78 research outputs found
Stock Price Dynamics and Option Valuations under Volatility Feedback Effect
According to the volatility feedback effect, an unexpected increase in
squared volatility leads to an immediate decline in the price-dividend ratio.
In this paper, we consider the properties of stock price dynamics and option
valuations under the volatility feedback effect by modeling the joint dynamics
of stock price, dividends, and volatility in continuous time. Most importantly,
our model predicts the negative effect of an increase in squared return
volatility on the value of deep-in-the-money call options and, furthermore,
attempts to explain the volatility puzzle. We theoretically demonstrate a
mechanism by which the market price of diffusion return risk, or an equity
risk-premium, affects option prices and empirically illustrate how to identify
that mechanism using forward-looking information on option contracts. Our
theoretical and empirical results support the relevance of the volatility
feedback effect. Overall, the results indicate that the prevailing practice of
ignoring the time-varying dividend yield in option pricing can lead to
oversimplification of the stock market dynamics.Comment: 23 pages, 7 figures, 2 table
Multilayer Aggregation with Statistical Validation: Application to Investor Networks
Multilayer networks are attracting growing attention in many fields,
including finance. In this paper, we develop a new tractable procedure for
multilayer aggregation based on statistical validation, which we apply to
investor networks. Moreover, we propose two other improvements to their
analysis: transaction bootstrapping and investor categorization. The
aggregation procedure can be used to integrate security-wise and time-wise
information about investor trading networks, but it is not limited to finance.
In fact, it can be used for different applications, such as gene,
transportation, and social networks, were they inferred or observable.
Additionally, in the investor network inference, we use transaction
bootstrapping for better statistical validation. Investor categorization allows
for constant size networks and having more observations for each node, which is
important in the inference especially for less liquid securities. Furthermore,
we observe that the window size used for averaging has a substantial effect on
the number of inferred relationships. We apply this procedure by analyzing a
unique data set of Finnish shareholders during the period 2004-2009. We find
that households in the capital have high centrality in investor networks,
which, under the theory of information channels in investor networks suggests
that they are well-informed investors
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected,
the dynamics of the financial markets pose both a challenge and an opportunity
for high frequency traders. In order to take advantage of the rapid, subtle
movement of assets in High Frequency Trading (HFT), an automatic algorithm to
analyze and detect patterns of price change based on transaction records must
be available. The multichannel, time-series representation of financial data
naturally suggests tensor-based learning algorithms. In this work, we
investigate the effectiveness of two multilinear methods for the mid-price
prediction problem against other existing methods. The experiments in a large
scale dataset which contains more than 4 millions limit orders show that by
utilizing tensor representation, multilinear models outperform vector-based
approaches and other competing ones.Comment: accepted in SSCI 2017, typos fixe
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