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Dynamic asset (and liability) management under market and credit risk
We introduce a modelling paradigm which integrates credit risk and market
risk in describing the random dynamical behaviour of the underlying fixed income assets.
We then consider an asset and liability management (ALM) problem and develop a mul-
tistage stochastic programming model which focuses on optimum risk decisions. These
models exploit the dynamical multiperiod structure of credit risk and provide insight
into the corrective recourse decisions whereby issues such as the timing risk of default is
appropriately taken into consideration. We also present a index tracking model in which
risk is measured (and optimised) by the CVaR of the tracking portfolio in relation to the
index. Both in- and out-of-sample (backtesting) experiments are undertaken to validate
our approach. In this way we are able to demonstrate the feasibility and flexibility of
the chosen framework
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint
This paper presents a simple method for a posteriori (historical)
multi-variate multi-stage optimal trading under transaction costs and a
diversification constraint. Starting from a given amount of money in some
currency, we analyze the stage-wise optimal allocation over a time horizon with
potential investments in multiple currencies and various assets. Three variants
are discussed, including unconstrained trading frequency, a fixed number of
total admissable trades, and the waiting of a specific time-period after every
executed trade until the next trade. The developed methods are based on
efficient graph generation and consequent graph search, and are evaluated
quantitatively on real-world data. The fundamental motivation of this work is
preparatory labeling of financial time-series data for supervised machine
learning.Comment: 25 pages, 4 figures, 6 table
A Comparison of Cointegration & Tracking Error Models for Mutual Funds & Hedge Funds
We present a detailed study of portfolio optimisation based on cointegration, a statistical tool that here exploits a long-run equilibrium relationship between stock prices and an index price. We compare the theoretical and empirical properties of cointegration optimal equity portfolios with those of portfolios optimised on the tracking error variance. From an eleven year out of sample performance analysis we find that for simple index tracking the additional feature of cointegration between the tracking portfolio and the index has no clear advantages or disadvantages relative to the tracking error variance (TEV) minimization model. However ensuring a cointegration relationship does pay off when the tracking task becomes more difficult. Cointegration optimal portfolios clearly dominate the TEV equivalents for all of the statistical arbitrage strategies based on enhanced indexation, in all market circumstancescointegration, tracking error, index tracking, statistical arbitrage
The Cointegration Alpha: Enchanced Index Tracking and Long-Short Equity Market Neutral Stragies
This paper presents two applications of cointegration based trading strategies: a classic index tracking strategy and a long-short equity market neutral strategy. As opposed to other traditional index tracking or long-short equity strategies, the portfolio optimisation is based on cointegration rather than correlation. The first strategy aims to replicate a benchmark accurately in terms of returns and volatility, while the other seeks to minimise volatility and generate steady returns under all market circumstances. Additionally, several combinations of these two strategies are explored. To validate the applicability of the cointegration technique to asset allocation, pioneered by Lucas (1997) and Alexander (1999), and explain how and why it works, we have employed a panel data on DJIA and its constituent stocks. When applied to constructing trading strategies in the DJIA, the cointegration technique produces encouraging results. For example, between January 1995 and December 2001 the most successful self-financing statistical arbitrage strategies returned (net of transaction and repo costs) approximately 10% with roughly 2% annual volatility and negligible correlation with the market. The comprehensive set of back-test results reported is meant to offer a detailed picture of the cointegration mechanism, and to emphasise its practical implementation issues. Its key characteristics, i.e. mean reverting tracking error, enhanced weights stability and better use of the information contained in stock prices, allow a flexible design of various funded and self-financing trading strategies, from index and enhanced index tracking, to long-short market neutral and alpha transfer techniques. Further enhancement of the strategy should target first, the identification of successful stock selection rules to supplement the simple cointegration results and second, the investigation of the potential benefits of applying optimal rebalancing rules.cointegration, enchanced index tracking, long-short equity, market neutral, hedge fund, alpha strategy
Equity Indexing: Conitegration and Stock Price Dispersion: A Regime Switiching Approach to market Efficiency
This paper examines the performance of a general dynamic equity indexing strategy based on cointegration, from a market efficiency perspective. A consistent return in excess of the benchmark is demonstrated over different time horizons and in different, real world and simulated stock markets. A measure of stock price dispersion is shown to be a leading indicator for the excess return, and their relationship is modelled as a Markov switching process of two market regimes. We find that the entire ‘abnormal return’ is associated with the high volatility regime, so the presence of a latent risk factor cannot be ruled out. Moreover, any market inefficiencies identified by the dynamic indexing model are temporary and occur only in special market circumstances. Our results have implications for equity fund managers: we shown how, without any stock selection, solely through smart optimisation and market timing, the benchmark performance can be significantly enhanced.cointegration, dispersion, efficient market hypothesis equity markets, index tracking, Markov switching
Conditional-Mean Hedging Under Transaction Costs in Gaussian Models
We consider so-called regular invertible Gaussian Volterra processes and
derive a formula for their prediction laws. Examples of such processes include
the fractional Brownian motions and the mixed fractional Brownian motions. As
an application, we consider conditional-mean hedging under transaction costs in
Black-Scholes type pricing models where the Brownian motion is replaced with a
more general regular invertible Gaussian Volterra process.Comment: arXiv admin note: text overlap with arXiv:1706.0153
Sparse and stable Markowitz portfolios
We consider the problem of portfolio selection within the classical Markowitz
mean-variance framework, reformulated as a constrained least-squares regression
problem. We propose to add to the objective function a penalty proportional to
the sum of the absolute values of the portfolio weights. This penalty
regularizes (stabilizes) the optimization problem, encourages sparse portfolios
(i.e. portfolios with only few active positions), and allows to account for
transaction costs. Our approach recovers as special cases the
no-short-positions portfolios, but does allow for short positions in limited
number. We implement this methodology on two benchmark data sets constructed by
Fama and French. Using only a modest amount of training data, we construct
portfolios whose out-of-sample performance, as measured by Sharpe ratio, is
consistently and significantly better than that of the naive evenly-weighted
portfolio which constitutes, as shown in recent literature, a very tough
benchmark.Comment: Better emphasis of main result, new abstract, new examples and
figures. New appendix with full details of algorithm. 17 pages, 6 figure
The optimal use of return predictability : an empirical study
In this paper we study the economic value and statistical significance of asset return predictability, based on a wide range of commonly used predictive variables. We assess the performance of dynamic, unconditionally efficient strategies, first studied by Hansen and Richard (1987) and Ferson and Siegel (2001), using a test that has both an intuitive economic interpretation and known statistical properties. We find that using the lagged term spread, credit spread, and inflation significantly improves the risk-return trade-off. Our strategies consistently outperform efficient buy-and-hold strategies, both in and out of sample, and they also incur lower transactions costs than traditional conditionally efficient strategies
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