3,776 research outputs found
Multiscaled Cross-Correlation Dynamics in Financial Time-Series
The cross correlation matrix between equities comprises multiple interactions
between traders with varying strategies and time horizons. In this paper, we
use the Maximum Overlap Discrete Wavelet Transform to calculate correlation
matrices over different timescales and then explore the eigenvalue spectrum
over sliding time windows. The dynamics of the eigenvalue spectrum at different
times and scales provides insight into the interactions between the numerous
constituents involved.
Eigenvalue dynamics are examined for both medium and high-frequency equity
returns, with the associated correlation structure shown to be dependent on
both time and scale. Additionally, the Epps effect is established using this
multivariate method and analyzed at longer scales than previously studied. A
partition of the eigenvalue time-series demonstrates, at very short scales, the
emergence of negative returns when the largest eigenvalue is greatest. Finally,
a portfolio optimization shows the importance of timescale information in the
context of risk management
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A review of portfolio planning: Models and systems
In this chapter, we first provide an overview of a number of portfolio planning models
which have been proposed and investigated over the last forty years. We revisit the
mean-variance (M-V) model of Markowitz and the construction of the risk-return
efficient frontier. A piecewise linear approximation of the problem through a
reformulation involving diagonalisation of the quadratic form into a variable
separable function is also considered. A few other models, such as, the Mean
Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the
Minimax (MM) model which use alternative metrics for risk are also introduced,
compared and contrasted. Recently asymmetric measures of risk have gained in
importance; we consider a generic representation and a number of alternative
symmetric and asymmetric measures of risk which find use in the evaluation of
portfolios. There are a number of modelling and computational considerations which
have been introduced into practical portfolio planning problems. These include: (a)
buy-in thresholds for assets, (b) restriction on the number of assets (cardinality
constraints), (c) transaction roundlot restrictions. Practical portfolio models may also
include (d) dedication of cashflow streams, and, (e) immunization which involves
duration matching and convexity constraints. The modelling issues in respect of these
features are discussed. Many of these features lead to discrete restrictions involving
zero-one and general integer variables which make the resulting model a quadratic
mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the
algorithms and solution methods for this class of problems are also discussed. The
issues of preparing the analytic data (financial datamarts) for this family of portfolio
planning problems are examined. We finally present computational results which
provide some indication of the state-of-the-art in the solution of portfolio optimisation
problems
An analysis of spending behaviour under liquidity constraints with an application to financial hedging
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Optimal execution strategy with an uncertain volume target
In the seminal paper on optimal execution of portfolio transactions, Almgren
and Chriss (2001) define the optimal trading strategy to liquidate a fixed
volume of a single security under price uncertainty. Yet there exist
situations, such as in the power market, in which the volume to be traded can
only be estimated and becomes more accurate when approaching a specified
delivery time. During the course of execution, a trader should then constantly
adapt their trading strategy to meet their fluctuating volume target. In this
paper, we develop a model that accounts for volume uncertainty and we show that
a risk-averse trader has benefit in delaying their trades. More precisely, we
argue that the optimal strategy is a trade-off between early and late trades in
order to balance risk associated with both price and volume. By incorporating a
risk term related to the volume to trade, the static optimal strategies
suggested by our model avoid the explosion in the algorithmic complexity
usually associated with dynamic programming solutions, all the while yielding
competitive performance
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
VaR and Liquidity Risk.Impact on Market Behaviour and Measurement Issues.
Current trends in international banking supervision following the 1996 Amendment to the Basel Accord emphasise market risk control based upon internal Value-at-risk (VaR) models. This paper discusses the merits and drawbacks of VaR models in the light of their impact on market liquidity. After a preliminary review of basic concepts and measures regarding market risk, market friction and liquidity risk, the arguments supporting the internal models approach to supervision on market risk are discussed, in the light of the debate on the limitations and possible enhancements of VaR models. In particular, adverse systemic effects of widespread risk management practices are considered. Risk measurement models dealing with liquidity risk are then examined in detail, in order to verify their potential for application in the field. We conclude that VaR models are still far from effectively treating market and liquidity risk in their multi-faceted aspects. Regulatory guidelines are right in recognising the importance of internal risk control systems. Implementation of those guidelines might inadvertently encourage mechanic application of VaR models, with adverse systemic effects.
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