Hedging With Volatility
Mario Alagoa (Sacred Heart University)
May 9, 2018
A risk-averse investor with a long equity position is presumably interested in identifying a hedging strategy that protects the value of that investment. The common approach encompasses using either financial derivatives or holding assets (such as gold or Swiss francs) as portfolio hedges as they show negative correlation with equities. This paper proposes using volatility indexes as portfolio hedges instead; it shows that a volatility-based dynamic hedging strategy is the most effective at protecting the value of an equity investment.

The Limits to Volatility Predictability: Quantifying Forecast Accuracy Across Horizons
Valeriy Zakamulin and Xingyi Li (University of Agder)
June 19, 2018
Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about how far ahead one can forecast volatility. First, in this paper we introduce the notions of the spot and forward predicted volatilities and propose to describe the term structure of volatility predictability by the spot and forward forecast accuracy curves. Then, by employing a few popular time-series volatility models, we perform a comprehensive empirical study on the horizon of volatility predictability. Our results suggest that, whereas the spot volatility can be predicted over horizons that extend to 35 weeks, the horizon of the forward volatility predictability is rather short and limited to approximately 7.5 weeks. Finally, we suggest a plausible explanation for why standard models fail to provide sensible longer-horizon volatility forecasts.

How Much Does the Fed Care About Stock Prices?
Alexander Kurov (West Virginia University), et al.
August 20, 2018
We use a predictable change in the intraday volatility of index futures to identify the effect of stock returns on monetary policy. This identification approach relies on a weaker set of assumptions than required under identification through heteroskedasticity based on lower frequency data. Our identification approach also allows examining time variation in the reaction of monetary policy to the stock market. The results show a sharp increase in the response of monetary policy expectations to stock returns during recessions and bear markets. This finding is consistent with the existence of the so-called “Fed put.”

Downside Volatility-Managed Portfolios
Xiao Qiao (SummerHaven Investment Management), et al.
August 22, 2018
Downside volatility and volatility typically comove but are not highly correlated during the most volatile times. We show that portfolios scaled by downside volatility expand the ex post mean-variance frontiers constructed using the original portfolios and volatility-managed portfolios of Moreira and Muir (2017), and improve the Sharpe ratios of the ex post tangency portfolios. Our results follow from the observation that downside volatility-managed portfolios are not spanned by the original portfolios or volatility-managed portfolios. Whereas downside volatility-managed portfolios expand the investment opportunity set, upside volatility-managed portfolios do not.

Equity Return Dispersion and Stock Market Volatility: Evidence from Multivariate Linear and Nonlinear Causality Tests
Riza Demirer (Southern Illinois University Edwardsville), et al.
July 2018
This paper contributes to the literature on stock market predictability by exploring the causal relationships between equity return dispersion, stock market volatility and excess returns via multivariate nonlinear causality tests. Both bivariate and multivariate nonlinear causality tests yield significant evidence of causality from return dispersion to both stock market volatility and equity premium, even after controlling for the state of the economy. While we find significant causality from business conditions to return dispersion, we see that expansionary (contractionary) market states are associated with low (high) level of equity return dispersion, indicating asymmetries in the relationship between equity return dispersion and economic conditions. Overall, our findings suggest that both return dispersion and business conditions are valid joint forecasters of both the stock market volatility and excess market returns and that return dispersion possesses incremental information regarding future stock return dynamics beyond which can be explained by the state of the economy.

Forecasting Realized Volatility With Kernel Ridge Regression
Blake LeBaron (Brandeis University)
August 9, 2018
This paper explores a common machine learning tool, the kernel ridge regression, as applied to financial volatility forecasting. It is shown that kernel ridge provides reliable forecast improvements to both a linear specification, and a fitted nonlinear specification which represents well known empirical features from volatility modeling. Therefore, the kernel ridge specification is still finding some nonlinear improvements that are not part of the usual volatility modeling toolkit. Various diagnostics show it to be a reliable and useful tool. Finally, the results are applied in a dynamic volatility control trading strategy. The kernel ridge results again show improvements over linear modeling tools when applied to building a dynamic strategy.