Events 2005-2006

Dr Daniel Sgroi

Senior Research Associate, Faculty of Economics, University of Cambridge

Informational Herding in Financial Market

This talk sets out to explain the theoretical background to rational informational herding as a general phenomenon and how it can be applied to financial markets. The theory shows how rational learning through observation can easily result in the propagation of incorrect views and suboptimal outcomes in fixed price environments.
When paired with efficient flexible prices it was long thought that informational herding could not explain suboptimality or clustered behaviour in efficient financial markets. However, recent work involving more complex and realistic modelling of informational structures has shown that the theory may well be a powerful explanation of clustered decision-making by traders.

Professor Jagjit Chadha

Chief Quantitative Economist, BNP Paribas, London

Macro-economic Models and the Yield Curve: An assessment of the Fit

Many have questioned the empirical relevance of the Calvo-Yun model. This paper appends three widely-studied macroeconomic models (Calvo-Yun, Hybrid and Svensson) with forward rate curves. We back out from observations on the yield curve the underlying macroeconomic model that most closely matches the level, slope and curvature of the yield curve. With each model we trace the response of the yield curve to macroeconomic shocks. We assess the fit of each model with the observed behaviour in forward rates. We find limited support for Calvo-Yun model in terms of fit with the observed yield curve but we find some support for each of the Hybrid and Svensson models. We conclude that macroeconomic persistence seems to be priced into the yield curve.

Professor William Shaw

Professor of Financial Mathematics, King's College London

Efficient methods for managing 'Student's' T Distribution in Equity, VaR and Credit applications

With the current interest in copula methods, and fat-tailed or other non-Normal distributions, it is appropriate to investigate technologies for managing marginal distributions of interest. We explore “Student’s” T distribution, survey its simulation, and present some new techniques for simulation. In particular, for a given real (not necessarily integer) value n of the number of degrees of freedom, we give a pair of power series approximations for the inverse of (Fn)^-1 the cumulative distribution function (CDF) Fn. We also give some simple and very fast exact and iterative techniques for defining this function when n is an even integer, based on the observation that for such cases the calculation of (Fn)^-1 amounts to the solution of a reduced-form polynomial equation of degree n−1. We also explain the use of Cornish-Fisher expansions to define the inverse CDF as the composition of the inverse CDF for the Normal case with a simple polynomial map. The methods presented are well adapted for use with copula and quasi-Monte-Carlo techniques.

Dr Peter Jaeckel

Global Head of Credit, Hybrid, Inflation and Commodity Derivative Analytics, ABN AMRO, London

Semi-analytic valuation of credit linked swaps in a Black-Karasinski framework

We present a simple model for the valuation of credit linked swaps in a framework that allows for strictly positive default hazard rates and permits explicit control over the market-observable skew of implied volatilities for options on the underlying swap. We discuss different aspects of calibration depending on the nature of the underlying swap. For speedy numerical evaluation, the resulting pricing equations are reduced to a dimensionality-pruned quadrature over a generic Ornstein-Uhlenbeck process path space.

Dr Stacy Williams

Associate Director, Quantitative Strategy and Model Trading, HSBC Global Markets

Model Making and Risk Taking

The demand for quantitative expertise in financial institutions has grown rapidly in recent years. Both investment banks, and the fund management industry which they serve, continue to build their quantitative capabilities and the pace is quickening. This talk looks at the various and very different roles occupied by quant professionals in financial markets, and examines what they actually do, and the reasons why there is such high demand. Quantitative trading, in particular, is enjoying a surge of interest and a great deal of model development is taking place on systems trading desks and hedge funds around the world. How are these models constructed and do they really make money?

Professor Michael Dempster

Centre for Financial Research, Judge Business School
Cambridge Systems Associates

An automated FX trading system using adaptive reinforcement learning

This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully automated trading system application. The system is designed to trade foreign exchange (FX) markets and relies on a layered structure consisting of a machine learning algorithm, a risk management overlay and a dynamic utility optimization layer. An existing machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL. One of the strengths of our approach is that the dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary. It also allows for a risk-return trade-off to be made by the user within the system. The trading system is able to make consistent gains out-of-sample while avoiding large draw-downs.

Dr Frank McGroarty

School of Management, University of Southampton

Microstructure Effects, Bid-ask Spreads and Volatility in the Spot Foreign Exchange Market Pre and Post-EMU

This talk examines how microstructure effects, evident in high frequency data, influence bid-ask spreads and volatility in transaction price series. It uses the event of the European Monetary Union (EMU), and the upheaval that this entailed, as an opportunity to empirically investigate these relationships in the electronic inter-dealer spot FX market. The microstructure effects relate to both price and time. There are two price effects, namely price discreteness and price clustering, and two time effects, namely the time elapsed between sample periods and the time-gap between successive trades or quoted price submissions. Strong evidence emerges that all four factors are important in the determination of bid-ask spreads.

Professor Michael Dempster

Centre for Financial Research, Judge Business School
Cambridge Systems Associates

Volatility-Induced Financial Growth: Modelling FX Market Makers

After describing the current state and structure of the global FX market, this talk will describe an FX trading strategy which can be proven to produce exponential portfolio growth asymptotically almost surely. Extension of these ideas to current work on modelling market maker behaviour will also be considered.

Professor Bernard Dumas

Professor of Finance, INSEAD

What can Rational Investors Do About Excessive Volatility and Sentiment Fluctuations?

Our objective is to understand the trading strategy that would allow an investor to take advantage of "excessive" stock price volatility and "sentiment" fluctuations. We construct a general equilibrium model of sentiment. In it, there are two classes of agents and stock prices are excessively volatile because one class is overconfident about a public signal. As a result, this class of irrational agents changes its expectations too often, sometimes being excessively optimistic, sometimes being excessively pessimistic. We determine and analyse the trading strategy of the rational investors who are not overconfident about the signal. We find that because irrational traders introduce an additional source of risk, rational investors reduce the proportion of wealth invested into equity except where they are extremely optimistic about future growth. Moreover, their optimal portfolio strategy is based not just on a current price divergence but also on a model of irrational behaviour and a prediction concerning the speed of convergence. Thus, the portfolio strategy includes a protection in case there is a deviation from that prediction. We find that long maturity bonds are an essential accompaniment of equity investment, as they serve to hedge this "sentiment" risk." Even though rational investors find it beneficial to trade on their belief that the market is excessively volatile, the answer to the question posed in the title is: "There is little that rational investors can do optimally to exploit, and hence, eliminate excessive volatility, except in the very long run."

Dr Chris Jones

CIO, Key Asset Management
Senior Associate, Judge Business School

Hedge Funds Past, Present and Future

● Key changes in the hedge fund world over the last 15 years
● The current hedge fund environment
● New developments in hedge funds
● The future for hedge funds

Dr Peter Friz

Statistical Laboratory, DPMMS

Volatility derivatives and the implied volatility smile

We present some results on the pricing of Volatility Derivatives. At least in the zero-correlation case the volatility smile contains all the information needed. (joint work with J. Gatheral). This and everyday calibration issues in the industry make it necessary to understand extrapolation of the implied volatility smile. We present some asymptotic techniques related to Roger Lee's moment formula (joint work with S. Benaim).

Dr John Crosby

Global Head of Quantitative Analytics + Research, Lloyds TSB Financial Markets

A multi-factor jump-diffusion model for commodities

We develop an arbitrage-free model for the pricing and risk management of commodity derivatives. The model generates futures (or forward) commodity prices consistent with any initial term structure. It is consistent with mean reversion in commodity prices, which is an empirically observed stylised fact about commodities markets, and it also generates stochastic convenience yields. Our model is a multi-factor jump-diffusion model, one version of which allows for long-dated futures contracts to jump by smaller amounts than short-dated futures contracts, which, to our knowledge, is a feature that has not previously appeared in the literature, in spite of it being in line with stylised empirical observations (especially in the natural gas and electricity markets). Finally, our model also allows for stochastic interest-rates. We show that within our model, it is possible to very rapidly price standard European options by two different and complementary methodologies. This opens up the possibility of calibrating the model parameters by deriving implied parameters from the market prices of options. Armed with the model parameters, it is easily possible to price complex (exotic) commodity derivatives within our model, using Monte Carlo simulation. The model is intuitive, straightforward to implement and appears suitable for modelling the prices of almost any commodity.

Dr David R Gallagher

Associate Professor of Finance, School of Banking and Finance, University of New South Wales
Director of the Finance (Hons) Co-op Programme

The Performance and Transaction Costs of Small-Cap Equity Fund Managers

This study presents evidence of significant stock selection skill on a risk-adjusted basis for small-cap equity managers. Our results are robust across three distinct observation units – total fund return, portfolio holdings and daily trades. More importantly, the magnitude of performance generated by managers in our sample indicates superior managerial ability, from both a statistical and economic perspective (even after controlling for transaction costs). The average monthly alphas range between 59.6 and 76.1 basis points, while the cumulative abnormal returns (CARs) over a one-month period for holdings-based and transactions-based metrics are 59.7 and 64.1 basis points, respectively. Our research provides important out-of-sample evidence concerning the value of active management, in a market segment which exhibits both lower liquidity and analyst coverage.