class_docstr: Risk Model
  
  A risk model is used to estimate the covariance matrix of stock returns.
class_docstr_tok: ["Risk","Model","A","risk","model","is","used","to","estimate","the","covariance","matrix","of","stock","returns","."]
docstr: Args:
      nan_option (str): nan handling option (`ignore`/`mask`/`fill`).
      assume_centered (bool): whether the data is assumed to be centered.
      scale_return (bool): whether scale returns as percentage.
docstr_tok: ["Args",":","nan_option","(","str",")",":","nan","handling","option","(","`","ignore","`","\/","`","mask","`","\/","`","fill","`",")",".","assume_centered","(","bool",")",":","whether","the","data","is","assumed","to","be","centered",".","scale_return","(","bool",")",":","whether","scale","returns","as","percentage","."]
code: def __init__(
      self,
      nan_option: str = "ignore",
      assume_centered: bool = False,
      scale_return: bool = True,
  ):
      """
      Args:
          nan_option (str): nan handling option (`ignore`/`mask`/`fill`).
          assume_centered (bool): whether the data is assumed to be centered.
          scale_return (bool): whether scale returns as percentage.
      """
      # nan
      assert nan_option in [
          self.MASK_NAN,
          self.FILL_NAN,
          self.IGNORE_NAN,
      ], f"`nan_option={nan_option}` is not supported"
      self.nan_option = nan_option
  
      self.assume_centered = assume_centered
      self.scale_return = scale_return
  
file_path: qlib/qlib/model/riskmodel/base.py
imports: import inspect
  import numpy
  import pandas
  from typing import Union
  from qlib.model.base import BaseModel
label_desc: Use image node_id 1 to create a new RiskModel object from inherited base classes: BaseModel with example: obj = RiskModel(nan_option, assume_centered, scale_return)