11,710 research outputs found
Semi-universal portfolios with transaction costs
Ministry of Education, Singapore under its Academic Research Funding Tier
Growth-optimal portfolios under transaction costs
This paper studies a portfolio optimization problem in a discrete-time
Markovian model of a financial market, in which asset price dynamics depend on
an external process of economic factors. There are transaction costs with a
structure that covers, in particular, the case of fixed plus proportional
costs. We prove that there exists a self-financing trading strategy maximizing
the average growth rate of the portfolio wealth. We show that this strategy has
a Markovian form. Our result is obtained by large deviations estimates on
empirical measures of the price process and by a generalization of the
vanishing discount method to discontinuous transition operators.Comment: 32 page
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
The financial crisis of 2008 generated interest in more transparent,
rules-based strategies for portfolio construction, with Smart beta strategies
emerging as a trend among institutional investors. While they perform well in
the long run, these strategies often suffer from severe short-term drawdown
(peak-to-trough decline) with fluctuating performance across cycles. To address
cyclicality and underperformance, we build a dynamic asset allocation system
using Hidden Markov Models (HMMs). We test our system across multiple
combinations of smart beta strategies and the resulting portfolios show an
improvement in risk-adjusted returns, especially on more return oriented
portfolios (up to 50 in excess of market annually). In addition, we propose
a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM)
algorithm that performs feature selection simultaneously with the training of
the HMM, to improve regime identification. We evaluate our systematic trading
system with real life assets using MSCI indices; further, the results (up to
60 in excess of market annually) show model performance improvement with
respect to portfolios built using full feature HMMs
Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior
Despite the availability of very detailed data on financial market,
agent-based modeling is hindered by the lack of information about real trader
behavior. This makes it impossible to validate agent-based models, which are
thus reverse-engineering attempts. This work is a contribution to the building
of a set of stylized facts about the traders themselves. Using the client
database of Swissquote Bank SA, the largest on-line Swiss broker, we find
empirical relationships between turnover, account values and the number of
assets in which a trader is invested. A theory based on simple mean-variance
portfolio optimization that crucially includes variable transaction costs is
able to reproduce faithfully the observed behaviors. We finally argue that our
results bring into light the collective ability of a population to construct a
mean-variance portfolio that takes into account the structure of transaction
costsComment: 26 pages, 9 figures, Fig. 8 fixe
High order compact finite difference schemes for a nonlinear Black-Scholes equation
A nonlinear Black-Scholes equation which models transaction costs arising in the hedging of portfolios is discretized semi-implicitly using high order compact finite difference schemes. In particular, the compact schemes of Rigal are generalized. The numerical results are compared to standard finite difference schemes. It turns out that the compact schemes have very satisfying stability and non-oscillatory properties and are generally more e±cient than the considered classical schemes.Option pricing, transaction costs, parabolic equations, compact finite difference discretizations
Multilinear Superhedging of Lookback Options
In a pathbreaking paper, Cover and Ordentlich (1998) solved a max-min
portfolio game between a trader (who picks an entire trading algorithm,
) and "nature," who picks the matrix of gross-returns of all
stocks in all periods. Their (zero-sum) game has the payoff kernel
, where is the trader's final wealth and
is the final wealth that would have accrued to a deposit into the best
constant-rebalanced portfolio (or fixed-fraction betting scheme) determined in
hindsight. The resulting "universal portfolio" compounds its money at the same
asymptotic rate as the best rebalancing rule in hindsight, thereby beating the
market asymptotically under extremely general conditions. Smitten with this
(1998) result, the present paper solves the most general tractable version of
Cover and Ordentlich's (1998) max-min game. This obtains for performance
benchmarks (read: derivatives) that are separately convex and homogeneous in
each period's gross-return vector. For completely arbitrary (even
non-measurable) performance benchmarks, we show how the axiom of choice can be
used to "find" an exact maximin strategy for the trader.Comment: 41 pages, 3 figure
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