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Forecasting Precious Metal Returns with Multivariate Random Forests

Pierdzioch, Christian - 2018

We use multivariate random forests to compute out-of-sample forecasts of a vector of returns of four precious metal prices (gold, silver, platinum, and palladium). We compare the multivariate forecasts with univariate out-of-sample forecasts implied by random forests independently fitted to...

Persistent link: https://www.econbiz.de/10012922049

Forecasting Realized Stock-Market Volatility : Do Industry Returns Have Predictive Value?

Demirer, Riza; Gupta, Rangan; Pierdzioch, Christian - 2021

bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out …-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not … crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show …

Persistent link: https://www.econbiz.de/10013249490

A Boosting Approach to Forecasting the Volatility of Gold-Price Fluctuations Under Flexible Loss

Pierdzioch, Christian - 2015

macroeconomic variables for forecasting the volatility of gold-price fluctuations. We use an out-of-sample R2 statistic to evaluate … when underestimating rather than overestimating the future volatility of gold-price fluctuations. We use a simulation …

Persistent link: https://www.econbiz.de/10013032102

Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? : a quantile machine-learning approach

Gupta, Rangan; Pierdzioch, Christian - In: Financial innovation : FIN 9 (2023), pp. 1-22

Persistent link: https://www.econbiz.de/10014288917

Heterogeneous forecasters and nonlinear expectation formation in the US stock market

Pierdzioch, Christian; Reitz, Stefan; Ruelke, Jan-Christoph - 2014

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time...

Persistent link: https://www.econbiz.de/10010384168

Heterogeneous forecasters and nonlinear expectation formation in the US stock market

Pierdzioch, Christian; Reitz, Stefan; Ruelke, Jan-Christoph - 2014

Persistent link: https://www.econbiz.de/10010407532

Heteroeneous forecasters and nonlinear expectation formation in US stock market

Pierdzioch, Christian; Reitz, Stefan; Ruelke, Jan-Christoph - 2015

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectationformation process in the US stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as...

Persistent link: https://www.econbiz.de/10010479018

Climate risks and U.S. stock-market tail risk : a forecasting experiment using over a century of data

Salisu, Afees A.; Gupta, Rangan; Pierdzioch, Christian; … - In: International review of finance : the official journal … 23 (2023) 2, pp. 228-244

Persistent link: https://www.econbiz.de/10014326299

Forecasting realized volatility of bitcoin returns : tail events and asymmetric loss

Gillas, Konstantinos Gkillas; Gupta, Rangan; … - In: The European journal of finance 27 (2021) 16, pp. 1626-1644

Persistent link: https://www.econbiz.de/10012872908

Forecasting Stock Market Volatility with Macroeconomic Variables in Real Time

Döpke, Jörg - 2016

We compared forecasts of stock market volatility based on real-time and revised …

Persistent link: https://www.econbiz.de/10012989311