Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures

Gerlach, Richard; Wang, Chao

doi:10.1016/j.ijforecast.2019.07.003

Cited by 27 publications

(9 citation statements)

References 48 publications

(53 reference statements)

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“…Encompassing tests for different functionals such as e.g., the mean, quantiles, expectiles or probability densities based on link functions which require testing on the boundary (e.g., using convex link functions) can be implemented through adapting our asymptotic theory to semiparametric models for the functional under consideration. Eventually, our asymptotic theory can be used to test (e.g., for nullity of) model parameters on the boundary of the parameter space for the semiparametric VaR and ES models of Patton et al (2019), Taylor (2019) or Gerlach and Wang (2020), along the lines of Francq and Zakoïan (2009).…”

Section: Discussionmentioning

confidence: 99%

Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary

Dimitriadis¹,

Liu²,

Schnaitmann³

2020

Preprint

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We propose forecast encompassing tests for the Expected Shortfall (ES) jointly with the Value at Risk (VaR) based on flexible link (or combination) functions. Our setup allows testing encompassing for convex forecast combinations and for link functions which preclude crossings of the combined VaR and ES forecasts. As the tests based on these link functions involve parameters which are on the boundary of the parameter space under the null hypothesis, we derive and base our tests on nonstandard asymptotic theory on the boundary. Our simulation study shows that the encompassing tests based on our new link functions outperform tests based on unrestricted linear link functions for one-step and multi-step forecasts. We further illustrate the potential of the proposed tests in a real data analysis for forecasting VaR and ES of the S&P 500 index.

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Section: Discussionmentioning

confidence: 99%

Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary

Dimitriadis¹,

Liu²,

Schnaitmann³

2020

Preprint

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“…In the same context, one could also examine the contribution of different trading partners to the overall magnitude of spillbacks. Models should also encompass the role of global uncertainty shocks (see Box 3) and tail risks (De Santis and Van der Veken, 2020;Gerlach and Wang, 2020;Carriero et al, 2020). Factors such as global credit booms affect the distribution of growth in a non-linear way (Adrian et al, 2019).…”

Section: 2mentioning

confidence: 99%

The Implications of Globalisation for the ECB Monetary Policy Strategy

et al. 2021

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This paper assesses how globalisation has shaped the economic environment in which the ECB operates and discusses whether this warrants adjustments to the monetary policy strategy. The paper first looks at how trade and financial integration have evolved since the last strategy review in 2003. It then examines the effects of these developments on global productivity growth, the natural interest rate (r*), inflation trends and monetary transmission. While trade globalisation initially boosted productivity growth, this effect may be waning as trade integration slows and market contestability promotes a winner-takes-all environment. The impact of globalisation on r* has been ambiguous: downward pressures, fuelled by global demand for safe assets and an increase in the propensity to save against a background of rising inequality, are counteracted by upward pressures, from the boost to global productivity associated with greater trade integration. Headline inflation rates have become more synchronised globally, largely because commodity prices are increasingly determined by global factors. Meanwhile, core inflation rates show a lower degree of commonality. Globalisation has made a rather modest contribution to the synchronised fall in trend inflation across countries and contributed only moderately to the reduction in the responsiveness of inflation to changes in activity. Regarding monetary transmission, globalisation has made the role of the exchange rate more complex by introducing new mechanisms through which it affects financial conditions, real activity and price dynamics. Against the background of this discussion, the paper then examines the implications for the ECB's monetary policy strategy. In doing so, it asks two questions. How is the ECB's economic and monetary analysis affected by globalisation? And how does globalisation influence the choice of the ECB's monetary policy objective and instruments? The paper concludes that while globalisation has had profound effects on the world economy, it has not significantly impeded the ECB's ability to achieve price stability autonomously. Nonetheless, over shorter horizons, globalisation has changed monetary policy transmission and affected trade-offs between price stability and other goals, such as stabilising real activity and prices on the one hand and enhancing financial stability on the other. Large financial spillovers strengthen the case for more systematic recourse to additional instruments such as forward guidance and asset purchases. Spillovers may also imply a need for more active macroprudential policy to counter any unduly large effects that global factors may have on domestic financial conditions.

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“…Under the Student's t distribution, the true values of VaR and ES are

V a R_{α, t + 1} = \frac{t^{- 1} ((), α)}{\sqrt{v false/ v - 2}} σ_{t + 1}

and

E S_{α, t + 1} = - \frac{1}{α} f ((), \frac{t^{- 1} ((), α)}{\sqrt{v false/ v - 2}}) σ_{t + 1}

, respectively, where v is the degrees of freedom, f is the probability density function of the standard Student's t , and

t^{- 1} ((), α)

is the corresponding α th quantile. Step 4:Compare results. We also apply other seven models: GARCH‐t; realized GARCH‐t (realGARCH‐t) of Hansen et al (2012); JE‐AL‐Mult and JE‐AL‐Add of Taylor (2019); ES‐CAViaR‐Mult‐X, ES‐CAViaR‐Add‐X, and ES‐X‐CAViaR‐X of Gerlach and Wang (2020), to forecast VaR and ES. These seven models all cannot directly use the high‐frequency data to “nowcast” the current period's risk.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model

Chen

Jiang

et al. 2021

Journal of Forecasting

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Low‐frequency risk measures can filter out noise and better reflect the trend. In order to improve the forecasting accuracy of low‐frequency risk through making full use of the valuable information contained in high‐frequency independent variables, we propose a novel joint elicitable mixed data sampling (JE‐MIDAS) model by introducing MIDAS method into JE regression model. We utilize the JE‐MIDAS model to forecast value at risk and expected shortfall simultaneously and compare its performance with that of other popular models through both the Monte Carlo simulations and real‐world applications. The numerical results show that our model is superior to other models because it can model mixed‐frequency data directly, which avoids the information loss caused by frequency conversion. The empirical results on three stock indices also show that market volatility can increase financial risk. Interest rate has positive effects on risks for U.S. S&P500 and U.K. FTSE, whereas negative for SHCI of China.

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Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures

Cited by 27 publications

References 48 publications

Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary

Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary

The Implications of Globalisation for the ECB Monetary Policy Strategy

Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model

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