat’ portfolios also lost less value during the 2007–2009 financial crisis compared to both S&P 500 and Russell 3000. In conclusion, we find that ‘Wide Moat’ stocks have created significant value for their investors over the course of our study.     

QARMA-Beta-t-EGARCH versus ARMA-GARCH: an application to S&P 500

Statistical performance and out-of-sample forecast precision of ARMA-GARCH and QARMA-Beta-t-EGARCH are compared. We study daily returns on the Standard and Poor’s 500 (S&P 500) index and a random sample of 50 stocks from the S&P 500 for period May 2006 to July 2010. Competing models are estimated for periods before and during the US financial crisis of 2008. Out-of-sample point and density forecasts are performed for periods during and after the US financial crisis. The results provide evidence of the superior in-sample statistical and out-of-sample predictive performance of QARMA-Beta-t-EGARCH.     

Tail dependence analysis of stock markets using extreme value theory

Financial risk modelling frequently uses the assumption of a normal distribution when considering the return series which is inefficient if the data is not normally distributed or if it exhibits extreme tails. Estimation of tail dependence between financial assets plays a vital role in various aspects of financial risk modelling including portfolio theory and hedging amongst applications. Extreme Value Theory (EVT) provides well established methods for considering univariate and multivariate tail distributions which are useful for forecasting financial risk or modelling the tail dependence of risky assets. The empirical analysis in this article uses nonparametric measures based on bivariate EVT to investigate asymptotic dependence and estimate the degree of tail dependence of the ASX-All Ordinaries daily returns with four other international markets, viz., the S&P-500, Nikkei-225, DAX-30 and Heng-Seng for both extreme right and left tails of the return distribution. It is investigated whether the asymptotic dependence between these markets is related to the heteroscedasticity present in the logarithmic return series using GARCH filters. The empirical evidence shows that the asymptotic extreme tail dependence between stock markets does not necessarily exist and rather can be associated with the heteroscedasticity present in the financial time series of the various stock markets.     

Markov regime-switching Beta-t-EGARCH

We suggest a Markov regime-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model for U.S. stock returns. We compare the in-sample statistical performance of the MS Beta-t-EGARCH model with that of the single-regime Beta-t-EGARCH model. For both models we consider leverage effects for conditional volatility. We use data from the Standard Poor’s 500 (S&P 500) index and also a random sample that includes 50 components of the S&P 500. We study the outlier-discounting property of the single-regime Beta-t-EGARCH and MS Beta-t-EGARCH models. For the S&P 500, we show that for the MS Beta-t-EGARCH model extreme observations are discounted more for the low-volatility regime than for the high-volatility regime. The conditions of consistency and asymptotic normality of the maximum likelihood estimator are satisfied for both the single-regime and MS Beta-t-EGARCH models. All likelihood-based in-sample statistical performance metrics suggest that the MS Beta-t-EGARCH model is superior to the single-regime Beta-t-EGARCH model. We present an application to the out-of-sample density forecast performance of both models. The results show that the density forecast performance of the MS Beta-t-EGARCH model is superior to that of the single-regime Beta-t-EGARCH model.     

Are U.S. growth and value stocks similarly integrated with the world markets? A test across business cycles

Utilizing the bivariate GARCH-in-mean methodology, this study examines the strength of global risk premia using 10 major foreign stock markets with two style-based, large-cap U.S. index funds and S&P500, for the period 1993–2014. We incorporated seven U.S. business cycles. The foreign risk premium was found to be significantly strong for both growth and value stocks, and the S&P500 index, indicating that U.S. integration within global market is strong and persistent over the past 20 years. We report distinct risk characteristics owing to global linkages, for the two style-based U.S. funds over different business cycles. The foreign risk premium for growth stocks is mostly positive and especially high during contractions; in contrast, the value stocks demand more premiums during expansions. The growth and value linkages with foreign countries also vary quite substantially over the business cycles. A possible sign of convergence is the decreasing difference between value and growth foreign risk premiums, post-2001, perhaps indicative of greater domestic and global market integration. Our results support a solid, continuing trend of U.S. integration within global markets, with an influential role of business cycles.     

A reliable performance measure to differentiate China’s actively managed open-end equity mutual funds

We compare different fund performance measures to examine which performance measures can generate risk-adjusted returns between high ranked and low ranked China’s actively managed open-end equity mutual funds. Our results show that only the six-factor (five factors (market, size, b/m, profitability & Investment facotrs) plus a momentum factor) alpha as the performance measure meets the criteria. Separated by the six-factor alpha, better performing funds have a larger asset under management, a better past 6-month cumulative return, a better stock picking ability, and a higher percentage of hybrid funds. Through our sample period from July 2004 to December 2015, the highest ranked quintile funds generate a monthly risk-adjusted return of 0.24% more than the lowest ranked quintile funds and the six-factor alpha reliably selects a better fund portfolio in both bear and bull markets on the basis of both fund return and holding data. Furthermore, our results from fund trading data show that funds with the highest six-factor alpha rank demonstrate a better trading skill in bear markets, suggesting that those better performing funds exhibit their market timing and stock picking abilities when investors need them most.     

Value at risk forecasting for volatility index

Forecasts of values at risk (VaRs) are made for volatility indices such as the VIX for the US S&P 500 index, the VKOSPI for the KOSPI (Korea Stock Price Index) and the OVX (oil volatility index) for crude oil funds, which is the first in the literature. In the forecasts, dominant features of the volatility indices are addressed: long memory, conditional heteroscedasticity, asymmetry and fat-tails. An out-of-sample comparison of the VaR forecasts is made in terms of violation probabilities, showing better performance of the proposed method than several competing methods which consider the features differently from ours. The proposed method is composed of heterogeneous autoregressive model for the mean, GARCH model for the volatility and skew-t distribution for the error.     

An information theoretic analysis of stock returns,volatility and trading volumes

Information theory is used to examine the dynamic relationships between stock returns, volatility and trading volumes for S&P500 stocks. This provides an alternative approach to traditional Granger causality tests when dealing with nonlinear relationships. The article highlights the dominant role played by trading volumes in all of these relationships – even in the return–volatility relation – and finds evidence of a market level feedback effect from index returns to the return–volatility relation at the stock level. The article also produces a number of stylized facts from an information theoretic perspective.     

Testing volatility persistence on Markov switching stochastic volatility models

In the literature, some researchers found that the high persistence of the volatility can be caused by Markov regime switching. This concern can be reflected as a unit root problem on the basis of Markov switching models. In this paper, our main purpose is to provide a Bayesian unit root testing approach for Markov switching stochastic volatility (MSSV) models. We illustrate the developed approach using S&P 500 daily return covering the subprime crisis started in 2008.     

The intraday relation between return volatility, transactions, and volume

In this article, we examine the relation between return volatility, average trade size, and the frequency of transactions using transaction data. Consistent with Jones, Kaul, and Lipson (1994)(. Review of Financial Studies, 7, 631–651), our results show that the frequency of trades has a high explanatory power for return volatility. However, contrary to their finding, we find that average trade size contains nontrivial information for return volatility. The positive relation between return volatility and average trade size is more significant for actively traded stocks. Furthermore, return volatility exhibits significant intraday variations. It is found that the effect of trade frequency on return volatility is much stronger in the opening trading period.     

When Do Opening Stock Returns Tend to be Higher?

Abstract

In this study, I make an effort to formulate a trading rule that would make use of some systematic interday patterns in individual stocks’ opening returns. I analyze intraday price data on all the stocks that were S&P 500 Index constituents during the period from 1993 to 2012. I document that if the general market direction of the previous day's opening session is controlled for, then a stock's opening return tends to be higher if, on the previous trading day, its opening return was relatively high (either positive, or higher than the same day's opening market return) and its open-to-close return was relatively low (either non-positive, or lower than or equal to the same day's open-to-close market return). Finally, for the sampling period, I construct two different investment portfolios involving a long position in the stocks on the days when, according to the findings, their opening returns are expected to be high and a short position in the stocks on the days when, according to the findings, their opening returns are expected to be low. Both portfolios are found to yield significantly positive returns, providing evidence for the practical applicability of the documented patterns in opening stock prices.     

Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S.

We propose three Realized-GARCH-Kernel-type models which do not make the distribution assumptions on the return disturbance terms. We use this type of model to predict the return volatilities of the 50ETF in China and the S&P500 index in the U.S. The semiparametric kernel density estimator of our models, which captures the skewness, asymmetry and fat-tail of financial assets, performs well both statistically and economically. Our models have more predictive power than other eight comparable volatility models that need to pre-specify the distribution of the disturbance terms. Our results are robust to eight measures of realized volatility. Using option straddle strategies, we show that our models generate larger trading profits and greater Sharpe ratios than the other competing models.     

Dynamic conditional score models of degrees of freedom: filtering with score-driven heavy tails

This article extends the quasi-autoregressive (QAR) plus Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) dynamic conditional score (DCS) model. For the new DCS model, the degrees of freedom parameter is time varying and tail thickness of the error term is updated by the conditional score. We compare the performance of QAR plus Beta-t-EGARCH with constant degrees of freedom (benchmark model) and QAR plus Beta-t-EGARCH with time-varying degrees of freedom (extended model). We use data from the Standard and Poor’s 500 (S&P 500) index, and a random sample of its 150 components that are from different industries of the United States (US) economy. For the S&P 500, all likelihood-based model selection criteria support the extended model, which identifies extreme events with significant impact on the US stock market. We find that for 59% of the 150 firms, the extended model has a superior statistical performance. The results suggest that the extended model is superior for those industries, which produce products that people usually are unwilling to cut out of their budgets, regardless of their financial situation. We perform an application to compare the density forecast performance of both DCS models. We perform an application to Monte Carlo value-at-risk for both DCS models.     

S&P 500 Affiliation and Stock Price Informativeness

Abstract

When firms are added to a stock index, more information should be discovered, traded on, and incorporated into their stock prices, making them more informative. We test this hypothesis using a large sample of additions to the S&P 500 index. Using two alternative statistical tests, we find that the stocks added experience more random, less predictable return and, thus, appear to be priced more efficiently information-wise. We further find concurrent increases in institutional ownership and investor awareness, which tend to contribute to the higher pricing efficiency, adding to the literature. These findings should be of interest to academics and practitioners.     

Adoption of Pollution Prevention Techniques: The Role of Management Systems and Regulatory Pressures

This paper investigates the extent to which firm level technological change that reduces unregulated emissions is driven by regulatory pressures, and firms’ technological and organizational capabilities. Using a treatment effects model with panel data for a sample of S&P 500 firms over the period 1994–1996, we find that organizational change in the form of Total Quality Environmental Management leads firms to adopt pollution prevention practices, after controlling for the effects of various regulatory pressures and firm-specific characteristics. We find that the threat of anticipated regulations and the presence of ‘complementary assets’ is important for creating the incentives and an internal capacity to undertake incremental adoption of pollution prevention techniques.