Malcolm Kemp – Extreme Events. Robust Portfolio Construction in the Presence of Fat Tails

Description

Description

Taking due account of excessive occasions when setting up portfolios of belongings or liabilities is a key self-discipline for market professionals. Extreme occasions are a truth of life in how markets function.

In Extreme Events: Robust Portfolio Construction in the Presence of Fat Tails, main professional Malcolm Kemp exhibits readers how one can analyse market knowledge to uncover fat-tailed behaviour, how one can incorporate professional judgement in the dealing with of such info, and how one can refine portfolio development methodologies to make portfolios much less weak to excessive occasions or to learn extra from them.

This is the solely textual content that mixes a complete remedy of trendy danger budgeting and portfolio development strategies with the particular refinements wanted for them to deal with excessive occasions. It explains in a logical sequence what constitutes fat-tailed behaviour and why it arises, how we are able to analyse such behaviour, at combination, sector or instrument degree, and the way we are able to then take benefit of this evaluation.

Along the approach, it offers a rigorous, complete and clear improvement of conventional portfolio development methodologies relevant if fat-tails are absent. It then explains how one can refine these methodologies to accommodate actual world behaviour.

Throughout, the guide highlights the significance of professional opinion, displaying that even the most data-centric portfolio development approaches finally depend upon practitioner assumptions about how the world would possibly behave.

The guide contains:

• Key ideas and strategies concerned in analysing excessive occasions
• A complete remedy of mean-variance investing, Bayesian strategies, market constant approaches, danger budgeting, and their utility to supervisor and instrument choice
• A scientific improvement of the refinements wanted to conventional portfolio development methodologies to cater for fat-tailed behaviour
• Latest developments in stress testing and again testing methodologies
• A robust deal with the sensible implementation challenges that may come up at every step in the course of and on how one can overcome these challenges

“Understanding how to model and analyse the risk of extreme events is a crucial part of the risk management process. This book provides a set of techniques that allow practitioners to do this comprehensively.”
Paul Sweeting, Professor of Actuarial Science, University of Kent

“How can the likeliness of crises affect the construction of portfolios? This question is highly topical in times where we still have to digest the last financial collapse. Malcolm Kemp gives the answer. His book is highly recommended to experts as well as to students in the financial field.”
Christoph Krischanitz, President Actuarial Association of Austria, Chairman WG “Market Consistency” of Groupe Consultatif

 

Table of Contents

Preface.

Acknowledgements.

Abbreviations.

Notation.

1 Introduction.

1.1 Extreme occasions.

1.2 The portfolio development drawback.

1.3 Coping with actually excessive occasions.

1.4 Risk budgeting.

1.5 Elements designed to maximise profit to readers.

1.6 Book construction.

2 Fat Tails – In Single (i.e., Univariate) Return Series.

2.1 Introduction.

2.2 A fats tail relative to what?

2.3 Empirical examples of fat-tailed behaviour in return collection.

2.4 Characterising fat-tailed distributions by their moments.

2.5 What causes fats tails?

2.6 Lack of diversification.

2.7 A time-varying world.

2.8 Stable distributions.

2.9 Extreme worth principle (EVT).

2.10 Parsimony.

2.11 Combining completely different doable supply mechanisms.

2.12 The practitioner perspective.

2.13 Implementation challenges.

3 Fat Tails – In Joint (i.e., Multivariate) Return Series.

3.1 Introduction.

3.2 Visualisation of fats tails in a number of return collection.

3.3 Copulas and marginals – Sklar’s theorem.

3.4 Example analytical copulas.

3.5 Empirical estimation of fats tails in joint return collection.

3.6 Causal dependency fashions.

3.7 The practitioner perspective.

3.8 Implementation challenges.

4 Identifying Factors That Significantly Influence Markets.

4.1 Introduction.

4.2 Portfolio danger fashions.

4.3 Signal extraction and principal parts evaluation.

4.4 Independent parts evaluation.

4.5 Blending collectively principal parts evaluation and unbiased parts evaluation.

4.6 The potential significance of choice results.

4.7 Market dynamics.

4.8 Distributional mixtures.

4.9 The practitioner perspective.

4.10 Implementation challenges.

5 Traditional Portfolio Construction Techniques.

5.1 Introduction.

5.2 Quantitative versus qualitative approaches?

5.3 Risk-return optimisation.

5.4 More common options of mean-variance optimisation.

5.5 Manager choice.

5.6 Dynamic optimisation.

5.7 Portfolio development in the presence of transaction prices.

5.8 Risk budgeting.

5.9 Backtesting portfolio development strategies.

5.10 Reverse optimisation and implied view evaluation.

5.11 Portfolio optimisation with choices.

5.12 The practitioner perspective.

5.13 Implementation challenges.

6 Robust Mean-Variance Portfolio Construction.

6.1 Introduction.

6.2 Sensitivity to the enter assumptions.

6.3 Certainty equivalence, credibility weighting and Bayesian statistics.

6.4 Traditional strong portfolio development approaches.

6.5 Shrinkage.

6.6 Bayesian approaches utilized to place sizes.

6.7 The ‘universality’ of Bayesian approaches.

6.8 Market constant portfolio development.

6.9 Resampled mean-variance portfolio optimisation.

6.10 The practitioner perspective.

6.11 Implementation challenges.

7 Regime Switching and Time-Varying Risk and Return Parameters.

7.1 Introduction.

7.2 Regime switching.

7.3 Investor utilities.

7.4 Optimal portfolio allocations for regime switching fashions.

7.5 Links with by-product pricing principle.

7.6 Transaction prices.

7.7 Incorporating extra complicated autoregressive behaviour.

7.8 Incorporating extra intrinsically fat-tailed behaviour.

7.9 More heuristic methods of dealing with fats tails.

7.10 The practitioner perspective.

7.11 Implementation challenges.

8 Stress Testing.

8.1 Introduction.

8.2 Limitations of present stress testing methodologies.

8.3 Traditional stress testing approaches.

8.4 Reverse stress testing.

8.5 Taking due account of stress assessments in portfolio development.

8.6 Designing stress assessments statistically.

8.7 The practitioner perspective.

8.8 Implementation challenges.

9 Really Extreme Events.

9.1 Introduction.

9.2 Thinking exterior the field.

9.3 Portfolio goal.

9.4 Uncertainty as a truth of life.

9.5 Market implied knowledge.

9.6 The significance of good governance and operational administration.

9.7 The practitioner perspective.

9.8 Implementation challenges.

10 The Final Word.

10.1 Conclusions.

10.2 Portfolio development ideas in the presence of fats tails.

Appendix: Exercises.

A.1 Introduction.

A.2 Fat tails – In single (i.e., univariate) return collection.

A.3 Fat tails – In joint (i.e., multivariate) return collection.

A.4 Identifying elements that considerably affect markets.

A.5 Traditional portfolio development strategies.

A.6 Robust mean-variance portfolio development.

A.7 Regime switching and time-varying danger and return parameters.

A.8 Stress testing.

A.9 Really excessive occasions.

References.

Index.

 

Author Information

Malcolm Kemp (London, UK) is Founder and Managing director of Nematrian Ltd, a consulting agency delivering companies to the quantitative finance and actuarial communities. Previously, he was Director and Head of the Quantitative Research Team at Threadneedle Asset Management, accountable for the by-product desk and its portfolio danger measurement and administration actions. He is a number one professional on derivatives, efficiency measurement, danger measurement, legal responsibility pushed funding and different quantitative funding strategies. Prior to this, Malcolm was a associate at Bacon & Woodrow in their funding consultancy follow. He holds a firstclass diploma in Mathematics from Cambridge University and can also be a Fellow of the Institute of Actuaries. He is an everyday on the convention circuit, together with Risk Europe and GARP occasions the place he speaks on a spread of portfolio administration and derivatives matters.