Asset selection using Factor Model and Data Envelope Analysis - A Quantile Regression approach

With the growing number of stocks and other financial instruments in the investment market, there is always a need for profitable methods of asset selection. The Fama-French three factor model, makes the problem of asset selection easy, by narrowing down the number of parameters, but the usual technique of Ordinary Least Square (OLS), used for estimation of the coefficients of the three factors suffers from the problem of modelling using the conditional mean of the distribution, as is the case with OLS. In this paper, we use the technique of Data Envelopment Analysis (DEA) applied to the Fama-French Three Factor Model, to choose stocks from Dow Jones Industrial Index. We use a more robust technique called as Quantile Regression to estimate the coefficients for the factor model and show that the assets selected using this regression method form a higher return equally weighted portfolio.Asset Selection, Factor Model, DEA, Quantile Regression

CAViaR and the Australian Stock Markets: An Appetiser

Value-at-Risk (VaR) has become the universally accepted metric adopted internationally under the Basel Accords for banking industry internal control and for regulatory reporting. This has focused attention on methods of measuring, estimating and forecasting lower tail risk. One promising technique is Quantile Regression which holds the promise of efficiently calculating (VAR). To this end, Engle and Manganelli in (2004) developed their CAViaR model (Conditional Autoregressive Value at Risk). In this paper we apply their model to Australian Stock Market indices and a sample of stocks, and test the efficacy of four different specifications of the model in a set of in and out of sample tests. We also contrast the results with those obtained from a GARCH(1,1) model, the RiskMetricsTM model and an APARCH modelVaR; Quantile regressions; Autoregressive; CAViaR

Comparing Australian and US Corporate Default Risk using Quantile Regression

The severe bank stresses of the Global Financial Crisis (GFC) have underlined the importance of understanding and measuring extreme credit risk. The Australian economy is widely considered to have fared much better than the US and most other major world economies. This paper applies quantile regression and Monte Carlo simulation to the Merton structural credit model to investigate the impact of extreme asset value fluctuations on default probabilities of Australian companies in comparison to the USA. Quantile regression allows modelling of the extreme quantiles of a distribution which allows measurement of capital and PDs at the most extreme points of an economic downturn, when companies are most likely to fail. Daily asset value fluctuations of over 600 Australian and US investment and speculative entities are examined over a ten year period spanning pre-GFC and GFC. The events of the GFC also showed how the capital of global banks was eroded as defaults increased. This paper therefore also examines the impact of these fluctuating default probabilities on the capital adequacy of Australian and US banks. The paper finds highly significant variances in default probabilities and capital between quantiles in both Australia and the US, and shows how these variances can assist banks and regulators in calculating capital buffers to sustain banks through volatile times.Classification-JEL:Probability of default; Quantile regression; Australian banks; United States banks.

Tail Risk for Australian Emerging Market Entities

Whilst the Australian economy is widely considered to have fared better than many of its global counterparts during the Global Financial Crisis, there was nonetheless extreme volatility experienced in Australian financial markets. To understand the extent to which emerging Australia entities were impacted by these extreme events as compared to established entities, this paper compares entities comprising the Emerging Markets Index (EMCOX) to established entities comprising the S&P/ASX 200 Index using four risk metrics. The first two are Value at Risk (VaR) and Distance to Default (DD), which are traditional measures of market and credit risk. The other two focuses on extreme risk in the tail of the distribution and include Conditional Value at Risk (CVaR) and Conditional Distance to Default (CDD), the latter metric being unique to the authors, and which applies CVaR techniques to default measurement. We apply these measures both prior to and during the GFC, and find that Emerging Market shares show higher risk for all metrics used, the spread between the emerging and established portfolios narrows during the GFC period and that the default risk spread between the two portfolios is greatest in the tail of the distribution. This information can be important to both investors and lenders in determining share or loan portfolio mix in extreme economic circumstances. Classification-JEL:Conditional value at risk; Conditional distance to default; Australian emerging markets

Minimizing loss at times of financial crisis : quantile regression as a tool for portfolio investment decisions

The worldwide impact of the Global Financial Crisis on stock markets, investors and fund managers has lead to a renewed interest in tools for robust risk management. Quantile regression is a suitable candidate and deserves the interest of financial decision makers given its remarkable capabilities for capturing and explaining the behaviour of financial return series more effectively than the ordinary least squares regression methods which are the standard tool. In this paper we present quantile regression estimation as an attractive additional investment tool, which is more efficient than Ordinary Least Square in analyzing information across the quantiles of a distribution. This translates into the more accurate calibration of asset pricing models and subsequent informational gains in portfolio formation. We present empirical evidence of the effectiveness of quantile regression based techniques as applied across the quantiles of return distributions to derive information for portfolio formation. We show, via stocks in Dow Jones Industrial Index, that at times of financial setbacks such as the Global Financial Crisis, a portfolio of stocks formed using quantile regression in the context of the Fama-French three factor model, performs better than the one formed using traditional OLS

CAViaR and the Australian stock markets : an appetiser

Value-at-Risk (VaR) has become the universally accepted metric adopted internationally under the Basel Accords for banking industry internal control and for regulatory reporting. This has focused attention on methods of measuring, estimating and forecasting lower tail risk. One promising technique is Quantile Regression which holds the promise of efficiently calculating (VAR). To this end, Engle and Manganelli in (2004) developed their CAViaR model (Conditional Autoregressive Value at Risk). In this paper we apply their model to Australian Stock Market indices and a sample of stocks, and test the efficacy of four different specifications of the model in a set of in and out of sample tests. We also contrast the results with those obtained from a GARCH(1,1) model, the RiskMetricsTM model and an APARCH model

A Highly Available Cluster of Web Servers with Increased Storage Capacity

Ponencias de las Decimoséptimas Jornadas de Paralelismo de la Universidad de Castilla-La Mancha celebradas el 18,19 y 20 de septiembre de 2006 en AlbaceteWeb servers scalability has been traditionally solved by improving software elements or increasing hardware resources of the server machine. Another approach has been the usage of distributed architectures. In such architectures, usually, file al- location strategy has been either full replication or full distribution. In previous works we have showed that partial replication offers a good balance between storage capacity and reliability. It offers much higher storage capacity while reliability may be kept at an equivalent level of that from fully replicated solutions. In this paper we present the architectural details of Web cluster solutions adapted to partial replication. We also show that partial replication does not imply a penalty in performance over classical fully replicated architectures. For evaluation purposes we have used a simulation model under the OMNeT++ framework and we use mean service time as a performance comparison metric.Publicad

Pressure-induced hole doping of the Hg-based cuprate superconductors

We investigate the electronic structure and the hole content in the copper-oxygen planes of Hg based high Tc cuprates for one to four CuO2 layers and hydrostatic pressures up to 15 GPa. We find that with the pressure-induced additional number of holes of the order of 0.05e the density of states at the Fermi level changes approximately by a factor of 2. At the same time the saddle point is moved to the Fermi level accompanied by an enhanced k_z dispersion. This finding explains the pressure behavior of Tc and leads to the conclusion that the applicability of the van Hove scenario is restricted. By comparison with experiment, we estimate the coupling constant to be of the order of 1, ruling out the weak coupling limit.Comment: 4 pages, 4 figure

A dynamic credit ratings model

The Global Financial Crisis (GFC) provided overwhelming evidence of the problems caused by inadequate credit ratings. Losses and problem loans experienced by banks over this period were staggering. Yet many of the securitized sub-prime parcels which were widely seen as an underlying cause of the GFC, as well as corporate obligors who experienced severe difficulties during the GFC, retained extremely strong external credit ratings. They may have had low perceived risk at the time of rating, but as circumstances changed, the ratings stayed static and became far removed from the underlying risk. A key problem is that the external credit ratings do not fluctuate with changing economic circumstances. Whilst there are models which measure changing default risk, they are not linked to credit ratings and it is often the rating itself, not the underlying risk that drives behavior, such as the purchase of securitized parcels, the pricing of credit risk, and the allocation of capital for credit risk, which under the Basel standardized model for corporates is based on the rating itself. This problem is exacerbated by the fact that these ratings carry descriptors such as “extremely strong capacity”. This descriptor may no longer be appropriate for the rated company if the market turns dramatically, yet the rating and descriptor remain unchanged. To overcome this problem, this paper shows how an innovative fluctuating credit ratings model can be generated by linking the Merton structural credit model to a credit ratings framework. The Merton model measures fluctuations in daily asset values and, using a combination of these fluctuating asset values and the capital structure of a company, it measures Distance to Default (DD) and the Probability of Default (PD) associated with each DD. Under the Merton structural model, default occurs when the firm’s debt exceeds asset values. Thus as fluctuations in asset values become more volatile, DD also becomes more volatile and PD increases. External raters such as Moody’s provide PD’s associated with each rating. Thus by using the Merton model, we are able to generate PDs which fluctuate over time and link these PD’s to credit ratings. Therefore, as our PD’s fluctuate, so do the credit ratings. To illustrate our approach, we apply this model to a French motor vehicle company (Renault) which experienced severe distress during the GFC. We compare the Moody’s rating changes that took place for Renault over the 2006 – 2009 period, which captures the events leading up to and during the GFC. Over this period, only three Moody’s external ratings changes took place and throughout this period, Renault stayed in the Moody’s ‘moderate’ risk band. Based on this, an investor would likely assume the company was in reasonable financial health, and a bank would not be required to change its capital allocation for this company if it was a borrower. Yet during this period, the company experienced such severe financial problems that it had to be bailed out by the French Government. Our model, on the other hand, recognizes these stresses far quicker, starting with rating downgrades for Renault from August 2007 and moving downwards through several risk bands, from ‘moderate’ to ‘substantial’ to ‘high’ and then to ‘very high’ credit risk. This downward spiral is far more in keeping with the actual problems experienced by Renault than the static ‘moderate’ risk tag would indicate. We thus find that the new model responds extremely rapidly to changing economic circumstances to produce ratings which can far more accurately depict the underlying credit risk of a corporate obligor in these times than prevailing external rating methods. The new ratings can benefit bond investors and banks through improved knowledge of the underlying credit risk of bonds and of corporate borrowers. As capital adequacy can also be linked to credit ratings, an improved rating model can assist banks and regulators to better measure required capital adequacy to protect against economic downturns

Make Me Democratic, But Not Yet: Sunrise lawmaking and Democratic Constitutionalism

Sunrise amendments -constitutional provisions that only take effect after a substantial time delay-could revolutionize American politics. Yet they remain undertheorized and unfamiliar. This Article presents the first comprehensive examination of sunrise lawmaking. It first explores a theoretical puzzle. On the one hand, sunrise lawmaking resuscitates the possibility of using Article V amendments to forge a more perfect union by inducing disinterested behavior from legislators. On the other, it exacerbates the counter-majoritarian difficulty inherent in all constitutional lawmaking. When one generation passes a law that affects exclusively its successors, it sidesteps the traditional forms of democratic accountability that constrain and legitimate the legislative process. The Article accordingly argues that while sunrise lawmaking holds considerable promise, it should be confined to democracy-enhancing reforms that increase future generations\u27 capacity to govern themselves. With this normative framework in place, the Article turns to the question of how time delays have actually been used in American constitutional history. It identifies six different instances of sunrise lawmaking in the U.S. Constitution. It argues that several of these illustrate how sunrise lawmaking can enhance the democratic character of American government, but at least one offers a cautionary tale of how temporal dislocation in constitutional lawmaking can have pernicious consequences