**Autumn Seminar – September **

**Sunday 11th – Wednesday 14th**

**For Event Registration, click here**

We have reserved rooms at the old Oxford prison now the Malmaison Hotel and Worcester College.

There is great demand for rooms in Oxford in early September. To simplify administration and ensure that we could reserve as many rooms as possible we offer room reservation with seminar registration combined. Book now to guarantee both a seminar place and accommodation.

If the seminar and room tickets have sold out you can book a seminar only ticket but you’ll need to find accommodation…. Don’t leave it too late.

All room bookings are for three nights from the evening of the 11th to the morning of the 14th September.

**Sunday September 11th**

19:00 Welcome Reception and Dinner at the Malmaison Hotel

**Monday September 12th**

**Dan diBartolomeo** – Founder and President, Northfield Information Systems

**Reconciliation of Default Risk and Spread Risk in Fixed Income**

**Abstract**

There are two conflicting concepts of what credit risk actually is. The classic definition has to do with the likelihood that a given fixed income instrument will default (Probability of Default, PD), and the expected severity of economic loss in the event of a default (Loss Given Default, or LGD). In this view the focus is on the “tail risk” (negative skew in the return distribution) associated with a singular default event. Many fixed income market participants prefer to think of a given fixed income instrument as offering a credit related yield spread above a comparable duration riskless instrument. These investors think of credit risk as the volatility of the credit yield spread and related impact on the market value of an instrument (conditional on the duration). Even more chaste, some fixed income participants simply use at “duration time spread” (DTS) as a market implied measure of risk. If investors are not risk-neutral, the credit spread will compensate investors for their expected loss (PD*LGD), plus provide a risk premium to induce risk averse investors to hold these instruments. These concepts of credit risk are not equivalent because credit spreads can change over time both because of changes in expected loss, and separately because aggregate investor risk aversion can change, forcing a change in the risk premium (incremental yield) which fixed income borrowers must pay. In this presentation we will review the relevant approaches to credit risk, and illustrate how to reconcile the three views in order to satisfy the default risk concerns of “buy and hold” investors, while simultaneously explaining yield spread volatility for investors who are more concerned with controlling variation in period to period returns.

**Mark Kritzman** – CEO of Windham Capital Management

**Advances in Factor Replication**

**Abstract**Factor investing has gained widespread acceptance among institutional investors. Factors such as economic variables are not directly investable. Investors, therefore, need to identify a combination of securities that tracks the movements in the economic variable. Other factors, however, are directly investable, such as securities with a certain attribute. Often times, though, investors choose to invest in a sub-set of the factor securities that are inexpensive to trade, or they choose to rebalance less frequently to reduce trading costs. In order to identify the best factor-tracking portfolio, investors must estimate covariances from historical observations whose realizations in the future are prone to several types of estimation error. Conventional approaches for mitigating estimation error in covariances, such as Bayesian shrinkage and resampling, are ineffective if the replicating portfolio weights include both long and short positions that sum to zero. We introduce a non-parametric procedure to account for estimation error, which enables us to incorporate the relative stability of covariances directly into the factor replication process. We show that, unlike Bayesian shrinkage and resampling, adjusting for the stability of covariances in this way produces replicating portfolios that are significantly more reliable than portfolios that are blind to estimation error.

**André Perold**– CIO and Co-Managing Partner of HighVista Strategies, George Gund Professor of Finance and Banking, Emeritus, Harvard Business School

**Risk Stabilization and Asset Allocation**

**Abstract** When asset class risks are time-varying, investors need to decide whether and how to adjust their asset allocations in response to changing estimates of risk. Traditional constant proportion policies such as 60/40 equities/bonds are unlikely to be optimal. In this session, I will examine properties of risk-conditioned strategies, including risk-stabilized portfolios that are managed to a constant conditional expected variance. I derive the portfolio Sharpe Ratio and kurtosis as a function of a) the relationship between expected return and volatility, b) the volatility of volatility, and c) the predictability of volatility. Within this framework, risk stabilized portfolios have the lowest exposure to fat tails. They have attractive Sharpe Ratios for a reasonable range of parameter values, and they have the highest Sharpe Ratio when the conditional Sharpe Ratio of the risky asset is constant.

**Steve Satchell** – Economics Fellow at Trinity College Cambridge

**What proportion of the time are markets efficient?**

**Abstract**

We assume that log equity prices follow multi-state threshold autoregressions and generalize existing results for threshold autoregressive models, presented in Knight and Satchell (2012) for the existence of a stationary process and the conditions necessary for the existence of a mean and a variance; we also present formulae for these moments. Using a simulation study we explore what these results entail with respect to the impact they can have on tests for detecting bubbles or market efficiency. We find that bubbles are easier to detect in processes where a stationary distribution does not exist. Furthermore, we explore how threshold autoregressive models with iid trigger variables may enable us to identify how often asset markets are inefficient. We find, unsurprisingly, that the fraction of time spent in an efficient state depends upon the full specification of the model; the notion of how efficient a market is, in this context at least, a model-dependent concept. However, our methodology allows us to compare efficiency across different asset markets.

**Micheal Steliaros** – Managing Director at Bank of America Merrill Lynch

**Cherry Muijsson** – BlackRock

**Tuesday September 13th**

**Victor DeMiguel** – Professor and Subject Area Chair Management Science and Operations, London Business School

**Fifty Ways to Beat the Market?**

**Abstract**

More than 300 characteristics have been proposed to explain the cross-section of stock returns. The existing literature employs Fama-MacBeth regressions to examine which characteristics are significant when considered jointly, but this approach ignores portfolio selection features such as diversification and transaction costs. We study which characteristics are jointly significant for portfolio construction and why.

**Ron Kahn** – Managing Director, Global Head of Scientific Equity Research at BlackRock

TBD

**James Sefton** – Professor at Imperial College London

TBD

**Jason MacQueen** – Legend and Head of Research Northfield Information Systems.

TBD

**An afternoon of Punting**

**Wednesday September 14th**

**Marielle De Jong** – Head of Fixed-Income Quant Research, Amundi

**Fundamental bond index including solvency criteria**

**Chris Watkins** – University of London – Dept. of Computer Science

Visualising covariance

**Ed Fishwick** – Managing Director and Global Co-Head of Risk and Quantitative Analysis at BlackRock

TBD

**Venue: Worcester College, Walton St, Oxford OX1 2HB**