Time-Varying Coefficient Models And The Kalman Filter : Applications To Hedge Funds

There are various studies concerned with the estimation of stochastically varying coefficients for the hedge fund series but just [sic] few are available in the literature that study the model with time-varying coefficients and non-linear factor, or make a comparison of the series before and during the financial crisis. This work studies a model with linear and non-linear factors with stochastically varying coefficients to obtain better estimation of the exposure of the hedge fund and accuracy in the results. Better exposure estimates implies better hedging against negative changes in the market hence a reduction in the risk taken by the hedge fund manager. Besides, different techniques have been studied, implemented and applied in this thesis to estimate and analyze time varying exposures of different HFRX Index (an index that describes the hedge fund industry performance). The study shows that option-like models with time-varying coefficients perform the best for most of the HFRX indexes analyzed. It also shows that the Kalman Filter technique combined with the Maximum Likelihood Estimator is the best approach to estimate time-varying coefficients. In addition, we provide evidence that Kalman Filter is in a better position to capture changes in the exposure to the market conditions.

TIME-VARYING COEFFICIENT MODELS: A PROPOSAL FOR SELECTING THE COEFFICIENT DRIVER SETS

Macroeconomic Dynamics ◽

10.1017/s1365100515000279 ◽

2016 ◽

Vol 21 (5) ◽

pp. 1158-1174 ◽

Cited By ~ 6

Author(s):

Stephen G. Hall ◽

P. A. V. B. Swamy ◽

George S. Tavlas

Keyword(s):

Arbitrary Element ◽

The Other ◽

Varying Coefficient Models ◽

Time Varying ◽

Varying Coefficient ◽

Sovereign Bond ◽

Varying Coefficients ◽

Bond Spreads

Coefficient drivers are observable variables that feed into time-varying coefficients (TVCs) and explain at least part of their movement. To implement the TVC approach, the drivers are split into two subsets, one of which is correlated with the bias-free coefficient that we want to estimate and the other with the misspecification in the model. This split, however, can appear to be arbitrary. We provide a way of splitting the drivers that takes account of any nonlinearity that may be present in the data, with the aim of removing the arbitrary element in driver selection. We also provide an example of the practical use of our method by applying it to modeling the effect of ratings on sovereign-bond spreads.

Forecasting Volatility with Time-Varying Coefficient Regressions

Discrete Dynamics in Nature and Society ◽

10.1155/2020/3151473 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Qifeng Zhu ◽

Miman You ◽

Shan Wu

Keyword(s):

Constant Coefficient ◽

Stock Exchange ◽

Composite Index ◽

Predictive Ability ◽

Varying Coefficient Models ◽

Time Varying ◽

Varying Coefficient ◽

Varying Coefficients ◽

Out Of Sample ◽

We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models.

Portfolio Optimization for Hedge Funds Through Time-Varying Coefficients

SSRN Electronic Journal ◽

10.2139/ssrn.2150056 ◽

2012 ◽

Author(s):

Benoit Dewaele

Keyword(s):

Portfolio Optimization ◽

Hedge Funds ◽

Time Varying ◽

Varying Coefficients ◽

Observer Design for Non-Uniform Sampled Systems Using Gain-Scheduling

2014 Conference on Information Storage and Processing Systems ◽

10.1115/isps2014-6982 ◽

2014 ◽

Cited By ~ 1

Author(s):

Omid Bagherieh ◽

Behrooz Shahsavari ◽

Ehsan Keikha ◽

Roberto Horowitz

Keyword(s):

Kalman Filter ◽

Hard Disk Drives ◽

Gain Scheduling ◽

Observer Design ◽

Time Varying ◽

Time Intervals ◽

Varying Coefficients ◽

Regular Time ◽

Sampled Systems

In non-uniform sampled systems, the measurements are arriving at irregular time intervals. However, the control is updated at regular time intervals. An observer is required to obtain the estimate of the states during the control update times. We evaluate two observer designs: A Kalman filter and a gain-scheduling observer. The Kalman filter has the optimal performance. However, it is computationally expensive. In contrast, a recent gain-scheduling synthesis technique [1] can be used to design a time varying observer, whose time varying coefficients are a function of the measured sampling time variations. This observer is suboptimal, but it has significantly less computational complexity as compared to the Kalman filter, which makes it feasible to implement. Simulations are conducted for a self servo writing process in hard disk drives, in order to evaluate performance of H2 gain-scheduling observer design.

Pricing formula for European currency option and exchange option in a generalized jump mixed fractional Brownian motion with time-varying coefficients

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2019.01.145 ◽

2019 ◽

Vol 522 ◽

pp. 215-231 ◽

Cited By ~ 4

Author(s):

Kyong-Hui Kim ◽

Sim Yun ◽

Nam-Ung Kim ◽

Ju-Hyuang Ri

Keyword(s):

Brownian Motion ◽

Fractional Brownian Motion ◽

Time Varying ◽

European Currency ◽

Currency Option ◽

Varying Coefficients ◽

Mixed Fractional Brownian Motion ◽

Pricing Formula ◽

Exchange Option

The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽

10.3390/eng2010008 ◽

2021 ◽

Vol 2 (1) ◽

pp. 99-125

Author(s):

Edward W. Kamen

Keyword(s):

Discrete Time ◽

Initial Conditions ◽

Linear Time ◽

Order Term ◽

Initial Time ◽

Time Varying ◽

Varying Coefficients ◽

Time Signals ◽

Time Varying Systems ◽

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

Estimating latent group structure in time-varying coefficient panel data models

Econometrics Journal ◽

10.1093/ectj/utz008 ◽

2019 ◽

Author(s):

Jia Chen

Keyword(s):

Panel Data ◽

Data Models ◽

Panel Data Models ◽

Time Varying ◽

Clustering Method ◽

Varying Coefficient ◽

Varying Coefficients ◽

Latent Group ◽

Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.