Macro News and Long-Run Volatility Expectations
Anders Vilhelmsson (Lund University)
December 10, 2019
I propose a new model-free method for estimating long-run changes in expected volatility using VIX futures contracts. The method is applied to measure the effect on stock market volatility of scheduled macroeconomic news announcements. I find that looking at long-run changes gives qualitatively different results compared to previous studies that only look at realized variance and the VIX. I further find that FOMC announcements on average resolve uncertainty, but only during times when policy uncertainty is higher than average. Real side macro announcements increase long-run volatility during times of low policy uncertainty, but the effect is reversed during times of high policy uncertainty.
Portfolio Strategies for Volatility Investing
Jim Campasano (Kansas State University)
November 20, 2019
The VIX premium has been shown to hold predictive power over volatility returns and investment risk. Applied within a portfolio construct, this study proposes a conditional strategy which allocates to market and volatility risk. While the strategy is predominantly short volatility, the strategy owns volatility during much of the financial crises. Both long and short volatility allocations prove profitable over the sample period, producing a portfolio more consistently profitable than the S\&P 500 Index and related strategies.
Risk On-Risk Off: A Regime Switching Model for Active Portfolio Management
José Pablo Dapena (University of CEMA), et al.
December 1, 2019
Unlike passive management, where investors almost do not buy and sell securities, active management involves a set of trading rules that govern investment decisions regarding mainly market timing. In this paper, we take the basics of active management and the two fund separation approach, to exploit the fact that an investor can switch between the market portfolio and the risk free asset according to the perceived state of the nature. Our purpose is to evaluate if there is an active management premium by testing performance with our own non-conventional multifactor model, constructed with a Hidden Markov Model which depending on the market states signaled by the level of volatility spread. We have documented that effectively, there is present a premium for actively manage the strategies, giving evidence against the idea that “active managers” destroy capital. We then propose the volatility spread as the active management factor into the Carhart’s model used to evaluate trading strategies with respect to a benchmark portfolio.
Financial Volatility and Economic Growth
Jon Danielsson (London School of Economics), et al.
November 12, 2019
We investigate the impact of financial volatility on economic growth, using a panel spanning 150 years and 74 countries. A positive shock to volatility and persistent high volatility lead to a short-term decrease in growth. Persistent low volatility affects growth differently: Initially leading to higher growth, but with a reversal two years hence, consistent with theories of how continued low risk environment induces higher risk-taking. The impact is stronger when volatility is low globally, during the post Bretton Woods era, and for countries experiencing high credit growth. Furthermore, long-lasting global volatility has a significant impact on capital flows, investment, and lending quality.
Do Excessively Volatile Forecasts Impact Investors?
Russell J. Lundholm (U. of British Columbia) and Rafael Rogo (Indiana U.)
There is a logical bound on the time-series variability of analyst forecasts; when variability exceeds this bound it must be caused by something besides statistically rational forecasting. We document occurrences of excessively volatile analyst forecasts and show that they influence investment performance. Comparing trading rules based on forecasts that are excessively volatile and those that are not, we find the returns to investing based on the former are significantly lower, with higher daily volatility, and a lower Sharpe ratio. We also show that returns to trading based on excessively volatile forecasts underperform the most when there is little news arriving and when the news that does arrive is relatively neutral. In this region, it is hardest to argue that analysts are unwittingly overreacting to news; instead, they appear to be intentionally making extreme forecasts to curry favor with management or to differentiate themselves from other analysts.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno