Clemens Kownatzki, PhD, Practitioner Faculty in Finance
How Good Is the Vix as a Predictor of Market Risk? Journal of Accounting and Finance, 16(6), 39-60. (2016).
Abstract
Volatility is a metric widely used to estimate financial risk. The VIX is an index derived from S&P 500 options prices designed to estimate the market’s expected 30-day volatility. Robert Whaley, the creator of the VIX, argued that it provided a cost-effective way to hedge risk but we question Whaley’s underlying assumption in this paper. We examine the VIX and implied volatility as a proxy for risk. Our studies show that the VIX consistently over-estimates actual volatility in normal times but it underestimates volatility in times of market crashes and crises making it unsuitable for many risk-management applications.
Joetta Forsyth, PhD, Associate Professor of Finance
Learning to Love Financial Market Barbarians, Yes Traders Do Make a Valuable Contribution to Society. Graziadio Business Review, 11(1). (2008).
Abstract
Traders in financial markets are often denounced, reviled, and blamed for the suffering of others. However, the author was surprised to observe commentators in a business news show dismissing speculators as trouble makers, while explaining that there may have been other good reasons for a swing in commodities prices. These reactions reflect a remarkable degree of misunderstanding even within the business community about the role of financial market participants, and how they ultimately contribute to society. Damaging regulations in the wake of some disruptive event are sometimes the result. The purpose of this article is to increase understanding within the business community of the role of traders, to call on financial practitioners to communicate the important role that they play, and to urge caution in calling for new regulations when financial markets undergo disruptions.
James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
Market-calibrated Forecasts for Natural Gas Prices. Energy Institute at the University of Texas at Austin, (2016), with Warren J. Hahn, James S. Dyer.
Abstract
Stochastic process models of commodity prices are important inputs in energy investment evaluation and planning problems. In this work, we focus on modeling and forecasting the long-term price level, since it is the dominant factor in many such applications. We apply a Kalman filtering method with maximum likelihood approach to estimate the model parameters for the two-factor Schwartz and Smith (2000) process, which decomposes the spot price into unobservable factors for forecasting the long-term equilibrium level and short-term deviation. The method also accommodates aspects of both a geometric Brownian motion process and a mean-reverting process. Historical natural gas futures data from 1996 to present were analyzed to determine the model parameters and we quantified the changes in both the drift rate and volatility that have resulted from developments in the natural gas markets since significant volumes of shale gas began to be produced. The parameterized model is then used to develop price forecasts with uncertainty bounds. The risk-neutral version of the stochastic price model is typically used theory and in academic work; however, risk-adjusted models of the expected spot price are often used in practice. These two approaches are connected by risk premia which are unfortunately often difficult to estimate. We use an asset pricing model approach to obtain improved estimates of the risk premia to facilitate development of both risk-neutral and expected spot price forecasts.
James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
What do market-calibrated stochastic processes indicate about the long-term price of crude oil?. Energy Economics, 44(July) 212-221, (2014), with Warren J. Hahn, James S. Dyer.
Abstract
Stochastic process models of commodity prices are important inputs in energy investment evaluation and planning problems. In this paper, we focus on modeling and forecasting the long-term price level, since it is the dominant factor in many such applications. To provide a foundation for our modeling approach we first evaluate the empirical characteristics of crude oil price data from 1990 to 2013 using unit root and variance ratio tests. Statistical evidence from these tests shows only weak support for the applicability of stationary mean-reverting type processes up through 2004, with non-stationary Brownian motion type processes becoming more plausible when the data from 2005 to 2013 is added. We then apply a Kalman filtering method with maximum likelihood approach to estimate the model parameters for both a single-factor Geometric Brownian motion (GBM) process as well as the two-factor Schwartz and Smith (2000) process. The latter process decomposes the spot price into unobservable factors for the long-term equilibrium level and short-term deviation, and it accommodates aspects of both a GBM process and a mean-reverting process. Both empirical and simulated data are analyzed with these models, and we quantify the increases in both the drift rate and volatility of these processes that result from developments in the crude oil markets since the middle of the last decade. We conclude by comparing and contrasting both historical accuracy and forecasts from the parameterized models, and show that when the emphasis is on the long-term expectations, a single factor GBM forecast may be sufficient.
Keywords: Oil prices, Futures markets, Stochastic processes, Kalman filter, Forecasting
Levan Efremidze, PhD, Assistant Professor of Finance
James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
Darrol J. Stanley, DBA, Professor of Finance and Accounting
Using VIX Entropy Indicators for Style Rotation Timing. Journal of Investing, 23(3), 130-143, (2014).
Abstract
In this article, the authors examine the feasibility of market timing between large-capitalization value and growth portfolios with the use of entropy measures as compared with previously tested methods of market timing using stock market volatility (using the CBOE’s Volatility Index, VIX). Including transaction fees, style rotations using entropy measures appear to provide superior risk-adjusted returns and may offer a desirable alternative strategy for risk-averse investors seeking equity exposure.
