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Our approach builds directly on recently developed realized variation measures and non\u2010parametric jump detection statistics constructed from high\u2010frequency intra\u2010day data. A sequence of simple\u2010to\u2010implement moment\u2010based tests involving various transformations of the daily returns speak directly to the importance of different distributional features, and may serve as useful diagnostic tools in the specification of empirically more realistic continuous\u2010time asset pricing models. On applying the tests to the 30 individual stocks in the Dow Jones Industrial Average index, we find that it is important to allow for both time\u2010varying diffusive volatility, jumps, and leverage effects to satisfactorily describe the daily stock price dynamics. 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