Thomas Conlon

Cross-correlation dynamics in financial time series

The dynamics of the equal-time cross-correlation matrix of multivariate financial time series is explored by examination of the eigenvalue spectrum over sliding time windows. Empirical results for the S&P 500 and the Dow Jones Euro Stoxx 50 indices reveal that the dynamics of the small eigenvalues of the cross-correlation matrix, over these time windows, oppose those of the largest eigenvalue. This behaviour is shown to be independent of the size of the time window and the number of stocks examined. A basic one-factor model is then proposed, which captures the main dynamical features of the eigenvalue spectrum of the empirical data. Through the addition of perturbations to the one-factor model, (leading to a ‘market plus sectors’ model), additional sectoral features are added, resulting in an Inverse Participation Ratio comparable to that found for empirical data. By partitioning the eigenvalue time series, we then show that negative index returns, (drawdowns), are associated with periods where the largest eigenvalue is greatest, while positive index returns, (drawups), are associated with periods where the largest eigenvalue is smallest. The study of correlation dynamics provides some insight on the collective behaviour of traders with varying strategies.

Random matrix theory and fund of funds portfolio optimisation

The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a Fund of Hedge Funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which induces noise. In this paper random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using hedge fund returns data. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to distinct groups of strategies that are applied by hedge fund managers. The Inverse Participation ratio is used to quantify the number of components that participate in each eigenvector. Finally, the correlation matrix is cleaned by separating the noisy part from the non-noisy part of C. This technique is found to greatly reduce the difference between the predicted and realised risk of a portfolio, leading to an improved risk profile for a fund of hedge funds.

Does Gold Glitter in the Long-Run? Gold as a Hedge and Safe Haven Across Time and Investment Horizon

During times of market turmoil, investors often seek to mitigate risks associated with traditional investment assets such as equities and debt. The hedging and safe-haven properties of gold are examined in this paper for investors with short- and long-run horizons. Utilizing wavelet analysis, we find that gold acts as a hedge for a variety of international equity and debt markets for horizons of up to one year. The safe haven properties of gold during financial crises are further established, with gold shown to act as a safe haven for equity investors for long-run horizons of up to one year. However, during the economic contractions of the early 1980s gold is found not to act as a safe haven, displaying a positive relationship with equities across a range of horizons.

Downside Risk and the Energy Hedger's Horizon

In this paper, we explore the impact of investor time-horizon on an optimal downside hedged energy portfolio. Previous studies have shown that minimum-variance hedging effectiveness improves for longer horizons using variance as the performance metric. This paper investigates whether this result holds for different hedging objectives and effectiveness measures. A wavelet transform is applied to calculate the optimal heating oil hedge ratio using a variety of downside objective functions at different time-horizons. We demonstrate decreased hedging effectiveness for increased levels of uncertainty at higher confidence intervals. Moreover, for each of the different hedging objectives and effectiveness measures studied, we also demonstrate increasing hedging effectiveness at longer horizons. While small differences in effectiveness are found across the different hedging objectives, time-horizon effects are found to dominate confirming the importance of considering the hedgers horizon. The findings suggest that while downside risk measures are useful in the computation of an optimal hedge ratio that accounts for unwanted negative returns, hedging horizon and confidence intervals should also be given careful consideration by the energy hedger.

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 time–scales and then explore theeigenvalue spectrumover 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, theEppseffect 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 time–scale information in the context of risk management.

Commodity Futures Hedging, Risk Aversion and the Hedging Horizon

This paper examines the impact of management preferences on optimal futures hedging strategy and associated performance. Applying an expected utility hedging objective, the optimal futures hedge ratio is determined for a range of preferences on risk aversion, hedging horizon and expected returns. Empirical results reveal substantial hedge ratio variation across distinct management preferences and are supportive of the hedging policies of real firms. Hedging performance is further shown to be strongly dependent on underlying preferences. In particular, hedgers with high risk aversion and short horizon reduce hedge portfolio risk but achieve inferior utility in comparison to those with low aversion.

Seizure characterisation using frequency-dependent multivariate dynamics

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on the recent use of multivariate techniques to study the cross-correlation dynamics between electroencephalographic (EEG) channels. The maximum overlap discrete wavelet transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency-dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency-dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics.

Asset allocation with correlation: A composite trade-off

We assess the ability of minimum-variance portfolio allocation strategies accounting for time-varying correlation between assets to provide performance benefits relative to an equally-weighted portfolio. Prior to transaction costs correlation-based strategies emphatically outperform the equally-weighted benchmark. This finding is strongest for short horizon correlation forecasts and attributed to dynamic correlation as opposed to variance forecasts. Thus, estimation error is not found to be the primary obstacle to successful portfolio optimization. Rather, frequent rebalancing and associated transaction costs pose a significant challenge. Limiting portfolio turnover through short-selling restrictions and greater rebalancing error tolerance results in regular outperformance of the correlation based strategies even for large transaction costs. Taken together, these findings provide evidence of a trade-off between optimal portfolio performance, forecasting horizon, rebalancing frequency and transaction costs.

Credit Default Swaps as Indicators of Bank Financial Distress

We examine whether CDS contracts written on individual banks are effective leading indicators of bank financial distress during a period of systemic bank crisis. Changes in CDS spreads are found to yield a robust signal of failure across a set of European and US banks, in keeping with indirect market discipline. Furthermore, changes in CDS spreads provide information about the condition of banks which supplements that available from equity markets and contained in accounting metrics. Consistent results are detailed for both senior and subordinated CDS spreads. Our results hold out-of-sample, for logit and proportional hazards models, for various cohorts, for idiosyncratic changes in CDS and are robust to the use of alternative measures of bank distress, including rating downgrades and accounting risk.

Beyond Common Equity: The Influence of Secondary Capital on Bank Insolvency Risk

Banks must adhere to strict rules regarding the quantity of regulatory capital held but have some flexibility as to its composition. In this paper, we examine if bank insolvency (distance to default) is sensitive to capital other than common equity for a sample of listed North American and European banks. Decomposing tier 1 capital into tangible equity and non-core components reveals a series of heretofore unidentified non-linear links with insolvency risk. We assess the influence of binding capital requirements, finding that low regulatory capital buffers are associated with increased insolvency risk for banks holding greater quantities of non-core tier 1 and tier 2 capital. The links between insolvency and capital, evident when the latter is denominated relative to tangible assets or total regulatory capital, are found to be expunged when defined relative to risk-weighted assets.