7,094 research outputs found
Dynamic modeling of mean-reverting spreads for statistical arbitrage
Statistical arbitrage strategies, such as pairs trading and its
generalizations, rely on the construction of mean-reverting spreads enjoying a
certain degree of predictability. Gaussian linear state-space processes have
recently been proposed as a model for such spreads under the assumption that
the observed process is a noisy realization of some hidden states. Real-time
estimation of the unobserved spread process can reveal temporary market
inefficiencies which can then be exploited to generate excess returns. Building
on previous work, we embrace the state-space framework for modeling spread
processes and extend this methodology along three different directions. First,
we introduce time-dependency in the model parameters, which allows for quick
adaptation to changes in the data generating process. Second, we provide an
on-line estimation algorithm that can be constantly run in real-time. Being
computationally fast, the algorithm is particularly suitable for building
aggressive trading strategies based on high-frequency data and may be used as a
monitoring device for mean-reversion. Finally, our framework naturally provides
informative uncertainty measures of all the estimated parameters. Experimental
results based on Monte Carlo simulations and historical equity data are
discussed, including a co-integration relationship involving two
exchange-traded funds.Comment: 34 pages, 6 figures. Submitte
Integrating genealogical and dynamical modelling to infer escape and reversion rates in HIV epitopes
The rates of escape and reversion in response to selection pressure arising
from the host immune system, notably the cytotoxic T-lymphocyte (CTL) response,
are key factors determining the evolution of HIV. Existing methods for
estimating these parameters from cross-sectional population data using ordinary
differential equations (ODE) ignore information about the genealogy of sampled
HIV sequences, which has the potential to cause systematic bias and
over-estimate certainty. Here, we describe an integrated approach, validated
through extensive simulations, which combines genealogical inference and
epidemiological modelling, to estimate rates of CTL escape and reversion in HIV
epitopes. We show that there is substantial uncertainty about rates of viral
escape and reversion from cross-sectional data, which arises from the inherent
stochasticity in the evolutionary process. By application to empirical data, we
find that point estimates of rates from a previously published ODE model and
the integrated approach presented here are often similar, but can also differ
several-fold depending on the structure of the genealogy. The model-based
approach we apply provides a framework for the statistical analysis of escape
and reversion in population data and highlights the need for longitudinal and
denser cross-sectional sampling to enable accurate estimate of these key
parameters
On-Line Portfolio Selection with Moving Average Reversion
On-line portfolio selection has attracted increasing interests in machine
learning and AI communities recently. Empirical evidences show that stock's
high and low prices are temporary and stock price relatives are likely to
follow the mean reversion phenomenon. While the existing mean reversion
strategies are shown to achieve good empirical performance on many real
datasets, they often make the single-period mean reversion assumption, which is
not always satisfied in some real datasets, leading to poor performance when
the assumption does not hold. To overcome the limitation, this article proposes
a multiple-period mean reversion, or so-called Moving Average Reversion (MAR),
and a new on-line portfolio selection strategy named "On-Line Moving Average
Reversion" (OLMAR), which exploits MAR by applying powerful online learning
techniques. From our empirical results, we found that OLMAR can overcome the
drawback of existing mean reversion algorithms and achieve significantly better
results, especially on the datasets where the existing mean reversion
algorithms failed. In addition to superior trading performance, OLMAR also runs
extremely fast, further supporting its practical applicability to a wide range
of applications.Comment: ICML201
Heavy tails and electricity prices
In the first years after the emergence of deregulated power markets it became apparent that for the valuation of electricity derivatives we cannot simply rely on models developed for financial or other commodity markets. However, before adequate models can be put forward the unique characteristics of electricity (spot) prices have to be thoroughly analyzed. In particular, the extreme volatility and price spikes which lead to heavy-tailed distributions of returns. In this paper we first analyze the stylized facts of electricity prices, then present two modeling approaches: jump-diffusion and regime-switching, which to some extent address the pertinent issues.Heavy-tailed distribution; Electricity spot price; Seasonality; Volatility; Price spike;
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