Oliver Linton

University of Cambridge | Cam · Faculty of Economics

This MATLAB code accompanies the paper titled 'A Large Confirmatory Dynamic Factor Model for Stock Market Returns in Different Time Zones.' Due to data restrictions, we do not have permission to upload the corresponding data sourced from Eikon.

In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs)....

We show that three prominent consumption-based asset pricing models—the Bansal–Yaron, Campbell–Cochrane and Cecchetti–Lam–Mark models—cannot explain the dynamic properties of stock market returns. We show this by estimating these models with GMM, deriving ex-ante expected returns from them and then testing whether the difference between realised an...

We propose a confirmatory dynamic factor model for a large number of daily returns across multiple time zones. The model has a global factor and three continental factors. We propose two estimators of the model: a quasi-maximum likelihood estimator (QML-just-identified), and an improved estimator (QML-all-res). Our estimators are consistent and asy...

We propose a confirmatory dynamic factor model for a large number of daily returns across multiple time zones. The model has a global factor and three continental factors. We propose two estimators of the model: a quasi-maximum likelihood estimator (QML-just-identified), and an improved estimator (QML-all-res). Our estimators are consistent and asy...

We consider a panel data model that allows for heterogeneous time trends at different locations. The model is well suited to identifying trends in climate data recorded at multiple stations. We propose a new estimation method for the model and derive an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a boo...

We develop the Double Principal Component Analysis (DPCA) based on a dual factor structure for high-frequency intraday returns contaminated with microstructure noise. The dual factor structure allows a factor structure for microstructure noise in addition to the factor structure for efficient log-prices. We construct estimators of factors for both...

We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undire...

To investigate causal mechanisms, causal mediation analysis decomposes the total treatment effect into the natural direct and indirect effects. This paper examines the estimation of the direct and indirect effects in a general treatment effect model, where the treatment can be binary, multi-valued, continuous, or a mixture. We propose generalized w...

There is substantial uncertainty about the impact of quantitative easing (QE) on market liquidity. Identifying the impact is particularly challenging due to the potential for reverse causality, because liquidity considerations might affect QE purchases. We address this challenge by studying the Bank of England’s 2016-17 Corporate Bond Purchase Sche...

This paper studies a heterogeneous coefficient spatial factor model that separately addresses both common factor risks (strong cross-sectional dependence) and local dependency (weak cross-sectional dependence) in equity returns. From the asset pricing perspective, we derive the theoretical implications of no asymptotic arbitrage for the heterogeneo...

This paper proposes an adjusted-range based self-normalization (SN) method to construct confidence intervals for censored dependent data, which helps to circumvent the long-run variance estimation and tuning parameter selection problems. Simulation studies confirm the validity of this new approach.

Interactive fixed effects are a popular means to model unobserved heterogeneity in panel data. Models with interactive fixed effects are well studied in the low-dimensional case where the number of parameters to be estimated is small. However, they are largely unexplored in the high-dimensional case where the number of parameters is large, potentia...

We propose tests of the conditional first- and second-order stochastic dominance in the presence of growing numbers of covariates. Our approach builds on a semiparametric location-scale model, where the conditional distribution of the outcome given the covariates is characterized by nonparametric mean and skedastic functions with independent innova...

We propose nonparametric tests for the null hypothesis of time stochastic dominance. Time stochastic dominance makes a partial order of different prospects over time based on the net present value criteria for general utility and time discount function classes. For example, time stochastic dominance can be used for ranking investment strategies or...

In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the...

Most stock markets are open for 6-8 hours per trading day. The Asian, European and North American stock markets are separated in time by time-zone differences. We propose a statistical factor model for daily returns across multiple time zones. Our model has a common global factor as well as a continent factor. We demonstrate that our model has a st...

This article proposes a score test statistic for whether there is a stochastic trend in conditional variances of a GARCH process. We derive its null limiting distribution and demonstrate its properties through simulation and empirical studies.

We study regression adjustments with additional covariates in randomized experiments under covariate-adaptive randomizations (CARs) when subject compliance is imperfect. We develop a regression-adjusted local average treatment effect (LATE) estimator that is proven to improve efficiency in the estimation of LATEs under CARs. Our adjustments can be...

We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary moments of the microstructure noise process based on high‐frequency data, where the noise pr...

We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator based on conditional moment restrictions and an efficient...

We propose an asset pricing factor model constructed with semiparametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothes...

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard error...

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard error...

We propose a general framework for the specification testing of continuous treatment effect models. We assume a general residual function, which includes the average and quantile treatment effect models as special cases. The null models are identified under the unconfoundedness condition and contain a nonparametric weighting function. We propose a...

We provide an estimator of the lower regression function and provide large sample properties for inference. We also propose a test of the hypothesis of positive expectation dependence and derive its limiting distribution under the null hypothesis and provide consistent critical values. We apply our methodology to the question of portfolio choice an...

We consider nonlinear moment restriction semiparametric models where both the dimension of the parameter vector and the number of restrictions are divergent with sample size and an unknown smooth function is involved. We propose an estimation method based on the sieve generalized method of moments (sieve-GMM). We establish consistency and asymptoti...

This paper presents a weighted optimization framework that unifies the binary, multivalued, and continuous treatment—as well as mixture of discrete and continuous treatment—under a unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile, and asymmetric least squares causal effect of treatment as...

Donald and Hsu (2014) studied the estimation and inference for the counterfactual distribution and quantile functions in a binary treatment model. We extend their work to the continuous treatment model. Specifically, we propose a weighted regression estimator for the counterfactual distribution but we estimate the weighting function from a covariat...

We propose a general framework for the specification testing of continuous treatment effect models. We assume a general residual function, which includes the average and quantile treatment effect models as special cases. The null models are identified under the confoundedness condition and contain a nonparametric weighting function. We propose a te...

Multilateral comparison of outcomes drawn from multiple groups pervade the social sciences and measurement of their variability, usually involving functions of respective group location and scale parameters, is of intrinsic interest. However, such approaches frequently mask more fundamental differences that more comprehensive examination of relativ...

We propose a new estimator, the quadratic form estimator, of the Kronecker product model for covariance matrices. We show that this estimator has good properties in the large dimensional case (i.e., the cross-sectional dimension n is large relative to the sample size T ). In particular, the quadratic form estimator is consistent in a relative Frobe...

We carry out some analysis of the daily data on the number of new cases and the number of new deaths by (191) countries as reported to the European Centre for Disease Prevention and Control (ECDC). Our benchmark model is a quadratic time trend model applied to the log of new cases for each country. We use our model to predict when the peak of the e...

We study a class of nonparametric regression models that includes deterministic time trends and both stationary and nonstationary stochastic processes (whose shocks are allowed to be mutually correlated). We propose a unified approach to estimation based on the weighted sieve method to tackle the issue of unbounded support of the covariates. This a...

We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann, and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference...

In this paper, we study the trending behaviour of COVID-19 data at country level, and draw attention to some existing econometric tools which are potentially helpful to understand the trend better in future studies. In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, whi...

This paper proposes five pointwise consistent and asymptotic normal estimators of the asymptotic variance function of the Nadaraya-Watson kernel estimator for nonparametric regression. The proposed estimators are constructed based on the first-stage nonparametric residuals, and their asymptotic properties are established under the assumption that t...

In a wide range of modern applications, one observes a large number of time series rather than only a single one. It is often natural to suppose that there is some group structure in the observed time series. When each time series is modeled by a nonparametric regression equation, one may in particular assume that the observed time series can be pa...

We develop a novel estimation methodology for an additive nonparametric panel model that is suitable for capturing the pricing of coupon-paying government bonds followed over many time periods. We use our model to estimate the discount function and yield curve of nominally riskless government bonds. The novelty of our approach is the combination of...

This paper studies nonparametric estimation of the infinite order regression E(Ytk|Ft−1), k∈Z with stationary and weakly dependent data. We propose a Nadaraya–Watson type estimator that operates with an infinite number of conditioning variables. We propose a bandwidth sequence that shrinks the effects of long lags, so the influence of all condition...

We propose a semi-parametric coupled component exponential GARCH model for intraday and overnight returns that allows the two series to have different dynamical properties. We adopt a dynamic conditional score model with t-distributed innovations that captures the very heavy tails of overnight returns. We propose a several-step estimation procedure...

We propose a Kronecker product model for correlation or covariance matrices in the large dimensional case. The number of parameters of the model increases logarithmically with the dimension of the matrix. We propose a minimum distance (MD) estimator based on a log-linear property of the model, as well as a one-step estimator, which is a one-step ap...

This paper uses transaction data to estimate how single stock circuit breakers on the London Stock Exchange affect other stocks that remain in continuous trading. This “spillover” effect is estimated by calculating the effect of a trading halt on the market quality of stocks that remain in continuous trading and comparing this with the effect of a...

We develop the limit theory of the quantilogram and cross-quantilogram under long memory. We establish the sub-root-n central limit theorems for quantilograms that depend on nuisance parameters. We propose a moving block bootstrap (MBB) procedure for inference and establish its consistency, thereby enabling a consistent confidence interval construc...

We study the efficient estimation of nonparametric regression in the presence of heteroskedasticity. We focus our analysis on local polynomial estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heterosked...

We propose two new estimators of the Kronecker product model of the covariance matrix. We show that these estimators have good properties in the large dimensional case where $n$ is large relative to $T.$ In particular, the partial means estimator is consistent in a relative Frobenius norm sense provided $\log^{3}n/T\rightarrow0,$ while the quadrati...

We consider a model with both a parametric global trend and a nonparametric local trend. This model may be of interest in a number of applications in economics, finance, ecology, and geology. We first propose two hypothesis tests to detect whether two nested special cases are appropriate. For the case where both null hypotheses are rejected, we pro...

This paper studies the estimation of large dynamic covariance matrices with multiple conditioning variables. We introduce an easy-to-implement semiparametric method to estimate each entry of the covariance matrix via model averaging marginal regression, and then apply a shrinkage technique to obtain the dynamic covariance matrix estimation. Under s...

In a wide range of modern applications, we observe a large number of time series rather than only a single one. It is often natural to suppose that there is some group structure in the observed time series. When each time series is modelled by a nonparametric regression equation, one may in particular assume that the observed time series can be par...

Cambridge Core - Finance and Accountancy - Financial Econometrics - by Oliver Linton

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final...

Supplementary Material for "Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case"

We propose new methods for estimating the bid–ask spread from observed transaction prices alone. Our methods are based on the empirical characteristic function. We compare our methods theoretically and numerically with the Roll (1984) method as well as with its best known competitor, the Hasbrouck (2004) method, and find that our estimators perform...

This paper presents a weighted optimization framework that unifies the binary,multi-valued, continuous, as well as mixture of discrete and continuous treatment, under the unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile and asymmetric least squares causal effect of treatment as special ca...