Volatility

Barndorff–Nielsen, Ole E.; Shephard, Neil

doi:10.1002/9780470061602.eqf19019

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…This clustering was also documented by Granger and Ding (1995) and Ding and Granger (1996). Ding et al (1993) and Barndorff-Nielsen and Shephard (2010) further found long-range dependence in volatility time series. Thus, the evidence in the literature suggests that the factor models do not necessarily perform similarly well within different sample periods.…”

Section: Introductionsupporting

confidence: 67%

Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China

Shu

Wang

Yuan

2021

Emerging Markets Finance and Trade

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This paper examines the performance of three nested factor pricing models for different market trends, such as bull, bear or consolidation, in China's stock market. Specifically, the models we consider are the Capital Asset Pricing Model (CAPM), the Fama-French threefactor model and the Fama-French extended five-factor model. Empirical results show that these models can explain the time-series variations in excess returns on a range of portfolios in the bear markets reasonably well, but have more difficulty explaining the cross-sectional variations in returns across the portfolios. We further adopt instability tests based on Hansen's statistics and recursive regressions, from which we derive the following two findings. First, we find the models to be more unstable in the bear and bull markets (trending markets) under time-series regression. Although the models in the time-series regressions usually perform well, this is due to the higher stock price synchronicity in trending markets, especially when it is bearish. Second, because the cross-sectional analysis of the Fama-MacBeth approach requires factor loadings to be estimated first by time-series regressions, the models inherit greater instability from trending markets. This causes the unitary parameter estimates to be less reliable, which in turn brings about difficulties in explaining the cross-sectional portfolio returns in trending markets.

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Section: Introductionsupporting

confidence: 67%

Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China

Shu

Wang

Yuan

2021

Emerging Markets Finance and Trade

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“…Recall from e.g. Barndorff-Nielsen & Shephard (2010), Pedersen (2003), Rajput & Rosinski (1989) that a Lévy basis 3 AMBIT FIELDS AND PROCESSES L = {L(B), B ∈ S} defined on a probability space (Ω, F, P ) is an independently scattered random measure with Lévy-Khinchin representation…”

Section: Background On Lévy Basesmentioning

confidence: 99%

Modelling Electricity Forward Markets by Ambit Fields

2011

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This paper proposes a new modelling framework for electricity forward markets, which is based on ambit fields. The new model can capture many of the stylised facts observed in energy markets. One of the main differences to the traditional models lies in the fact that we do not model the dynamics, but the forward price directly, where we focus on models which are stationary in time. We give a detailed account on the probabilistic properties of the new model and we discuss martingale conditions and change of measure within the new model class. Also, we derive a model for the spot price which is obtained from the forward model through a limiting argument.

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“…Stochastic volatility (or intermittency) has been studied extensively in the last decade both in the finance literature as well as in physics in the context of modelling turbulence, see e.g. Barndorff-Nielsen & Shephard (2013) for a textbook treatment in the finance context and see Barndorff-Nielsen, Benth & Veraart (2012) for relevant references in the turbulence context. In this paper, we discuss various new methods for accounting for stochastic volatility clusters in the context of integer-valued stochastic processes.…”

Section: Introductionmentioning

confidence: 99%

Integer-Valued Trawl Processes: A Class of Stationary Infinitely Divisible Processes

et al. 2013

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This paper introduces a new continuous-time framework for modelling serially correlated count and integer-valued data. The key component in our new model is the class of integer-valued trawl (IVT) processes, which are serially correlated, stationary, infinitely divisible processes. We analyse the probabilistic properties of such processes in detail and, in addition, study volatility modulation and multivariate extensions within the new modelling framework. Moreover, we describe how the parameters of a trawl process can be estimated and obtain promising estimation results in our simulation study. Finally, we apply our new modelling framework to high frequency financial data.

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Volatility

Cited by 13 publications

References 29 publications

Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China

Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China

Modelling Electricity Forward Markets by Ambit Fields

Integer-Valued Trawl Processes: A Class of Stationary Infinitely Divisible Processes

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