Sudden changes in volatility in emerging markets: The case of Gulf Arab stock markets

This article examines sudden changes in volatility for five Gulf area Arab stock markets using the iterated cumulative sums of squares (ICSS) algorithm and analyzes their impacts on the estimated persistence of volatility. This algorithm identifies large shifts in volatility of the stock markets during the weekly period 1994 to 2001. In contrast to Aggarwal et al. [Aggarwal, R., Inclan, C., & Leal, R., 1999, Volatility in emerging markets. Journal of Financial and Quantitative Analysis 34, 33-55], this paper found that most of the Gulf Arab stock markets are more sensitive to major global events than to local and regional factors. The 1997 Asian crisis, the collapse of oil prices in 1998 after the crisis, the adoption of the price band mechanism by OPEC in 2000, and the September 11th attack have been found to have consistently affected the Gulf markets. Accounting for these large shifts in volatility in the GARCH(1,1) models significantly reduces the estimated persistence of the volatility in the Gulf stock markets.     

The Effect of VaR Based Risk Management on Asset Prices and the Volatility Smile

Value-at-risk (VaR) has become the standard criterion for assessing risk in the financial industry. Given the widespread usage of VaR, it becomes increasingly important to study the effects of VaR based risk management on the prices of stocks and options. We solve a continuous-time asset pricing model, based on Lucas (1978) and Basak and Shapiro (2001), to investigate these effects. We find that the presence of risk managers tends to reduce market volatility, as intended. However, in some cases VaR risk management undesirably raises the probability of extreme losses. Finally, we demonstrate that option prices in an economy with VaR risk managers display a volatility smile.     

State‐preference pricing and volatility indices

This study uses a state‐preference pricing approach to develop a state‐price volatility index (SVX), as a forecast for market future realised volatility. We show that SVX is a more efficient forecaster than CBOE VIX for 30‐day realised volatility of SPX returns, using both in‐the‐sample and out‐of‐the‐sample tests. This result is robust to different measures of realised market volatilities. We also show that SVX provides a better volatility forecast than other alternative measures, including the at‐the‐money implied volatilities and GARCH (1, 1) volatility. Our results provide a foundation for forecasting higher risk‐neutral moments using the same state prices.     

Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time

There has been an increase in price volatility in oil prices during and since the global financial crisis (GFC). This study investigates the Granger causality patterns in volatility spillovers between West Texas International (WTI) and Brent crude oil spot prices using daily data. We use Hafner and Herwartz’s (2006) test and employ a rolling sample approach to investigate the changes in the dynamics of volatility spillovers between WTI and Brent oil prices over time. Volatility spillovers from Brent to WTI prices are found to be more pronounced at the beginning of the analysis period, around the GFC, and more recently in 2020. Between 2015 and 2019, the direction of volatility spillovers runs unidirectionally from WTI to Brent oil prices. In 2020, however, a Granger-causal feedback relation between the volatility of WTI and Brent crude oil prices is again detected. This is due to the uncertainty surrounding how the COVID-19 pandemic will evolve and how long the economies and financial markets will be affected. In this uncertain environment, commodities markets participants could be reacting to prices and volatility signals on both WTI and Brent, leading to the detection of a feedback relation.     

Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk

We explore the cross‐sectional pricing of volatility risk by decomposing equity market volatility into short‐ and long‐run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short‐run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long‐run component relates to business cycle risk. Furthermore, a three‐factor pricing model with the market return and the two volatility components compares favorably to benchmark models.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

Cross‐Asset Speculation in Stock Markets

In practice, heterogeneously informed speculators combine private information about multiple stocks with information in prices, taking into account how their trades influence the inferences of other speculators via prices. We show how this speculation causes prices to be more correlated than asset fundamentals, raising price volatility. The covariance structure of asset fundamentals drives that of prices, while the covariance structure of liquidity trade drives that of order flows. We characterize how speculator profits vary with the distributions of information and liquidity trade across assets and speculators, and relate the cross‐asset factor structure of order flows to that of returns.     

Output, Stock Volatility, and Political Uncertainty in a Natural Experiment: Germany, 1880–1940

Why does stock volatility increase when output declines? The theory of investment under uncertainty implies that political uncertainty may simultaneously increase volatility and reduce output. Though cause and effect are typically hard to separate, the transition from Imperial to Weimar Germany offers a natural experiment because major political events left clear traces on stock prices. Current and past increases in volatility are associated with output declines, consistent with U.S. experience. However, political events are more clearly the source of volatility, and the results support the view that the relationship between volatility and output reflects the joint effects of political factors.     

Valuing Companies with Cash‐Flow@Risk

The classic DCF approach to capital budgeting—the one that MBA students in the world's top business schools have been taught for the last 30 years—begins with the assumption that the corporate investment decision is “independent of” the financing decision. That is, the value of a given investment opportunity should not be affected by how a company is financed, whether mainly with debt or with equity. A corollary of this capital structure “irrelevance” proposition says that a company's investment decision should also not be influenced by its risk management policy—by whether a company hedges its various price exposures or chooses to leave them unhedged. In this article, the authors—one of whom is the CFO of the French high‐tech firm Gemalto—propose a practical alternative to DCF that is based on a concept they call “cash‐flow@risk.” Implementation of the concept involves dividing expected future cash flow into two components: a low‐risk part, or “certainty equivalent,” and a high‐risk part. The two cash flow streams are discounted at different rates (corresponding to debt and equity) when estimating their value. The concept of cash‐flow@risk derives directly from, and is fully consistent with, the concept of economic capital that was developed by Robert Merton and Andre Perold in the early 1990s and that has become the basis of Value at Risk (or VaR) capital allocation systems now used at most financial institutions. But because the approach in this article focuses on the volatility of operating cash flows instead of asset values, the authors argue that an internal capital allocation system based on cash‐flow@risk is likely to be much more suitable than VaR for industrial companies.     

GARCH vs. stochastic volatility: Option pricing and risk management

In this paper we compare the out-of-sample performance of two common extensions of the Black–Scholes option pricing model, namely GARCH and stochastic volatility (SV). We calibrate the three models to intraday FTSE 100 option prices and apply two sets of performance criteria, namely out-of-sample valuation errors and Value-at-Risk (VaR) oriented measures. When we analyze the fit to observed prices, GARCH clearly dominates both SV and the benchmark Black–Scholes model. However, the predictions of the market risk from hypothetical derivative positions show sizable errors. The fit to the realized profits and losses is poor and there are no notable differences between the models. Overall, we therefore observe that the more complex option pricing models can improve on the Black–Scholes methodology only for the purpose of pricing, but not for the VaR forecasts.     

Risk Transmission from Oil and Natural Gas Futures to Emerging Market Mutual Funds

This study evaluates the impacts of energy markets on emerging market mutual funds (EMMFs). In particular, we investigate the volatility transmission between these funds and the oil and natural gas prices. The findings suggest significant risk spillover from the energy markets to EMMFs. Furthermore, we find a large number of EMMFs’ risk transmitting to oil prices and almost all of the EMMFs’ risk transmitting to natural gas prices. By dividing the sample into two (before and after 2008), we find the EMMFs’ influence on the oil market decreasing after this turbulent period. Our results have important implications for mutual fund managers and investors.     

TIME‐VARYING VOLATILITY IN CANADIAN AND U.S. STOCK INDEX AND INDEX FUTURES MARKETS: A MULTIVARIATE ANALYSIS

We use a multivariate generalized autoregressive heteroskedasticity model (M‐GARCH) to examine three stock indexes and their associated futures prices: the New York Stock Exchange Composite, S&P 500, and Toronto 35. The North American context is significant because markets in Canada and the United States share similar structures and regulatory environments. Our model allows examination of dependence in volatility as it captures time variation in volatility and cross‐market influences. Estimated time variation in volatility is significant, and the volatilities are highly positively correlated. Yet, we find that the correlation in North American index and futures markets has declined over time.     

DOES VOLATILITY DECREASE AFTER REVERSE STOCK SPLITS?

Previous research documents that volatility decreases after reverse stock splits. I show that measurement effects bias observed volatility, especially for lower priced stocks. Based on observed returns, volatility decreases 25% after reverse splits. Controlling for bid–ask bounce, volatility still decreases for stocks with prices above $5.00. However, for stocks below $2.00, volatility increases slightly. The portion of observed volatility attributable to measurement effects declines as the stock price increases and as the minimum tick size decreases. Finally, there is a significant and positive cross‐sectional relation between changes in the number of trades and changes in volatility after reverse splits.     

Gone fishin’: Seasonality in trading activity and asset prices

We use seasonality in stock trading activity associated with summer vacation as a source of exogenous variation to study the relationship between trading volume and expected return. Using data from 51 stock markets, we first confirm a widely held belief that stock turnover is significantly lower during the summer because market participants are on vacation. Interestingly, we find that mean stock return is also lower during the summer for countries with significant declines in trading activity. This relationship is not due to time-varying volatility. Moreover, both large and small investors trade less and the price of trading (bid-ask spread) is higher during the summer. These findings suggest that heterogeneous agent models are essential for a complete understanding of asset prices.     

Linear‐Rational Term Structure Models

We introduce the class of linear‐rational term structure models in which the state price density is modeled such that bond prices become linear‐rational functions of the factors. This class is highly tractable with several distinct advantages: (i) ensures nonnegative interest rates, (ii) easily accommodates unspanned factors affecting volatility and risk premiums, and (iii) admits semi‐analytical solutions to swaptions. A parsimonious model specification within the linear‐rational class has a very good fit to both interest rate swaps and swaptions since 1997 and captures many features of term structure, volatility, and risk premium dynamics—including when interest rates are close to the zero lower bound.     

Oil prices implied volatility or direction: Which matters more to financial markets?

We examine the impact of oil price uncertainty on US stock returns by industry using the US Oil Fund options implied volatility OVX index and a GJR-GARCH model. We test the effect of the implied volatility of oil on a wide array of domestic industries’ returns using daily data from 2007 to 2016, controlling for a variety of variables such as aggregate market returns, market volatility, exchange rates, interest rates, and inflation expectations. Our main finding is that the implied volatility of oil prices has a consistent and statistically significant negative impact on nine out of the ten industries defined in the Fama and French (J Financ Econ 43:153–193, 1997) 10-industry classification. Oil prices, on the other hand, yield mixed results, with only three industries showing a positive and significant effect, and two industries exhibiting a negative and significant effect. These findings are an indication that the volatility of oil has now surpassed oil prices themselves in terms of influence on financial markets. Furthermore, we show that both oil prices and their volatility have a positive and significant effect on corporate bond credit spreads. Overall, our results indicate that oil price uncertainty increases the risk of future cash flows for goods and services, resulting in negative stock market returns and higher corporate bond credit spreads.     

Regime Shifts in Price‐Dividend Ratios and Expected Stock Returns: A Present‐Value Approach

We incorporate regime shifts in the mean of price‐dividend ratios into the present value model of van Binsbergen and Koijen (2010) who propose a latent variable approach to modeling expected returns and dividend growth rates. We find that accounting for regime shifts results in much lower persistence of expected returns and higher volatility of expected returns, and thus higher in‐sample predictability, when compared to the results from the van Binsbergen and Koijen (2010) model. We also show that the main source of the increase in the mean of price‐dividend ratios in the mid‐1990s is a decrease in the mean of expected returns.     

Interest Rate Caps “Smile” Too! But Can the LIBOR Market Models Capture the Smile?

Using 3 years of interest rate caps price data, we provide a comprehensive documentation of volatility smiles in the caps market. To capture the volatility smiles, we develop a multifactor term structure model with stochastic volatility and jumps that yields a closed‐form formula for cap prices. We show that although a three‐factor stochastic volatility model can price at‐the‐money caps well, significant negative jumps in interest rates are needed to capture the smile. The volatility smile contains information that is not available using only at‐the‐money caps, and this information is important for understanding term structure models.     

An inverted U‐shaped crude oil price return‐implied volatility relationship

We examined the return–volatility relationship for USO ETF oil price return and CBOE Crude Oil ETF Volatility Index, OVX. The data for the USO and OVX covers the period covering May 11, 2007 to February 28, 2013. Our OLS regression results suggest evidence of regular feedback and leverage effects. When we employ linear quantile regression techniques, we find evidence of regular and inverse feedback effects. The inverse feedback effects being noticeable in the upper quantile region of the oil return distribution. There is also support for a regular leverage effect in USO prices. We also examined the return–volatility relationship using quantile regression copula methods for measuring the degree of asymmetry in the relationships between the oil price return and implied volatility. The results of the analysis indicate, first, that there exists a negative relationship between contemporaneous oil VIX and USO ETF oil returns. Second, that the relationship between oil returns and implied volatilities depends on the quartile at which the relationship is being investigated. Third, there exists an inverted U-shaped dependency relationship between returns and implied volatilities across quantiles. Fourth, though an inverted U-shape exists, the shape is different from those observed in stock markets.