This book is an ambitious effort by three well-known and
 well-respected scholars to fill an acknowledged void in the
 literature—a text covering the burgeoning field of empirical finance.
 As the authors note in the preface, there are several excellent books
 covering financial theory at a level suitable for a Ph.D. class or as
 a reference for academics and practitioners, but there is little or
 nothing similar that covers econometric methods and applications.
 Perhaps the closest existing text is the recent addition to the Wiley
 Series in Financial and Quantitative Analysis. written by Cuthbertson
 (1996). The major difference between the books is that Cuthbertson
 focuses exclusively on asset pricing in the stock, bond, and foreign
 exchange markets, whereas Campbell, Lo, and MacKinlay (henceforth CLM)
 consider empirical applications throughout the field of finance,
 including corporate finance, derivatives markets, and market
 microstructure. The level of anticipation preceding publication
 can be partly measured by the fact that at least three reviews
 (including this one) have appeared since the book arrived. Moreover,
 in their reviews, both Harvey (1998) and Tiso (1998) comment on the
 need for such a text, a sentiment that has been echoed by numerous
 finance academics.

/pdf/the-econometrics-of-financial-markets-gsz6uyl6wd.pdf

The econometrics of financial markets

Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory.

Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics

https://econ.duke.edu/~boller/Published_Papers/joe_96b.pdf

Modeling and pricing long- memory in stock market volatility

Typical value-at-risk (VaR) calculations involve the probabilities of extreme dollar losses, based on the statistical distributions of market prices. Such quantities do not account for the fact that the same dollar loss can have two very different economic valuations, depending on business conditions. We propose a nonparametric VaR measure that incorporates economic valuation according to the state-price density associated with the underlying price processes. The state-price density yields VaR values that are adjusted for risk aversion, time preferences, and other variations in economic valuation. In the context of a representative agent equilibrium model, we construct an estimator of the risk-aversion coefficient that is implied by the joint observations on the cross-section of option prices and time-series of underlying asset values.

/pdf/non-parametric-risk-management-and-implied-risk-aversion-qcm2dmkkxy.pdf

Non-Parametric Risk Management and Implied Risk Aversion

In this paper the author examines the long-run behavior of oil, coal, and natural gas prices, using up to 127 years of data, and address the following questions: What does over a century of data tell us about the stochastic dynamics of price evolution, and how it should be modeled? Can models of reversion to stochastically fluctuating trend lines help forecast prices over horizons of 20 years or more? And what doe the answers to these questions tell about investment decisions that are dependent on prices and their stochastic evolution?

/pdf/the-long-run-evolution-of-energy-prices-4bxag7mw1p.pdf

The long-run evolution of energy prices

Option pricing formulas obtained from continuous-time no- arbitrage arguments such as the Black-Scholes formula generally do not depend on the drift term of the underlying asset's diffusion equation. However, the drift is essential for properly implementing such formulas empirically, since the numerical values of the parameters that do appear in the option pricing formula can depend intimately on the drift. In particular, if the underlying asset's returns are predictable, this will influence the theoretical value and the empirical estimate of the diffusion coefficient a. We develop an adjustment to the Black-Scholes formula that accounts for predictability and show that this adjustment can be important even for small levels of predictability, especially for longer-maturity options. We propose a class of continuous-time linear diffusion processes for asset prices that can capture a wider variety of predictability, and provide several numerical examples that illustrate their importance for pricing options and other derivative assets.

Jiang Wang

Papers

Implementing option pricing models when asset returns are predictable