Forward-looking portfolio selection with multivariate non-Gaussian models

In this study, we suggest a portfolio selection framework based on time series of stock log-returns, option-implied information, and multivariate non-Gaussian processes. We empirically assess a multivariate extension of the normal tempered stable (NTS) model and of the generalized hyperbolic (GH) one by implementing an estimation method that simultaneously calibrates the multivariate time series of log-returns and, for each margin, the univariate observed one-month implied volatility smile. To extract option-implied information, the connection between the historical measure P and the risk-neutral measure Q, needed to price options, is provided by the multivariate Esscher transform. The method is applied to fit a 50-dimensional series of stock returns, to evaluate widely known portfolio risk measures and to perform a forward-looking portfolio selection analysis. The proposed models are able to produce asymmetries, heavy tails, both linear and non-linear dependence and, to calibrate them, there is no need for liquid multivariate derivative quotes.     

Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests

We examine whether the dynamics of the implied volatility surface of individual equity options contains exploitable predictability patterns. Predictability in implied volatilities is expected due to the learning behavior of agents in option markets. In particular, we explore the possibility that the dynamics of the implied volatility surface of individual stocks may be associated with movements in the volatility surface of S&P 500 index options. We present evidence of strong predictable features in the cross-section of equity options and of dynamic linkages between the volatility surfaces of equity and S&P 500 index options. Moreover, time-variation in stock option volatility surfaces is best predicted by incorporating information from the dynamics in the surface of S&P 500 options. We analyze the economic value of such dynamic patterns using strategies that trade straddle and delta-hedged portfolios, and find that before transaction costs such strategies produce abnormal risk-adjusted returns.     

Price Limits and Stock Market Volatility in the Athens Stock Exchange

In this paper, we have examined the effects of price limits on the stock volatility in the Athens Stock Exchange. We put forward two hypotheses, the information hypothesis, which implies that price limits only slow down the process of adjustment and have no effect on stock volatility; and the over-reaction hypothesis, which assumes that investors tend to overreact to new information, so that price limits give them time to reassess the information and reduce stock volatility. Our results show strong support for the information hypothesis. This evidence is obtained by performing the tests on ten stocks, which include heavily traded stocks as well as less active stocks, and covering a variety of industries, and on a market wide price index. The results are also robust to the frequency of the measurement of the returns, and to the tightness of the limits.     

Asymmetric Correlation and Volatility Dynamics among Stock, Bond, and Securitized Real Estate Markets

We apply a multivariate asymmetric generalized dynamic conditional correlation GARCH model to daily index returns of S&P500, US corporate bonds, and their real estate counterparts (REITs and CMBS) from 1999 to 2008. We document, for the first time, evidence for asymmetric volatilities and correlations in CMBS and REITs. Due to their high levels of leverage, REIT returns exhibit stronger asymmetric volatilities. Also, both REIT and stock returns show strong evidence of asymmetries in their conditional correlation, suggesting reduced hedging potential of REITs against the stock market downturn during the sample period. There is also evidence that corporate bonds and CMBS may provide diversification benefits for stocks and REITs. Furthermore, we demonstrate that default spread and stock market volatility play a significant role in driving dynamics of these conditional correlations and that there is a significant structural break in the correlations caused by the recent financial crisis.     

The dynamics of volatility transmission and information flow between ADRs and their underlying stocks

This research examines the dynamics of volatility transmission and information flow between ADRs and the underlying stocks. Using a bivariate GARCH model with BEKK parameterisation, the study investigates how changes in volatility in the ADR market affect the volatility in the underlying equity market and vice versa. The findings suggest a bidirectional volatility transmission and information flow between the ADR and underlying stock markets. ADRs and underlying stocks respond to their own innovations as well as to the innovations in each other's market. The findings are consistent for all countries in the sample as well as for different sub-periods. The evidence suggests that the differences in synchronicity of trading period between the US market and other developed markets included in the sample has had no effect on the volatility transmission and information flow between ADRs and underlying stocks.     

Individual Investors and Volatility

We show that retail trading activity has a positive effect on the volatility of stock returns, which suggests that retail investors behave as noise traders. To identify this effect, we use a reform of the French stock market that raises the relative cost of speculative trading for retail investors. The daily return volatility of the stocks affected by the reform falls by 20 basis points (a quarter of the sample standard deviation of the return volatility) relative to other stocks. For affected stocks, we also find a significant decrease in the magnitude of return reversals and the price impact of trades.     

Index Futures Trading and Stock Return Volatility: Evidence from the Introduction of Mid Cap 400 Index Futures

The MidCap 400 stock index is used to provide new evidence on the relation between stock index futures trading and stock return volatility. The study documents a significant decrease in return volatility and systematic risk, and a significant increase in trading volume for the MidCap 400 stocks after the introduction of the MidCap index. A control sample of medium-capitalization stocks, however, exhibits similar contemporaneous changes in these measures. The MidCap stocks and the control stocks also experience a significant decrease in volatility and an increase in volume after the introduction of MidCap 400 index futures. Thus, the study finds no difference in the behavior of the MidCap 400 stocks and the control stocks and no evidence of a relation between index futures trading and volatility in the stock market.     

A high-low-based omnibus test for symmetry, the Lévy property, and other hypotheses on intraday returns

We consider different models for intraday log-returns: Lévy models, symmetric models, and Lévy processes subjected to independent continuous time-changes. For these models, we show bivariate interchangeability of intraday up- and downside volatility ratios which are built using daily high-low prices. Using conditional inference permutation tests on bivariate interchangeability, we develop an omnibus test for the above-mentioned models. Empirically, we find strong evidence against intraday returns belonging to these model classes, as we reject bivariate interchangeability of the volatility ratios for half of the components of the DJIA, two thirds of the S&P 500 shares and almost all stocks of the German DAX.     

Media sentiment and short stocks performance during a systemic crisis

During crisis periods, investors often engage in short selling of stocks, in line with their pessimistic view of the present and future market performance as well as with the hope to repurchase the stocks back in the future at much lower prices. This attitude not only affects stock returns, but also may lead to significant risk transmission among assets. Addressing this concern, our study examines the returns and volatility connectedness between media coverage index (MCI) and high short interest stocks during the recent Covid-19 pandemic. We document MCI as a net transmitter for all returns series, whereas the results for volatility series exhibits binary behavior, acting as either a transmitter or recipient depending on the considered sector of economic activity. We highlight that the healthcare and energy sector stocks behave as net recipients of both, returns and volatility; hence, a certain caution is required while including them in investment portfolios. Finally, the causality test indicates that the MCI is more strongly connected with stock returns than with volatilities, thus signaling that media, may not only provoke a rise in stock volatility, but cause intense risk transmission especially during a systemic crisis similar to Covid-19.     

Does Idiosyncratic Risk Really Matter?

Goyal and Santa‐Clara (2003) find a significantly positive relation between the equal‐weighted average stock volatility and the value‐weighted portfolio returns on the NYSE/AMEX/Nasdaq stocks for the period of 1963:08 to 1999:12. We show that this result is driven by small stocks traded on the Nasdaq, and is in part due to a liquidity premium. In addition, their result does not hold for the extended sample of 1963:08 to 2001:12 and for the NYSE/AMEX and NYSE stocks. More importantly, we find no evidence of a significant link between the value‐weighted portfolio returns and the median and value‐weighted average stock volatility.     

Stock return volatility,operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly

This study highlights the link between stock return volatility, operating performance, and stock returns. Prior studies suggest that there is a ‘low volatility’ anomaly, where firms with a low stock return volatility out-perform firms with a high stock return volatility. This paper confirms that low volatility stocks earn higher returns than high volatility stocks in emerging markets and developed markets outside of North America. We also show that low volatility stocks have higher operating returns and this might explain why low volatility stocks earn higher stock returns. These results provide a partial explanation for the ‘low volatility effect’ that is independent from the existence of market anomalies or per se inefficiencies that might otherwise drive a low volatility effect. We emphasize the importance of controlling for stock return volatility when analyzing operating performance and stock performance.     

The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility

Does cross-sectional dispersion in the returns of different stocks help forecast volatility of the S&P 500 index? This paper develops a model of stock returns where dispersion in returns across different stocks is modeled jointly with aggregate volatility. Although specifications that allow for feedback from cross-sectional dispersion to aggregate volatility have a better fit in sample, they prove not to be robust for purposes of out-of-sample forecasting. Using a full cross-section of stock returns jointly, however, I find that use of cross-sectional dispersion can help improve parameter estimates of a GARCH process for aggregate volatility to generate better forecasts both in sample and out of sample. Given this evidence, I conclude that cross-sectional information helps predict market volatility indirectly rather than directly entering in the data-generating process.     

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

Tempered stable and tempered infinitely divisible GARCH models

In this paper, we introduce a new GARCH model with an infinitely divisible distributed innovation. This model, which we refer to as the rapidly decreasing tempered stable (RDTS) GARCH model, takes into account empirical facts that have been observed for stock and index returns, such as volatility clustering, non-zero skewness, and excess kurtosis for the residual distribution. We review the classical tempered stable (CTS) GARCH model, which has similar statistical properties. By considering a proper density transformation between infinitely divisible random variables, we can find the risk-neutral price process, thereby allowing application to option-pricing. We propose algorithms to generate scenarios based on GARCH models with CTS and RDTS innovations. To investigate the performance of these GARCH models, we report parameter estimates for the Dow Jones Industrial Average index and stocks included in this index. To demonstrate the advantages of the proposed model, we calculate option prices based on the index.     

How Do Crises Spread? Evidence from Accessible and Inaccessible Stock Indices

We provide empirical evidence that stock market crises are spread globally through asset holdings of international investors. By separating emerging market stocks into two categories, namely, those that are eligible for purchase by foreigners (accessible) and those that are not (inaccessible), we estimate and compare the degree to which accessible and inaccessible stock index returns co‐move with crisis country index returns. Our results show greater co‐movement during high volatility periods, especially for accessible stock index returns, suggesting that crises spread through the asset holdings of international investors rather than through changes in fundamentals.     

The behavior of stock returns in the mining industry following the Iraq war

The aim of this article is to characterize the dynamics of stock returns of 10 leading mining firms over a politically unstable period, marked by 9/11 and the subsequent invasion of Iraq. To that end, we analyze the evolution of return volatility over time, examine the dynamics of volatility persistence, and test for the presence of volatility shifts. We also examine whether volatility and trading volume obey the one-factor mixture-of-distribution hypothesis (MDH). Finally, we analyze whether the performance of mining stock returns may be influenced by the evolution of the energy sector. The results suggest that firms which belong to the same industry did not necessarily exhibit identical patterns of return volatility. Secondly, shocks to volatility and volume are in general dynamically asymmetric, which violates the one-factor MDH. Thirdly, the metals and minerals analyzed exhibited different degrees of dependency on energy prices.     

Asset Price Dynamics and Infrequent Feedback Trades

This article combines the continuous arrival of information with the infrequency of trades, and investigates the effects on asset price dynamics of positive and negative-feedback trading. Specifically, we model an economy where stocks and bonds are traded by two types of agents: speculators who maximize expected utility, and feedback traders who mechanically respond to price changes and infrequently submit market orders. We show that positive-feedback strategies increase the volatility of stock returns, and the response of stock prices to dividend news. Conversely, the presence of negative-feedback traders makes stock returns less volatile, and prices less responsive to dividends.     

Does futures trading increase stock market volatility? The case of the Nikkei stock index futures markets

We propose new tests to examine whether stock index futures affect stock market volatility. These tests decompose spot portfolio volatility into the cross-sectional dispersion and the average volatility of returns on the portfolio's constituent securities. Our tests show that for Nikkei stocks spot portfolio volatility increased and cross-sectional dispersion decreased compared with average volatility when Nikkei futures began trading on the Osaka Securities Exchange, but not on the Singapore International Monetary Exchange. For non-Nikkei stocks, no shift occurred when futures trading began on either exchange. These findings are consistent with the hypotheses that futures trading increases spot portfolio volatility but that there is no volatility “spillover” to stocks against which futures are not traded. However, the increase in volatility attributable to futures trading is small compared with volatility shifts induced by changes in broad economic factors.     

Asset market linkages: Evidence from financial, commodity and real estate assets

We use a general Markov switching model to examine the relationships between returns over three different asset classes: financial assets (US stocks and Treasury bonds), commodities (oil and gold) and real estate assets (US Case-Shiller index). We confirm the existence of two distinct regimes: a “tranquil” regime with periods of economic expansion and a “crisis” regime with periods of economic decline. The tranquil regime is characterized by lower volatility and significantly positive stock returns. During these periods, there is also evidence of a flight from quality - from gold to stocks. By contrast, the crisis regime is characterized by higher volatility and sharply negative stock returns, along with evidence of contagion between stocks, oil and real estate. Furthermore, during these periods, there is strong evidence of a flight to quality - from stocks to Treasury bonds.