Canada
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Meet Hybrid Data: A Blend of Alternative and Traditional Data. A Case Study to Construct an Improved Inflation Index
In the following article, I introduce the concept of “hybrid data,” a combination of alternative and traditional data, which I illustrate through an example on inflation, to be of better value than considering purely a traditional data or alternative data source alone. We present a case where we use alternative data from Zillow, to improve upon the Consumer Price Index (CPI), and thus create an index that is more pertinent to consumers and investors alike. -
Deep Learning in Segregated Fund Valuation: Part 2
This article is the second part of an article that appeared in April 2022 on the Emerging Topics Community webpage. It will discuss the data preparation, hyperparameter tuning and selection, and the training and testing process of the deep learning models. To reach the final conclusions, the article will continue to compare the projected cash flow results from LSTM and LSTM-Attn with those from the traditional method, and evaluate the time series generations of interest rates and equity returns by WGAN and TCN-GAN -
Deep Learning in Segregated Fund Valuation: Part I
Segregated Fund is a special investment fund to provide capital appreciation with embedded insurance features. The traditional methodology to estimate the capital reserve and pricing of contracts goes to Monte-Carlo based stochastic models due to its complexity. Recent research has introduced a deep learning model, Long Short-Term Memory (LSTM), to help cash flow projection for a Segregated Fund in its whole lifetime horizon. In this paper three new deep learning models are presented: Long Short-Term Memory with attention (LSTM-ATTN) to estimate the liability reserve and pricing Segregated Fund contracts, Wasserstein Generative Adversarial Network (WGAN) for stock return forecasting and Temporal Convolutional Network on GAN (TCN-GAN) for interest rate time series generation. As an example, the cash flow projection and Economic Capital for Segregated Fund portfolio are used to compare deep learning models against traditional models in terms of accuracy and computation efficiency.
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