Time series momentum and volatility scaling

By A. Kim, Y. Tse, J. Wald. 2016

81 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Bryan Lim
2. Stephen J. Roberts
3. Xing Han
4. Youwei Li
5. Stefan Zohren
6. Weiwei Jiang

In Finance

1. Bryan Lim
2. Stephen J. Roberts
3. Xing Han
4. Youwei Li
5. Stefan Zohren

Citations in the corpus

• Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, methodology): “Model calibration was undertaken using minibatch stochastic gradient descent with the Adam optimizer (Kingma and Ba 2015), based on the previously defined loss functions. Backpropagation was performed up to a maximum of 100 training epochs using 90% of a given block of training data with the most re”
• Can investor sentiment be a momentum time-series predictor? Evidence from China (2017, background): “Second, Kim et al. (2016) find that the TSMOM profit mainly stems from the risk parity approach (volatility scheme) adopted in Moskowitz et al. (2012)’s paper.”