scholarly journals Testing for the Diffusion Matrix in a Continuous-Time Markov Process Model with Applications to the Term Structure of Interest Rates*

2019 ◽  
Author(s):  
Fuchun Li
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Process Model ◽  
Standard Normal Distribution ◽  
Test Statistics ◽  
Diffusion Matrix ◽  
Affine Term Structure Models ◽  
U Statistics ◽  
Markov Process Model ◽  
Standard Normal

Abstract For each component in the diffusion matrix of a d-dimensional diffusion process, we propose a test for the parametric specification of this component. Overall, d(d−1)/2 test statistics are constructed for the off-diagonal components, while d test statistics being for the main diagonal components. Using theories of degenerate U-statistics, each of all these test statistics is shown to follow an asymptotic standard normal distribution under null hypothesis, while diverging to infinity if the component is misspecified over a significant range. We obtain new empirical findings by applying our tests to evaluate a variety of three-factor affine term structure models in modeling the volatility dynamics of monthly U.S. Treasury yields.

scholarly journals Term Premia and Inflation Uncertainty: Empirical Evidence from an International Panel Dataset: Comment

2014 ◽  
Vol 104 (1) ◽  
pp. 323-337 ◽  
Author(s):  
Michael D. Bauer ◽  
Glenn D. Rudebusch ◽  
Jing Cynthia Wu
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Small Sample ◽  
Maximum Likelihood Estimates ◽  
Affine Term Structure ◽  
Term Premium ◽  
Affine Term Structure Models ◽  
International Panel ◽  
Term Premia ◽  
Macroeconomic Determinants

Term premia implied by maximum likelihood estimates of affine term structure models are misleading because of small-sample bias. We show that accounting for this bias alters the conclusions about the trend, cycle, and macroeconomic determinants of the term premia estimated in Wright (2011). His term premium estimates are essentially acyclical, and often just parallel the secular trend in longterm interest rates. In contrast, bias-corrected term premia show pronounced countercyclical behavior, consistent with theoretical and empirical arguments about movements in risk premia. (JEL E31, E43, E52, G12, H63)

scholarly journals On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality

2012 ◽  
Vol 49 (2) ◽  
pp. 159-175 ◽  
Author(s):  
Zofia Hanusz ◽  
Joanna Tarasińska ◽  
Zbigniew Osypiuk
Keyword(s):  
Small Sample ◽  
Multivariate Normality ◽  
Small Samples ◽  
Standard Normal Distribution ◽  
Test Statistics ◽  
Significance Level ◽  
Nominal Significance ◽  
Standard Normal ◽  
Small Sample Properties ◽  
Mean And Variance

Summary The kurtosis-based tests of Mardia and Srivastava for assessing multivariate normality (MVN) are considered. The asymptotic standard normal distribution of their test statistics, under normality, is often misused for too small samples. The purpose of this paper is to suggest mean-and-variance corrected versions of the Mardia and Srivastava test statistics. Simulation studies evaluating both the true sizes and the powers of original and corrected tests against selected alternatives are presented and compared to the size and the power of the Henze-Zirkler test. The proposed corrected statistics have empirical sizes closer to a nominal significance level than the original ones. It is also shown that the corrected versions of the tests can be more powerful than the original ones.

A NEW FINITE ELEMENT METHOD FOR PRICING OF BOND OPTIONS UNDER TIME INHOMOGENEOUS AFFINE TERM STRUCTURE MODELS OF INTEREST RATES

2007 ◽  
Vol 10 (01) ◽  
pp. 31-49
Author(s):  
HONGTAO YANG
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Interest Rates ◽  
Term Structure ◽  
Cir Model ◽  
Affine Term Structure ◽  
Affine Term Structure Models ◽  
Bond Options ◽  
The Stability ◽  
Element Method

In this paper we propose a new finite element method for pricing of bond options under time inhomogeneous one-factor affine models of short interest rates: the Hull–White model and the extended CIR model. The stability and weak convergence are established. Numerical results are presented to examine the method and to compare the calibrated models.

AFFINE PROCESSES, ARBITRAGE-FREE TERM STRUCTURES OF LEGENDRE POLYNOMIALS, AND OPTION PRICING

2005 ◽  
Vol 08 (02) ◽  
pp. 161-184 ◽  
Author(s):  
CAIO IBSEN RODRIGUES DE ALMEIDA
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
State Vector ◽  
Legendre Polynomials ◽  
Factor Model ◽  
Random Coefficients ◽  
Characteristic Functions ◽  
Affine Function ◽  
Fixed Income ◽  
Affine Term Structure Models

Multivariate Affine term structure models have been increasingly used for pricing derivatives in fixed income markets. In these models, uncertainty of the term structure is driven by a state vector, while the short rate is an affine function of this vector. The model is characterized by a specific form for the stochastic differential equation (SDE) for the evolution of the state vector. This SDE presents restrictions on its drift term which rule out arbitrages in the market. In this paper we solve the following inverse problem: Suppose the term structure of interest rates is modelled by a linear combination of Legendre polynomials with random coefficients. Is there any SDE for these coefficients which rules out arbitrages? This problem is of particular empirical interest because the Legendre model is an example of factor model with clear interpretation for each factor, in which regards movements of the term structure. Moreover, the Affine structure of the Legendre model implies knowledge of its conditional characteristic function. From the econometric perspective, we propose arbitrage-free Legendre models to describe the evolution of the term structure. From the pricing perspective, we follow Duffie et al. [22] in exploring their conditional characteristic functions to obtain a computational tractable method to price fixed income derivatives.

Markov Process Model Forecasting of Quarterly Accounting Earnings Per Share

2011 ◽  
Vol 3 (6) ◽  
pp. 99-103
Author(s):  
M. P. Rajakumar M. P. Rajakumar ◽  
◽  
Dr. V. Shanthi Dr. V. Shanthi
Keyword(s):  
Markov Process ◽  
Process Model ◽  
Accounting Earnings ◽  
Earnings Per Share ◽  
Markov Process Model

Sebaran Peluang Acak Kontinu, Distribusi Normal, Distribusi Normal Baku, Distribusi T, Distribusi Chi Square, dan Distribusi F

2020 ◽  
Author(s):  
Ahmad Sudi Pratikno
Keyword(s):  
Hypothesis Test ◽  
Standard Normal Distribution ◽  
Process Data ◽  
Nominal Data ◽  
Chi Square ◽  
Stable Curve ◽  
Standard Normal ◽  
Research Findings ◽  
F Distribution ◽  
T Distribution

In statistics, there are various terms that may feel unfamiliar to researcher who is not accustomed to discussing it. However, despite all of many functions and benefits that we can get as researchers to process data, it will later be interpreted into a conclusion. And then researcher can digest and understand the research findings. The distribution of continuous random opportunities illustrates obtaining opportunities with some detection of time, weather, and other data obtained from the field. The standard normal distribution represents a stable curve with zero mean and standard deviation 1, while the t distribution is used as a statistical test in the hypothesis test. Chi square deals with the comparative test on two variables with a nominal data scale, while the f distribution is often used in the ANOVA test and regression analysis.

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