Future long-horizon performance measurement conditional on past survival

This paper examines the measurement of long-horizon abnormal performance when stock selection is conditional on an extended period of past survival. Filtering on survival results in a sample driven towards more-established, frequently traded stocks and this has implications for the choice of benchmark used in performance measurement (especially in the presence of the well-documented size effect). A simulation study is conducted to document the properties of commonly employed performance measures conditional on past survival. The results suggest that the popular index benchmarks used in long-horizon event studies are severely biased and yield test statistics that are badly misspecified. In contrast, a matched-stock benchmark based on size and industry performs consistently well. Also, an eligible-stock index designed to mitigate the influence of the size effect proves effective.

Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

Long-run restrictions and survey forecasts of output, consumption and investment

We consider the extent to which long-horizon survey forecasts of consumption, investment and output growth are consistent with theory-based steady-state values, and whether imposing these restrictions on long-horizon forecasts will enhance their accuracy. The restrictions we impose are consistent with a two-sector model in which the variables grow at different rates in steady state. The restrictions are imposed by exponential-tilting of simple auxiliary forecast densities. We show that imposing the consumption-output restriction yields modest improvements in the long-horizon output growth forecasts, and larger improvements in the forecasts of the cointegrating combination of consumption and output: the transformation of the data on which accuracy is assessed plays an important role.

United States still no closer to database legislation

Last year European Intellectual Property Review published an article comparing the latest version of the proposed US database legislation, the Collections of Information Antipiracy Bill with the UK's Copyright and Rights in Database Regulations 1997. Subsequently a new US Bill, the Consumer and Investor Access to Information Act has emerged, the Antipiracy Bill has been amended and much debate has occurred, but the US seems no closer to enacting database legislation. This article briefly outlines the background to the US legislative efforts, examines the two Bills and draws some comparisons with the UK Regulations. A study of the US Bills clearly demonstrates the starkly divided opinion on database protection held by the Bills' proponents and the principal lobby groups driving the legislative efforts: the Antipiracy Bill is very protective of database producers' interests, whereas the Access Bill is heavily user-oriented. If the US experience is any indication there will be a long horizon involved in achieving any consensus on international harmonisation of this difficult area.

Jackknife for bias reduction in predictive regressions

One of the fundamental econometric models in finance is predictive regression. The standard least squares method produces biased coefficient estimates when the regressor is persistent and its innovations are correlated with those of the dependent variable. This article proposes a general and convenient method based on the jackknife technique to tackle the estimation problem. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. The effectiveness of the proposed method is demonstrated by simulations. An empirical application to equity premium prediction using the dividend yield and the short rate highlights the differences between the results by the standard approach and those by the bias-reduced estimator. The significant predictive variables under the ordinary least squares become insignificant after adjusting for the finite-sample bias. These discrepancies suggest that bias reduction in predictive regressions is important in practical applications.

In conversation with the Honourable Ian Callinan AC

When, in 1977, the Australian electorate provided a double majority to effect a change of section 72 of the Commonwealth Constitution requiring judges of the High Court of Australia to retire at the age of 70 years old, I doubt we understood the continuing capacity of these esteemed members of the judiciary. For the opportunity to sit and talk with Ian Callinan AC who, in compliance with that amendment, retired from the High Court in September 2007, I needed to wait until he returned from The Hague where he was sitting as a Judge ad hoc on the International Court of Justice. Although a native of Casino, New South Wales, Mr Callinan is regarded as a Queenslander. Indeed, he grew up in Brisbane, finished high school at Brisbane Grammar and graduated in law at The University of Queensland. Appointed in 1978 as a Queen’s Counsel, Mr Callinan enjoyed this period of his legal career and we discussed an aspect of the Christopher Skase case, which reinforced my belief that Mr Callinan is an incredibly skilful advocate. On 14 September 1998, ABC Four Corners broadcasted the views of some prominent Australians on the appointment of Mr Callinan to the High Court. In assessing the type of person Mr Callinan is, Tony Morris QC said: “Ian Callinan isn't a coward”, while former Commonwealth Attorney-General, Michael Lavarch, said: “He was regarded as an absolutely outstanding criminal lawyer within the Queensland legal profession, I mean really a top-notch advocate”. I was not interested in raising any of the controversial issues that Mr Callinan has encountered as an advocate in high profile matters. I wanted to know how he felt about his time on the High Court, what his thoughts are on the operation of the High Court, the IP cases he decided, the real life issues that he feels impact on counsel who are appearing before the High Court and the people he regarded as role models. During our conversation, Mr Callinan laughed often and when he did his eyes lit up, revealing his passion for life. He is an incredibly genuine Australian who loved his time as a barrister, enjoyed his role on the High Court, enjoys his current job as mediator, loves writing novels, has a great desire for continual improvement in the quality of legal education and legal advocacy and sees a need for change in IP law. When I asked: “So, what might the future hold for you?”, he laughed and said: “Well, at my age I don’t have a long horizon time”. I said: “Just enjoy the journey?”, to which Mr Callinan responded: “Exactly”.

The value of tail risk hedging in defined contribution plans: What does history tell us

Hedging against tail events in equity markets has been forcefully advocated in the aftermath of recent global financial crisis. Whether this is beneficial to long horizon investors like employees enrolled in defined contribution (DC) plans, however, has been subject to criticism. We conduct historical simulation since 1928 to examine the effectiveness of active and passive tail risk hedging using out of money put options for hypothetical equity portfolios of DC plan participants with 20 years to retirement. Our findings show that the cost of tail hedging exceeds the benefits for a majority of the plan participants during the sample period. However, for a significant number of simulations, hedging result in superior outcomes relative to an unhedged position. Active tail hedging is more effective when employees confront several panic-driven periods characterized by short and sharp market swings in the equity markets over the investment horizon. Passive hedging, on the other hand, proves beneficial when they encounter an extremely rare event like the Great Depression as equity markets go into deep and prolonged decline.

Forecasting the Yield Curve of Government Bonds: A Comparative Study

For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields

The existence and source of stock return predictability : Evidence from dividend, output and consumption ratios

Date of Acceptance: 13/03/2015

The existence and source of stock return predictability : Evidence from dividend, output and consumption ratios

Date of Acceptance: 13/03/2015

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.

Soil carbon saturation : implications for measurable carbon pool dynamics in long-term incubations

The efficiency of agricultural management practices to store SOC depends on C input level and how far a soil is from its saturation level (i.e. saturation deficit). The C Saturation hypothesis suggests an ultimate soil C stabilization capacity defined by four SOM pools capable of C saturation: (1) non-protected, (2) physically protected, (3) chemically protected and (4) biochemically protected. We tested if C saturation deficit and the amount of added C influenced SOC storage in measurable soil fractions corresponding to the conceptual chemical, physical, biochemical, and non-protected C pools. We added two levels of C-13- labeled residue to soil samples from seven agricultural sites that were either closer to (i.e., A-horizon) or further from (i.e., C-horizon) their C saturation level and incubated them for 2.5 years. Residue-derived C stabilization was, in most sites, directly related to C saturation deficit but mechanisms of C stabilization differed between the chemically and biochemically protected pools. The physically protected C pool showed a varied effect of C saturation deficit on C-13 stabilization, due to opposite behavior of the POM and mineral fractions. We found distinct behavior between unaggregated and aggregated mineral-associated fractions emphasizing the mechanistic difference between the chemically and physically protected C-pools. To accurately predict SOC dynamics and stabilization, C Saturation of soil C pools, particularly the chemically and biochemically protected pools, should be considered. (C) 2008 Elsevier Ltd. All rights reserved.