A comprehensive look at financial volatility prediction by economic variables

Christiansen, Charlotte; Schmeling, Maik; Schrimpf, Andreas

doi:10.1002/jae.2298

Cited by 234 publications

(167 citation statements)

References 87 publications

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“…A subject of particular interest concerns the joint-behavior of volatility and the economy with a special emphasis on their lead-lag pattern and on the cyclical behavior of stock market volatility. Many contributions, including Hamilton and Lin (1996), Perez-Quiros and Timmermann (2000), Christiansen et al (2012), Corradi et al (2013), and Paye (2012), reported evidence of the counter-cyclical behavior of the long-run market volatility. Hence, if the economy shrinks, stock market volatility is expected to increase in response to concerns about future market conditions.…”

Section: Introductionmentioning

confidence: 99%

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

Dominicy

Elst

2015

SSRN Journal

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This paper studies in some details the joint-use of high-frequency data and economic variables to model financial returns and volatility. We extend the Realized LGARCH model by allowing for a timevarying intercept, which responds to changes in macroeconomic variables in a MIDAS framework and allows macroeconomic information to be included directly into the estimation and forecast procedure.Using more than 10 years of high-frequency transactions for 55 U.S. stocks, we argue that the combination of low-frequency exogenous economic indicators with high-frequency financial data improves our ability to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multiperiod Value-at-Risk. We document that nominal corporate profits and term spreads generate accurate risk measures forecasts at horizons beyond two business weeks.

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Section: Introductionmentioning

confidence: 99%

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

Dominicy

Elst

2015

SSRN Journal

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“…For the i-th stock, the observed value of JU MP at t is denoted by jump i,t ; we checked that the jump i,t series has evident peaks in periods of financial turmoil (the estimated jumps series are available on request). For reasons of consistency, when we analyse FPC, we compute jump t as the first principal component of jump i,t , for i = 1, ..., 16.…”

Section: Realized Volatilities and Conditioning Variablesmentioning

confidence: 99%

“…For instance, Christiansen et al [16] predicted the asset return volatility using macroeconomic and financial variables in a Bayesian model averaging framework. They considered several asset classes, such as equities, foreign exchange, bonds and commodities, over long time spans and found that economic variables provide information about future volatility from both an in-sample and an out-of-sample perspective.…”

Section: Introductionmentioning

confidence: 99%

The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

Bonaccolto

Caporin

2016

JRFM

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Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables' relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles.

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“…However, the focus has mostly been on univariate models, such as the GARCH class of models (Engle 1982, Bollerslev 1986), or univariate …ltering methods that use realized high-frequency volatility (Barndor¤-Nielsen andShephard 2002, Andersen et al 2003). A much smaller literature has, like us, looked directly at the information in other economic and …nancial variables concerning future volatility (Schwert 1989, Christiansen, Schmeling, and Schrimpf 2012, Paye 2012, Engle, Ghysels, and Sohn 2013.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intertemporal CAPM with Stochastic Volatility

et al. 2012

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This paper studies the pricing of volatility risk using the first-order conditions of a long-term equity investor who is content to hold the aggregate equity market rather than tilting towards value stocks and other equity portfolios that are attractive to short-term investors. We show that a conservative long-term investor will avoid such tilts in order to hedge against two types of deterioration in investment opportunities: declining expected stock returns, and increasing volatility. Empirically, we present novel evidence that low-frequency movements in equity volatility, tied to the default spread, are priced in the cross-section of stock returns.

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A comprehensive look at financial volatility prediction by economic variables

Cited by 234 publications

References 87 publications

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

An Intertemporal CAPM with Stochastic Volatility

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