We study both theoretically and empirically option prices on firms undergoing a cash merger offer. To estimate the merger’s success probability, we use a Markov Chain Monte Carlo (MCMC) method using a state space representation of our model. Our estimated probability measure has significant predictive power for the merger outcome even after controlling for variables used in the merger literature. As

AbstractIn response to the Subprime mortgage crisis, the Basel Committee on Banking Supervision (BCBS) has spent the previous decade overhauling the regulatory framework that governs how banks calculate minimum capital requirements. In 2019, the BCBS finalized the Basel 3 regulatory regime, which changes the regulatory measure of market risk and adds new complex calculations based on liquidity and

AbstractThis article proposes a model for discrete-time currency hedging based on continuous-time movements in portfolio and foreign exchange rate returns. The vector of optimal currency exposures is given by the negative realized regression coefficients from a one-period conditional expectation of the intraperiod quadratic covariation matrix for portfolio and exchange rate returns. Empirical results

AbstractI introduce an index of market return autocorrelation based on the prices of index options and of forward-start index options and implement it at a six-month horizon. The results suggest that the autocorrelation of the S&P 500 index was close to zero before the subprime crisis but was negative in its aftermath, attaining values around –20% to –30%. I speculate that this may reflect market perceptions

We investigate price duration variance estimators that have long been neglected in the literature. In particular, we consider simple-to-construct non-parametric duration estimators, and parametric price duration estimators using autoregressive conditional duration specifications. This paper shows (i) how price duration estimators can be used for the estimation and forecasting of the integrated variance

We investigate the effects of constraining gross-exposure and shrinking covariance matrix in constructing large portfolios, both theoretically and empirically. Considering a wide variety of setups that involve conditioning or not conditioning the covariance matrix estimator on the recent past (multivariate GARCH), smaller versus larger universe of stocks, alternative portfolio formation objectives

Empirical evidence for multivariate stock suggests that there are changes from asymptotic independence to asymptotic dependence and vice versa. Under asymptotic independence, the probability of joint extremes vanishes, whereas under asymptotic dependence, this probability remains positive. In this paper, we propose a dynamic model for bivariate extremes that allows for smooth transitions between regimes

Every year, the Society for Financial Econometrics celebrates the fundamental contributions to econometric theory and practice made by our late colleague and friend Hal White. Central to these celebrations is a lecture presented by an established scholar during a plenary session of the Society’s Annual Conference. This lecture is subsequently published in the journal.

AbstractThis paper provides a guide to high-frequency option trade and quote data disseminated by the Options Price Reporting Authority (OPRA). We present a comprehensive overview of the U.S. option market, including details on market regulation and the trading processes for all 16 constituent option exchanges. We review the existing literature that utilizes high-frequency options data, summarizes

The Journal of Financial Econometrics will move to an online-only publication format starting with the first issue of 2021. We polled the journal’s advisory board and recorded unanimous support and enthusiasm for this move, which we also personally endorse. The move to an online format offers many benefits including cost-savings, a reduced environmental footprint, and extensive functionality that cannot

AbstractMotivated by the recent availability of extensive electronic news databases and advent of new empirical methods, there has been renewed interest in investigating the impact of financial news on market outcomes for individual stocks. We develop the information processing hypothesis of return volatility to investigate the relation between firm-specific news and volatility. We propose a novel

AbstractIn empirical equity asset pricing, the stochastic discount factor (SDF) is implicitly modeled as a linear function of equity factors and is influenced by the empirical properties of the factor returns. We investigate the pricing error introduced by a misspecified SDF which ignores each of the following established empirical phenomena: autocorrelation, dynamics of covariances, dynamics of correlations

AbstractIn this article, we propose a nonparametric approach to estimating generalized autoregressive conditional heteroskedasticity (1,1) models with time-varying parameters. We model the time-varying parameters as a smooth function of time and estimate them using a local linear estimator. We show that our estimator is consistent and is asymptotically normal and that the proposed estimator outperforms

In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES. We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter. These two backtests are the first backtests in the literature which solely backtest

Selecting the optimal Markowitz porfolio depends on estimating the covariance matrix of the returns of $N$ assets from $T$ periods of historical data. Problematically, $N$ is typically of the same order as $T$, which makes the sample covariance matrix estimator perform poorly, both empirically and theoretically. While various other general purpose covariance matrix estimators have been introduced in

We are extremely grateful to the discussants appointed by the co-editors of Journal of Financial Econometrics for their thorough and constructive comments on our paper. Lars P. Hansen has chosen to set the focus on the population analysis while Patrick Gagliardini and Diego Ronchetti have co-authored an impressive contribution mainly devoted to (statistical) comparison of estimators. Furthermore, Sydney

This paper combines a term structure model of credit default swaps (CDS) with weak-identification robust methods to jointly estimate the probability of default and the loss given default of the underlying firm. The model is not globally identified because it forgoes parametric time series restrictions that have aided identification in previous studies, but that are also difficult to verify in the data

Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance matrix certainly will not do. In this paper, we review our work in this area, going back 15+ years. We have

This paper introduces a volatility model with a component structure allowing for a realized measure based on high-frequency data (e.g realized variance) to drive the short-run volatility dynamics. In a joint model of the daily return and the realized measure, the conditional variance of the daily return has a multiplicative component structure: the first component traces long-run (secular) volatility

Asset pricing models are, at best, approximations of reality and are bound to be misspecified. However, it can still be useful to empirically evaluate the degree of model misspecification and the relative performance of competing asset pricing models using actual data. In their seminal paper, Hansen and Jagannathan (1997, HJ hereafter) propose two measures of model misspecification, which are now routinely

We formulate a continuous-time price discovery model in which the price discovery measure varies (stochastically) at daily frequency. We estimate daily measures of price discovery using a kernel-based OLS estimator instead of running separate daily VECM regressions as standard in the literature. Our method outperforms the standard daily VECM in finite samples. We illustrate our theoretical findings

I show that frequent batch auctions for stocks have the potential to reduce the severity of stock price crashes when they occur. For a given sequence of orders from a continuous electronic limit order book market, matching orders using one second apart batch auctions results in nearly the same trades and prices. Increasing the time interval between auctions to one minute significantly reduces the severity

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale

We introduce a set of new Value-at-Risk independence backtests by establishing a connection between the independence property of Value-at-Risk forecasts and the extremal index, a general measure of extremal clustering of stationary sequences. We introduce a sequence of relative excess returns whose extremal index has to be estimated. We compare our backtest to both popular and recent competitors using

In this paper we evaluate the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and estimation criteria . Our empirical findings highlight that: independently of the econometrician’s forecasting loss function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts

In the last few decades, a broad strand of literature in finance has implemented artificial neural networks as forecasting method. The major advantage of this approach is the possibility to approximate any linear and nonlinear behaviors without knowing the structure of the data generating process. This makes it suitable for forecasting time series which exhibit long memory and nonlinear dependencies

This paper develops a simultaneous model and moment selection procedure for factor copula models. Since the density of the factor copula is generally not known in closed form, widely used likelihood or moment based model selection criteria cannot be directly applied on factor copulas. The new approach is inspired by the methods for GMM proposed by Andrews (1999) and Andrews & Lu (2001). The consistency

In general location-scale models for asset returns, we develop central limit theory for tail risk forecasts. We do so for a wide range of risk measures, viz. distortion risk measures and expectiles. Two popular members of the class of distortion risk measures are the Value-at-Risk and the Expected Shortfall. The estimators we consider are motivated by a Pareto-type tail assumption and allow for extrapolation

Social media data presents challenges for forecasters since one must convert text into data and deal with issues related to these measures being collected at different frequencies and volumes than traditional financial data. In this paper, we use a deep learning algorithm to measure sentiment within Twitter messages on an hourly basis and introduce a new method to undertake MIDAS that allows for a

Variance estimation is central to many questions in finance and economics. Until now ex-post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency data. This paper offers a new exact finite sample approach to estimating ex-post variance using Bayesian nonparametric methods. In contrast to the classical counterpart, the proposed method exploits pooling over

The author proposes a test for the parametric specification of each component in the diffusion matrix of a d-dimensional diffusion process. Overall, d (d-1)/2 test statistics are constructed for the off-diagonal components, while d test statistics are constructed for the main diagonal components. Using theories of degenerate U-statistics, each of these test statistics is shown to follow an asymptotic

Despite their effectiveness, linear models for realized variance neglect measurement errors on integrated variance and exhibit several forms of misspecification due to the inherent nonlinear dynamics of volatility. We propose new extensions of the popular approximate long-memory heterogeneous autoregressive (HAR) model apt to disentangle these effects and quantify their separate impact on volatility