Forecasting volatility with time-varying leverage and volatility of volatility effects

Predicting volatility is of primary importance for business applications in risk management, asset allocation, and the pricing of derivative instruments. This paper proposes a measurement model that considers the possibly time-varying interaction of realized volatility and asset returns according to a bivariate model to capture its major characteristics: (i) the long-term memory of the volatility process, (ii) the heavy-tailedness of the distribution of returns, and (iii) the negative dependence of volatility and daily market returns. We assess the relevance of the effects of “the volatility of volatility” and time-varying “leverage” to the out-of-sample forecasting performance of the model, and evaluate the density of forecasts of market volatility. Empirical results show that our specification can outperform the benchmark HAR–GARCH model in terms of both point and density forecasts.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.     

OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach

This paper provides a novel perspective to the predictive ability of OPEC meeting dates and production announcements for (Brent Crude and West Texas Intermediate) oil futures market returns and GARCH-based volatility using a nonparametric quantile-based methodology. We show a nonlinear relationship between oil futures returns and OPEC-based predictors; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. When the quantile-causality test is implemented, we observe that the impact of OPEC variables is restricted to Brent Crude futures only (with no effect observed for the WTI market). Specifically, OPEC production announcements, and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market returns. While, predictability of volatility covers the majority of the quantile distribution, barring extreme ends.     

Anomalies in emerging markets: The case of Mexico

In this article we explore the relationship between 19 of the most common anomalies reported for the US market and the cross-section of Mexican stock returns. We find that 1-month stock returns in Mexico are robustly predicted only by 3 of the 19 anomalies: momentum, idiosyncratic volatility, and the lottery effect. Momentum has a positive relation with future 1-month returns, while idiosyncratic volatility and the lottery effect have a negative relation. For longer horizons of 3 and 6 months, only the 3 most important factors in the US market predict returns: size, book-to-market, and momentum.     

Economic policy uncertainty,oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.     

Lottery mindset,mispricing and idiosyncratic volatility puzzle: Evidence from the Chinese stock market

This study investigates the MAX effect regarding lottery mindset in the Chinese stock market. The MAX effect significantly affects stock returns through quintile portfolio and cross-sectional regression analyses. The most-overpriced stock groups, as categorized by mispricing index, show more support for the MAX effect. However, the idiosyncratic volatility (IVOL) effect continues regardless of consideration for the MAX effect, indicating that the MAX effect is not a source of the IVOL effect. Our results suggest that the MAX effect, which is highly relevant for overpriced stocks, might have information for determining stock price, and appears to be independent from information of the IVOL effect in the Chinese stock market.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.     

Tax aggressiveness and idiosyncratic volatility

This study finds that aggressive tax strategies adopted by a firm affect idiosyncratic stock return volatility. Aggressive tax strategies, which I measure as tax paid by a firm divided by pretax income (adjusted for special items), are associated with higher levels of idiosyncratic stock volatility. Uncertainty associated with tax strategies may result due to several factors, such as penalties, fines, and additional tax payments if particular tax strategies are disallowed by taxation authorities, or if there are changes in tax rules. Such uncertainty affects the future cash flows of a firm and is reflected in more volatile stock returns. Financial constraints, corporate governance mechanisms, and information environments surrounding a firm influence the relation between idiosyncratic volatility and effective tax rates.     

Modeling common volatility characteristics and dynamic risk premia in European equity markets

The paper applies a Factor-GARCH model to evaluate the impact of the market portfolio, as a single common dynamic risk factor, on conditional volatility and risk premia for the returns on size-based equity portfolios of three major European markets; France, Germany and the United Kingdom. The results show that for the size-based portfolios the factor loading for the dynamic market factor is significant and positive but the association between the risk premia and the conditional market volatility is weak. However, the dynamic market factor is shown to explain common characteristics in the conditional variance such as asymmetry and persistence. This finding is consistent across markets and portfolio sizes.     

Positive IVOL-MAX effect: A study on the Singapore Stock Market

This paper demonstrates a positive and significant IVOL effect in the Singapore Stock Market meaning that the highly volatile stocks are showing better returns in the subsequent month. More explicitly, there is a strong positive relationship between stock’s idiosyncratic volatility (IVOL) and its subsequent month’s return in the Singapore equity market. This positive IVOL effect is stronger only for small market-statistic firms. But for the Large capital firms, the positive IVOL effect is insignificant. In addition, this paper shows that the relationship between maximum daily return over a month (MAX) and the subsequent month’s return is positive and significant in this market. However, IVOL is the true effect of this market rather than MAX.     

Forecasting risk in the US Dollar exchange rate under volatility shifts

Recent evidence suggests that volatility shifts (i.e. structural breaks in volatility) in returns increases kurtosis which significantly contributes to the observed non-normality in market returns. In this paper, we endogenously detect significant shifts in the volatility of US Dollar exchange rate and incorporate this information to estimate Value-at-Risk (VaR) to forecast large declines in the US Dollar exchange rate. Our out-of-sample performance results indicate that a GARCH model with volatility shifts produces the most accurate VaR forecast relative to several benchmark methods. Our contribution is important as changes in US Dollar exchange rate have a substantial impact on the global economy and financial markets.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Cross-sectional implications of dynamic asset pricing with stochastic volatility and ambiguity aversion

Building on recent research that highlights the importance of macroeconomic volatility and ambiguity aversion in explaining the dynamics of stock returns, in this paper we propose a dynamic asset pricing model that simultaneously accounts for stochastic macroeconomic volatility and ambiguity, assuming that investors deal with uncertainty about the mechanics of macroeconomic fluctuations using first-release consumption and revisions to aggregate consumption on vintage data. Our results show that the proposed model captures a large fraction of the cross-sectional variation of excess returns for a wide range of market anomaly portfolios. Furthermore, while the price of risk for ambiguity is positive and significant for the vast majority of assets under study, macroeconomic volatility yields ambiguous outcomes, although it significantly increases the explanatory power of the model for specific assets. Our results suggest that macroeconomic volatility and ambiguity complement each other in explaining the cross-sectional behavior of stock returns.     

Towards a unified framework for high and low frequency return volatility modeling

This paper provides a selective summary of recent work that has documented the usefulness of high-frequency, intraday return series in exploring issues related to the more commonly studied daily or lower-frequency returns. We show that careful modeling of intraday data helps resolve puzzles and shed light on controversies in the extant volatility literature that are difficult to address with daily data. Among other things, we provide evidence on the interaction between market microstructure features in the data and the prevalence of strong volatility persistence, the source of significant day-of-the-week effect in daily returns, the apparent poor forecast performance of daily volatility models, and the origin of long-memory characteristics in daily return volatility series.     

Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis

We investigate how sensitive developed and emerging equity markets are to volatility dynamics of Bitcoin during tranquil, bear, and bull market regimes. Intraday price fluctuations of Bitcoin are represented by three measures of realized volatility, viz. total variance, upside semivariance, and downside semivariance. Our empirical analysis relies on a quantile regression framework, after orthogonalizing raw returns with respect to an array of relevant global factors and accounting for structural shifts in the series. The results suggest that developed-market returns are positively related to the realized variance proxy across various market conditions, while emerging-market returns are positively (negatively) correlated with realized variance during bear (normal and bull) market periods. The upside (downside) component of realized variance has a negative (positive) influence on returns of either market category, and the dependence structure is highly asymmetric across the return distribution. Additionally, we document that developed and emerging markets are more sensitive to downside volatility than to upside volatility when they enter tranquil or bull territory. Our results offer practical implications for policymakers and investors.     

Does financial integration affect real exchange rate volatility and cross-country equity market returns correlation?

Existing empirical studies show that financial integration affects the behavior of average excess returns, cross-country equity market returns (EMR) correlations and real exchange rate (RER) volatility. We employ a recently developed two-country model with recursive preferences, frictionless and complete markets and highly correlated long-run innovations to examine whether full financial integration (i.e. full risk-sharing) affects the US-Canada EMR correlation and the US RER volatility, consistently with existing empirical findings. First, full risk-sharing gives rise to a relatively high RER volatility. Second, it induces very strong positive cross-country EMR correlations. Both quantities are higher than those observed in the US-Canada asset pricing data, and increase as the risk-sharing incentive increases. In contrast, “international consumption quantities” are weakly sensitive to changes in the level of aversion to consumption and utility risk.     

Overnight stock returns and realized volatility

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has yet emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introduced formal testing procedure, we find that for the S&P 500 index, a realized volatility estimator that optimally incorporates overnight information is more accurate in-sample. In contrast, estimators that do not incorporate overnight information are more accurate for individual stocks. We also show that accounting for overnight returns may affect the conclusions drawn in an out-of-sample horserace of forecasting models. Finally, there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate in-sample RV estimators are considered.     

Efficient predictability of stock return volatility: The role of stock market implied volatility

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our in-sample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.     

Oil price uncertainty and movements in the US government bond risk premia

In this paper, we analyze the predictability of the movements of bond premia of US Treasury due to oil price uncertainty over the monthly period 1953:06 to 2016:12. For our purpose, we use a higher order nonparametric causality-in-quantiles framework, which in turn, allows us to test for predictability over the entire conditional distribution of not only bond returns, but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and structural breaks, which we show to exist in our data. We find that oil uncertainty not only predicts (increases) US bond returns, but also its volatility, with the effect on the latter being stronger. In addition, oil uncertainty tends to have a stronger impact on the shortest and longest maturities (2- and 5-year), and relatively weaker impact on bonds with medium-term (3- and 4-year) maturities. Our results are robust to alternative measures of oil market uncertainty and bond market volatility.     

Horse race of weekly idiosyncratic momentum strategies with respect to various risk metrics: Evidence from the Chinese stock market

This paper focuses on the horse race of weekly idiosyncratic momentum (IMOM) with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the return predictability with respect to various idiosyncratic risk metrics. Further, we perform a comparative study on the performance of the IMOM portfolios with respect to various risk metrics. At last, we explore the possible explanations to IMOM as well as risk-based IMOM portfolios. We find that 1) there are prevailing contrarian effect and IMOM effect for the whole sample; 2) the negative relations exist between most of the idiosyncratic risk metrics and the cross-sectional stock returns, and better performance is linked to idiosyncratic volatility (IVol) and maximum drawdowns (IMDs); 3) additionally, the IVol-based and IMD-based IMOM portfolios exhibit better explanatory power to the IMOM portfolios with respect to other risk metrics; 4) finally, higher profitability of IMOM as well as IVol-based and IMD-based IMOM portfolios is found to be related to upside market states, high levels of liquidity and high levels of investor sentiment.