I am conducting an academic study that involves rigorous time-series analysis on stock-price data, and my primary objective is to test a set of hypotheses around market efficiency and volatility behaviour. To make sure the work meets publication standards, I need guidance from someone who is already publishing in SSCI-indexed journals and can point me to those articles for verification. During our collaboration I would like to discuss model selection, specification diagnostics, and the correct interpretation of results. If your strengths include techniques such as ARIMA, VAR/VECM, GARCH family models, structural-break tests or similar advanced tools (in EViews, Stata, R or Python), we will be on the same wavelength. Deliverables I am expecting: • A live consultation session (video or voice) focused on my dataset and hypotheses • Brief written notes or slides summarising the recommended workflow, equations, and robustness checks so I can replicate everything afterward • Any sample code or command scripts you deem helpful Please include at least one SSCI-indexed publication—preferably in financial econometrics—when you respond. Without this evidence of expertise I will not be able to proceed. I am ready to start as soon as I find the right academic partner. *Critical Improvements Needed before Starting Work on Paper:* 1. Get the Missing Data • Download missing variables – even the main outcome variable ERP is completely missing • Fill the missing values in data 2. Expand Literature Citations • Currently ZERO references - need to cite Chen (2010), Bhamra et al. (2010), Lettau & Wachter (2011), and others • Cannot claim "research gap" without showing what already exists 3. Try fixing Sample Size • Try expanding sample – larger sample size for financial models is recommended • Calculate if your sample is big enough to detect realistic effects • If not, acknowledge as limitation or get more data 4. Add More Control Variables • Include Federal funds rate, inflation, and other risk factors • Otherwise, can't prove effects are due to your channels specifically 5. Better Framing • Lead with economic or finance theory, not statistical methods • Position as timely policy-relevant work, not just methodology exercise Bottom Line: Good research question, but needs actual data , better justifications, simpler starting point , more observations (data), clearer methods, and stronger economic framing. *Key References - Key Literature should be used for Literature Review and Improving Methodology*: ERP & Credit Spreads: Chen, H. (2010). Macroeconomic Conditions and the Puzzles of Credit Spreads and Capital Structure. Journal of Finance, 65(6), 2171-2212. Bhamra, H. S., Kuehn, L. A., & Strebulaev, I. A. (2010). The Levered Equity Risk Premium and Credit Spreads: A Unified Framework. Review of Financial Studies, 23(2), 645-703. Shi, Z. (2019). Time-varying ambiguity, credit spreads, and the levered equity premium. Journal of Financial Economics, 134(3), 617-646. Chen, L., Collin-Dufresne, P., & Goldstein, R. S. (2009). On the Relation Between the Credit Spread Puzzle and the Equity Premium Puzzle. Review of Financial Studies, 22(9), 3367-3409. Term Structure & ERP: Lettau, M., & Wachter, J. A. (2011). The term structures of equity and interest rates. Journal of Financial Economics, 101(1), 90-113. Campbell, J. Y., & Shiller, R. J. (1988). The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors. Review of Financial Studies, 1(3), 195-228. Campbell, J. Y., & Shiller, R. J. (1991). Yield Spreads and Interest Rate Movements: A Bird's Eye View. Review of Economic Studies, 58(3), 495-514. Cochrane, J. H. (2008). Financial Markets and the Real Economy. In R. Mehra (Ed.), Handbook of the Equity Risk Premium (pp. 237-325). Amsterdam: Elsevier. Nonparametric Methods in Finance: Aït-Sahalia, Y. (1996). Testing Continuous-Time Models of the Spot Interest Rate. Review of Financial Studies, 9(2), 385-426. Stanton, R. (1997). A Nonparametric Model of Term Structure Dynamics and the Market Price of Interest Rate Risk. Journal of Finance, 52(5), 1973-2002. Cai, Z., & Hong, Y. (2003). Nonparametric Methods in Continuous-Time Finance: A Selective Review. In M. G. Akritas & D. N. Politis (Eds.), Recent Advances and Trends in Nonparametric Statistics (pp. 283-302). Amsterdam: Elsevier. Additional Useful References: Intermediary/Funding Channel: Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705-1741. He, Z., & Krishnamurthy, A. (2013). Intermediary Asset Pricing. American Economic Review, 103(2), 732-770. Bootstrap Methods: Politis, D. N., & Romano, J. P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313.