A jackknife-type estimator for portfolio revision

This article proposes a novel approach to portfolio revision. The current literature on portfolio optimization uses a somewhat naïve approach, where portfolio weights are always completely revised after a predefined fixed period. However, one shortcoming of this procedure is that it ignores parameter uncertainty in the estimated portfolio weights, as well as the biasedness of the in-sample portfolio mean and variance as estimates of the expected portfolio return and out-of-sample variance. To rectify this problem, we propose a jackknife procedure to determine the optimal revision intensity, i.e. the percent of wealth that should be shifted to the new, in-sample optimal portfolio. We find that our approach leads to highly stable portfolio allocations over time, and can significantly reduce the turnover of several well established portfolio strategies. Moreover, the observed turnover reductions lead to statistically and economically significant performance gains in the presence of transaction costs.     

A simple structural estimator of disclosure costs

Review of Accounting Studies - This study recovers a simple firm-level measure of disclosure costs implied by the voluntary disclosure theory of Verrecchia (Journal of Accounting and Economics...     

A noise-robust estimator of volatility based on interquantile ranges

This paper proposes a new class of estimators based on the interquantile range of intraday returns, referred to as interquantile range based volatility (IQRBV), to estimate the integrated daily volatility. More importantly and intuitively, it is shown that a properly chosen IQRBV is jump-free for its trimming of the intraday extreme two tails that utilize the range between symmetric quantiles. We exploit its approximation optimality by examining a general class of distributions from the Pearson type IV family and recommend using IQRBV^.04 as the integrated variance estimate. Both our simulation and the empirical results highlight interesting features of the easy-to-implement and model-free IQRBV over the other competing estimators that are seen in the literature.     

A general closed-form spread option pricing formula

We propose a new accurate method for pricing European spread options by extending the lower bound approximation of Bjerksund and Stensland (2011) beyond the classical Black–Scholes framework. This is possible via a procedure requiring a univariate Fourier inversion. In addition, we are also able to obtain a new tight upper bound. Our method provides also an exact closed form solution via Fourier inversion of the exchange option price, generalizing the Margrabe (1978) formula. The method is applicable to models in which the joint characteristic function of the underlying assets forming the spread is known analytically. We test the performance of these new pricing algorithms performing numerical experiments on different stochastic dynamic models.     

关于国际市场重要利差指标的研究

文章对国际市场最具代表性的两大利差指标——货币市场与债券市场利差指标TED利差,和货币市场与利率衍生品市场利差指标Libor—OiS利差进行了研究,TED利差是反映整体经济信用风险状况的指标,Libor-OiS利差则可衡量银行体系流动性风险和信用风险,文章考察了两个指标的市场含义、历史走势和理论研究,并对其在金融危机期间的表现进行分析和解释。     

A parametric alternative to the Hill estimator for heavy-tailed distributions

Despite its wide use, the Hill estimator and its plot remain to be difficult to use in Extreme Value Theory (EVT) due to substantial sampling variations in extreme sample quantiles. In this paper, we propose a new plot we call the eigenvalue plot which can be seen as a generalization of the Hill plot. The theory behind the plot is based on a heavy-tailed parametric distribution class called the scaled Log phase-type (LogPH) distributions, a generalization of the ordinary LogPH distribution class which was previously used to model insurance claims data. We show that its tail property and moment condition are well aligned with EVT. Based on our findings, we construct the eigenvalue plot from fitting a shifted PH distribution to the excess log data with a minimal phase size. Through various numerical examples we illustrate and compare our method against the Hill plot.     

Disentangling the effect of jumps on systematic risk using a new estimator of integrated co-volatility

We propose a new threshold–pre-averaging realized estimator for the integrated co-volatility of two assets using non-synchronous observations with the simultaneous presence of microstructure noise and jumps. We derive a noise-robust Hayashi–Yoshida estimator that allows for very general structure of jumps in the underlying process. Based on the new estimator, different aspects and components of co-volatility are compared to examine the effect of jumps on systematic risk using tick-by-tick data from the Chinese stock market during 2009–2011. We find controlling for jumps contributes significantly to the beta estimation and common jumps mostly dominate the jump’s effect, but there is also evidence that idiosyncratic jumps may lead to significant deviation. We also find that not controlling for noise and jumps in previous realized beta estimations tend to considerably underestimate the systematic risk.     

Bias of a Value-at-Risk estimator

We develop the analytical second-order bias of a Value-at-Risk estimator based on an ARCH(1) volatility specification when the parameters are estimated by the method of quasi maximum likelihood. We show that the bias results from two sources: assumption on the distribution of the standardized residuals and the parameter estimation error.     

A simulation estimator for testing the time homogeneity of credit rating transitions

The measurement of credit quality is at the heart of the models designed to assess the reserves and capital needed to support the risks of both individual credits and portfolios of credit instruments. A popular specification for credit-rating transitions is the simple, time-homogeneous Markov model. While the Markov specification cannot really describe processes in the long run, it may be useful for adequately describing short-run changes in portfolio risk. In this specification, the entire stochastic process can be characterized in terms of estimated transition probabilities. However, the simple homogeneous Markovian transition framework is restrictive. We propose a test of the null hypotheses of time-homogeneity that can be performed on the sorts of data often reported. We apply the tests to 4 data sets, on commercial paper, sovereign debt, municipal bonds and S&P-rated Corporates. The results indicate that commercial paper looks Markovian on a 30-day time scale for up to 6 months; sovereign debt also looks Markovian (perhaps due to a small sample size); municipals are well-modeled by the Markov specification for up to 5 years, but could probably benefit from frequent updating of the estimated transition matrix or from more sophisticated modeling, and S&P Corporate ratings are approximately Markov over 3 transitions but not 4.     

Estimation of the bid - ask spread and its components: a new approach

We show that time variation in expected returns and/or partialprice adjustments lead to a downward bias in previous estimatorsof both the spread and its components. We introduce a new approachthat provides unbiased and efficient estimators of the componentsof the spread. We find that between 77 and 97 percent of thedownward bias in previous spread estimate is caused by timevariation in expected returns. More importantly, the adverse-selectioncomponent, though significant, accounts for a much smaller proportion(8 to 13 percent) of the quoted spread, at least for small trades,than the proportion (over 40 percent) previously reported inthe literature. Order processing costs are the predominant componentof quoted spreads.     

The heteroskedasticity-consistent covariance estimator in accounting

The main purpose of this paper is to compare the White (1980) heteroskedasticity-consistent (HC) covariance matrix estimator with alternative estimators. Many regression packages compute the White (1980) heteroskedasticity-consistent (HC) covariance matrix estimator. The common procedure in Accounting and Finance research to deal with the heteroskedasticity problem is based on this estimator, despite its worse finite-samples properties when compared with other consistent estimators. In this paper we compare several HC covariance matrix estimators based on a sample of 3706 European listed companies from Austria, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden and the United Kingdom. We conclude that HC standard errors increase when finite-samples more appropriate estimators are considered and in the most part of countries the Ohlson (1995) model coefficients estimates became statistically insignificant. This can be explained by the high leverage points in the design matrix. To the best of our knowledge it is the first time that these alternative estimators are compared with the one of White (1980) in accounting research.     

A computationally fast estimator for random coefficients logit demand models using aggregate data

This article proposes a computationally fast estimator for random coefficients logit demand models using aggregate data that Berry, Levinsohn, and Pakes ( 1995 ; hereinafter, BLP) suggest. Our method, which we call approximate BLP (ABLP), is based on a linear approximation of market share functions. The computational advantages of ABLP include (i) the linear approximation enables us to adopt an analytic inversion of the market share equations instead of a numerical inversion that BLP propose, (ii) ABLP solves the market share equations only at the optimum, and (iii) it minimizes over a typically small dimensional parameter space. We show that the ABLP estimator is equivalent to the BLP estimator in large data sets. Our Monte Carlo experiments illustrate that ABLP is faster than other approaches, especially for large data sets.     

New low-frequency spread measures

I develop new spread proxies that pick up on three attributes of the low-frequency (daily) data: (1) price clustering, (2) serial price covariance accounting for midpoint prices on no-trade days, and (3) the quoted spread that is available on no-trade days. I develop and empirically test two different approaches: an integrated model and combined models. I test both new and existing low-frequency spread measures relative to two high-frequency benchmarks (percent effective spread and percent quoted spread) on three performance dimensions: (1) higher individual firm correlation with the benchmarks, (2) higher portfolio correlation with the benchmarks, and (3) lower distance relative to the benchmarks. I find that on all three performance dimensions the new integrated model and the new combined model do significantly better than existing low-frequency spread proxies.     

A study on foreign exchange dealers' bid–ask spread quote behavior

Based on a questionnaire surveying dealers in the Taipei inter-bank foreign exchange market that was conducted in March 2001, I attempted to answer the question of who initiated the wider currency spread. It was found that the risk-averse dealers of small banks quoted wider spreads in order to conceal their inferior positions regarding information and inventory or to avoid market volatility risk. Some of the dealers of large multinational banks in major financial centers who normally quote conventional spreads were found to quote wider spreads in response to the request for quotations by small Taiwanese bank dealers who widened their spread quotes.     

The micro-price: a high-frequency estimator of future prices

The micro-price estimated using high-frequency data is empirically a better predictor of short-term prices than the mid-price or the weighted mid-price     

A two-factor cointegrated commodity price model with an application to spread option pricing

In this paper, we propose an easy-to-use yet comprehensive model for a system of cointegrated commodity prices. While retaining the exponential affine structure of previous approaches, our model allows for an arbitrary number of cointegration relationships. We show that the cointegration component allows capturing well-known features of commodity prices, i.e., upward sloping (contango) and downward sloping (backwardation) term-structures, smaller volatilities for longer maturities and an upward sloping correlation term structure. The model is calibrated to futures price data of ten commodities. The results provide compelling evidence of cointegration in the data. Implications for the prices of futures and options written on common commodity spreads (e.g., spark spread and crack spread) are thoroughly investigated.     

A yield spread perspective on the great financial crisis: Break-point test evidence

We use a simple partial adjustment econometric framework to investigate the effects of financial crises on the dynamic properties of yield spreads. We find that crises manifest themselves in the form of substantial disruptions revealed by changes in the persistence of the shocks to spreads as much as by in their unconditional mean levels. Formal breakpoint tests confirm that in the U.S. the Great Financial Crisis has been over approximately since the Spring of 2009 and provide a conservative dating centered around the August 2007–June 2009 dates. However, some yield spread series point to an end of the most serious disruptions as early as in December 2008. Some symptoms of an impending crisis re-appear instead in the second half of 2011. We also uncover evidence that the LSAP program implemented by the Fed in the U.S. residential mortgage market has been effective, in the sense that the risk premia in this market have been uniquely shielded from the disruptive effects of the crisis.     

Asset pricing and the bid-ask spread

This paper studies the effect of the bid-ask spread on asset pricing. We analyze a model in which investors with different expected holding periods trade assets with different relative spreads. The resulting testable hypothesis is that market-observed expexted return is an increasing and concave function of the spread. We test this hypothesis, and the empirical results are consistent with the predictions of the model.     

Bivariate normal mixture spread option valuation

We discuss the pricing and hedging of European spread options on correlated assets when the marginal distribution of each asset return is assumed to be a mixture of normal distributions. Being a straightforward two-dimensional generalization of a normal mixture diffusion model, the prices and hedge ratios have a firm behavioural and theoretical foundation. In this ‘bivariate normal mixture’ (BNM) model no-arbitrage option values are just weighted sums of different ‘2GBM’ option values that are based on the assumption of two correlated lognormal diffusions, and likewise for their sensitivities. The main advantage of this approach is that BNM option values are consistent with both volatility smiles and with the implied correlation ‘frown’. No other ‘frown consistent’ spread option valuation model has such straightforward implementation. We apply analytic approximations to compare BNM valuations of European spread options with those based on the 2GBM assumption and explain the differences between the two as a weighted sum of six second-order 2GBM sensitivities. We also examine BNM option sensitivities, finding that these, like the option values, can sometimes differ substantially from those obtained under the 2GBM model. Finally, we show how the correlation frown that is implied by the BNM model is affected as we change (a) the correlation structure and (b) the tail probabilities in the joint density of the asset returns.