Bond pricing with a surface of zero coupon yields

We present a new method for consistent cross‐sectional pricing of all traded bonds in the fixed income market. By applying thin plate regression splines ( Wood, 2003 ) to bootstrapped zero coupon bond yields ( Hagan and West, 2006 ), the method decomposes traded yields into a risk‐free component plus premia for credit and liquidity risks, where the decomposition is consistent with the market valuations and underlying cash flows of the bonds. We apply the framework to end of quarter yield data from 2008 to 2011 on Australian dollar denominated semi‐government, supranational and agency (SSA) bonds, and find that the surface provides an excellent fit to the underlying zero coupon yield curves. Further, the decomposition of selected yield time series and cross‐sections demonstrates how credit premia increased for Australian SSA bonds through the Global Financial Crisis (GFC), but were counterbalanced by liquidity discounts as investors sought safe haven securities.     

Stock and Bond Liquidity and its Effect on Prices and Financial Policies

An asset is liquid if it can be traded at the prevailing market price quickly and at low cost. We show that in addition to risk, liquidity affects asset prices and returns. Theories of asset pricing suggest that the expected return of an asset is increasing in its risk, because risk-averse investors require compensation for bearing more risk. Because investors are also averse to the costs of illiquidity and want to be compensated for bearing them, asset returns are increasing in illiquidity. Thus, asset prices should depend on two asset characteristics: risk and liquidity. This paper surveys research on the effects of liquidity on asset prices and returns, showing that liquidity is an important factor in capital asset pricing.     

Time Inhomogeneous Multiple Volatility Modeling

Price variations at speculative markets exhibit positive autocorrelationand cross correlation. Due to large parameter spaces necessaryfor joint modeling of variances and covariances, multivariateparametric volatility models become easily intractable in practice.We propose an adaptive procedure that identifies periods ofsecond-order homogeneity for each moment in time. To overcomethe high dimensionality of the problem we transform the multivariateseries into a set of univariate processes. We discuss thoroughlythe implementation of the adaptive technique. Theoretical andMonte Carlo results are given. We provide two applications ofthe new method. For a bivariate exchange rate series we comparethe multivariate GARCH approach with our method and find thelatter to be more in line with the underlying assumption ofindependently distributed innovations. Analyzing a 23-dimensionalvector of asset returns we underscore the case for adaptivemodeling in high-dimensional systems.     

Endogenous Parameter Time Series Estimation of the Ohlson Model: Linear and Nonlinear Analyses

This paper tests the empirical validity of the Ohlson (1995) model on a firm-level time series basis. The coefficients of the earnings dynamic and valuation equations are first estimated by OLS. Next, recognizing the nonlinear relationships among the parameters, each equation is estimated by nonlinear Least Squares. Lastly, the model is estimated as a restricted system by nonlinear Least Squares and nonlinear SUR. In all cases, parameters are endogenously estimated. Irrespective of the estimation method, the Ohlson model often yields inconsistent or insignificant parameter estimates. Nevertheless, point estimates of equity risk premia are similar to those obtained from alternative methodologies.     

Liquidity needs and vulnerability to financial underdevelopment

This paper provides evidence that financial development has a large causal effect in the reduction of macroeconomic volatility resulting from the role of the financial system in liquidity provision. In particular, financial system development leads to a comparatively larger reduction in the volatility of output in sectors with high liquidity needs. Most of this decline results from the stabilization of the output of existing firms, although the volatility of the number of firms also drops significantly. Among different aspects of the financial system, the depth of financial intermediaries plays the main role in the reduction of volatility.     

Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions

Parameter estimation and statistical inference are challenging problems for stochastic volatility (SV) models, especially those driven by pure jump Lévy processes. Maximum likelihood estimation (MLE) is usually preferred when a parametric statistical model is correctly specified, but traditional MLE implementation for SV models is computationally infeasible due to high dimensionality of the integral involved. To overcome this difficulty, we propose a gradient-based simulated MLE method under the hidden Markov structure for SV models, which covers those driven by pure jump Lévy processes. Gradient estimation using characteristic functions and sequential Monte Carlo in the simulation of the hidden states are implemented. Numerical experiments illustrate the efficiency of the proposed method.     

Empirical Comparisons in Short-Term Interest Rate Models Using Nonparametric Methods

This study applies the nonparametric estimation procedure tothe diffusion process modeling the dynamics of short-term interestrates. This approach allows us to operate in continuous time,estimating the continuous-time model, despite the use of discretedata. Three methods are proposed. We apply these methods totwo important financial data. After selecting an appropriatebandwidth for each dataset, empirical comparisons indicate thatthe specification of the drift has a considerable impact onthe pricing of derivatives through its effect on the diffusionfunction. In addition, a novel nonparametric test has been proposedfor specification of linearity in the drift. Our simulationdirects us to reject the null hypothesis of linearity at the5% significance level for the two financial datasets.     

经典波动模型下的最优债券投资组合

文章认为,由于利率和股价兼有相同的趋势性和波动性属性,股价经典波动模型对利率建模具有研究价值。通过引入经典的波动模型,结合极大似然估计的方法,本文探讨了无风险债券的最优投资方案,并将该成果运用于全球主要国债市场进行实证模拟投资,结果表明,该模型在全球主要国债市场均能取得较好的超额收益。     

Calibrating the exponential Ornstein–Uhlenbeck multiscale stochastic volatility model

This paper demonstrates a tractable and efficient way of calibrating a multiscale exponential Ornstein–Uhlenbeck stochastic volatility model including a correlation between the asset return and its volatility. As opposed to many contributions where this correlation is assumed to be null, this framework allows one to describe the leverage effect widely observed in equity markets. The resulting model is non-exponential and driven by a degenerate noise, thus requiring a high level of care in designing the estimation algorithm. The way this difficulty is overcome provides guidelines concerning the development of an estimation algorithm in a non-standard framework. The authors propose using a block-type expectation maximization algorithm along with particle smoothing. This method results in an accurate calibration process able to identify up to three timescale factors. Furthermore, we introduce an intuitive heuristic which can be used to choose the number of factors.