4,513 research outputs found
Dynamic hedging of portfolio credit derivatives
We compare the performance of various hedging strategies for index collateralized debt obligation (CDO) tranches across a variety of models and hedging methods during the recent credit crisis. Our empirical analysis shows evidence for market incompleteness: a large proportion of risk in the CDO tranches appears to be unhedgeable. We also show that, unlike what is commonly assumed, dynamic models do not necessarily perform better than static models, nor do high-dimensional bottom-up models perform better than simpler top-down models. When it comes to hedging, top-down and regression-based hedging with the index provide significantly better results during the credit crisis than bottom-up hedging with single-name credit default swap (CDS) contracts. Our empirical study also reveals that while significantly large movesāājumpsāādo occur in CDS, index, and tranche spreads, these jumps do not necessarily occur on the default dates of index constituents, an observation which shows the insufficiency of some recently proposed portfolio credit risk models.hedging, credit default swaps, portfolio credit derivatives, index default swaps, collateralized debt obligations, portfolio credit risk models, default contagion, spread risk, sensitivity-based hedging, variance minimization
Afraid of Niche, Tired of Mass: Atypical Idea Combination on Crowdfunding Platform
A new idea usually follows a stream of similar ideas yet simultaneously combines atypical elements from ideas outside this stream. A successful business idea usually balances well between familiarity and atypicality. To investigate the relationship between atypicality innovation and crowdfunding project performance, we collected data from one of the largest crowdfunding platforms in China. We build a similarity network of crowdfunding projects to measure the degree of atypicality innovation for these projects. Using a double machine learning model, we find that the atypical combination of mainstream and niche ideas has a significant positive effect on the individual project\u27s funding, i.e., five times more successful than other projects. We also find the potential reasons that cause the poor performance of niche and mainstream projects. Donors are more conservative due to the high risk of niche projects and driven away by the monotonous repetition of mainstream projects
Deep Landscape Forecasting for Real-time Bidding Advertising
The emergence of real-time auction in online advertising has drawn huge
attention of modeling the market competition, i.e., bid landscape forecasting.
The problem is formulated as to forecast the probability distribution of market
price for each ad auction. With the consideration of the censorship issue which
is caused by the second-price auction mechanism, many researchers have devoted
their efforts on bid landscape forecasting by incorporating survival analysis
from medical research field. However, most existing solutions mainly focus on
either counting-based statistics of the segmented sample clusters, or learning
a parameterized model based on some heuristic assumptions of distribution
forms. Moreover, they neither consider the sequential patterns of the feature
over the price space. In order to capture more sophisticated yet flexible
patterns at fine-grained level of the data, we propose a Deep Landscape
Forecasting (DLF) model which combines deep learning for probability
distribution forecasting and survival analysis for censorship handling.
Specifically, we utilize a recurrent neural network to flexibly model the
conditional winning probability w.r.t. each bid price. Then we conduct the bid
landscape forecasting through probability chain rule with strict mathematical
derivations. And, in an end-to-end manner, we optimize the model by minimizing
two negative likelihood losses with comprehensive motivations. Without any
specific assumption for the distribution form of bid landscape, our model shows
great advantages over previous works on fitting various sophisticated market
price distributions. In the experiments over two large-scale real-world
datasets, our model significantly outperforms the state-of-the-art solutions
under various metrics.Comment: KDD 2019. The reproducible code and dataset link is
https://github.com/rk2900/DL
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great
importance and has found its usage in many Web applications including
recommender systems, web search and online advertising. The data in those
applications is mostly categorical and contains multiple fields; a typical
representation is to transform it into a high-dimensional sparse binary feature
representation via one-hot encoding. Facing with the extreme sparsity,
traditional models may limit their capacity of mining shallow patterns from the
data, i.e. low-order feature combinations. Deep models like deep neural
networks, on the other hand, cannot be directly applied for the
high-dimensional input because of the huge feature space. In this paper, we
propose a Product-based Neural Networks (PNN) with an embedding layer to learn
a distributed representation of the categorical data, a product layer to
capture interactive patterns between inter-field categories, and further fully
connected layers to explore high-order feature interactions. Our experimental
results on two large-scale real-world ad click datasets demonstrate that PNNs
consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201
Deep Recurrent Survival Analysis
Survival analysis is a hotspot in statistical research for modeling
time-to-event information with data censorship handling, which has been widely
used in many applications such as clinical research, information system and
other fields with survivorship bias. Many works have been proposed for survival
analysis ranging from traditional statistic methods to machine learning models.
However, the existing methodologies either utilize counting-based statistics on
the segmented data, or have a pre-assumption on the event probability
distribution w.r.t. time. Moreover, few works consider sequential patterns
within the feature space. In this paper, we propose a Deep Recurrent Survival
Analysis model which combines deep learning for conditional probability
prediction at fine-grained level of the data, and survival analysis for
tackling the censorship. By capturing the time dependency through modeling the
conditional probability of the event for each sample, our method predicts the
likelihood of the true event occurrence and estimates the survival rate over
time, i.e., the probability of the non-occurrence of the event, for the
censored data. Meanwhile, without assuming any specific form of the event
probability distribution, our model shows great advantages over the previous
works on fitting various sophisticated data distributions. In the experiments
on the three real-world tasks from different fields, our model significantly
outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code:
https://github.com/rk2900/drs
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