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This paper investigates the calibration of co-movement indices such as the implied correlation index (ICX) and the herd behavior index (HIX) to different kind of stochastic processes. Both of them give an accurate description of the future level of market fear. The implied correlation index emerges when we match the observed index option price with the corresponding model price. The underlying model assumes that the individual stocks are characterized by a log-normal distribution whereas the dependence structure is described by a Gaussian copula. The herd behavior index is obtained by comparing the observed market situation with the extreme theoretical situation where all the market is driven by one single factor. This alternative measure is model-free. As an illustration, we will use the Dow Jones Industrial Average spanning from January $2000$ until October $2009$ to construct both time series. The numerical study undertakes the hard task to find a suitable model for them. These models are built by combining a stochastic differential equation with a monotonic mapping function which maps the definition domain to the unit interval. The such obtained diffusion process preserve, to some extent, the fundamental properties of the so-called herd behavior indices.