The aim of this study is to investigate the effects of monetary policy on financial asset prices in Poland. Following Gürkaynak et al. (2005) I test how many factors adequately explain the variability of short-term interest rates around MPC meetings, finding that there are two such factors. The first one has a structural interpretation as a “current interest rate change” factor, and the second one as a “future interest rate changes” factor, with the latter related to MPC communication. Regression analysis shows that, controlling for foreign interest rates and global risk aversion, both MPC actions and communication matter for government bond yields, and that communication is more important for stock prices. Furthermore, the foreign exchange rate used to depreciate (appreciate) after MPC statements signalling tighter (easier) future monetary policy. However, the effect disappeared at the end of the sample. For most of the sample the exchange rate would appreciate (depreciate) or would not change in a statistically significant manner after an increase (a decrease) of the current interest rate. The results indicate that not only changes of the current interest rate but also MPC communication matters for financial asset prices in Poland. It has important implications for the conduct of monetary policy, especially in a low inflation and low interest rate environment.

This chapter reviews the behavior of financial asset prices in relation to consumption. The chapter lists some important stylized facts that characterize US data, and relates them to recent developments in equilibrium asset pricing theory. Data from other countries are examined to see which features of the US experience apply more generally. The chapter argues that to make sense of asset market behavior one needs a model in which the market price of risk is high, time-varying, and correlated with the state of the economy. Models that have this feature, including models with habit formation in utility, heterogeneous investors, and irrational expectations, are discussed. The main focus is on stock returns and short-term real interest rates, but bond returns are also considered.

This paper presents new evidence on how asset prices respond to new information about the money stock. It shows that the information content of money stock announcements and the response of asset prices to new information in the announcements vary with changes in the monetary policy regime, the Federal Reserve operating procedures, and the reserve accounting rules. While previous studies have examined how asset prices respond to the money stock announcements under the interest-rate targeting procedure and the nonborrowed reserve procedure, we have included new evidence from the borrowed reserve targeting procedure under both lagged and contemporaneous reserve accounting rules. Looking at how both forward exchange rates and other asset prices respond to the announcements, we distinguish between periods when the asset-price response reflected a change in the real interest rate and those when it reflected a change in the inflation premium. Finally, we show that the new contemporaneous reserve accounting rules have greatly reduced the information content of the money stock announcements.

The hypothesis is that an asset pricing model can explain real US short term bond interest rates. This is tested by using data to construct the three shocks of the model and inputting the shocks back into the model to produce the model generated US real bond interest rate from 1975-2020. This is then compared to the actual US data. The notable results are that the data matches the model generated data with a high correlation and relative volatility near one, indicating a close fit.

The quarterly data set presents all variables used to fit the model to the data, for 1975Q1 to 2020Q4. All data series after construction are transformed by taking natural logarithms and detrending them to be in deviations from their respective trends. In the filtered results, we used a Hodrick-Prescott filter with λ=1600.

In constructing real output, consumption, investment, government expenditures, and net exports real per capita series from raw data we follow Chari et al. (2007). [Chari, V. V., Kehoe, Patrick J., and McGrattan, Ellen R., 2007. "Business Cycle Accounting", Econometrica, vol. 75(3), pp. 781--836, May.] The final output series used is then obtained by deducted government expenditures and net exports from the total output to be consistent with our model.

Quarterly employment and physical capital are obtained by interpolating annual data using the method in Chari et al. (2007). The goods sector labor share is measured by the Total Full-Time and Part-Time Employees minus the Full-Time and Part-Time Employees in Finance and Insurance Services (FIS) and divided by the Civilian Noninstitutional Population. The proxy for the banking time share is the same as employees in FIS as divided by the Civilian Noninstitutional Population. Leisure is then the residual share. The quarterly physical capital stock is constructed as the sum of the annual Current cost net stock of consumer durables and fixed assets interpolated. It is transformed into real terms by normalizing with the implicit price deflator for durable goods.

The inflation measure is the CPI index, quarterly, with the percentage change from the year before (on an annual basis). Velocity measures are constructed by dividing real consumption with the respective real money stocks. The nominal series for exchange credit and deposits are transformed to real terms by normalizing with the CPI index.

The data can be used in conjunction with the Matlab Code files for the model, which are attached here. This allows one to replicate the model results.

This study examines the behavior of financial asset prices in relation to consumption. The study highlights some important stylized facts that characterize U.S. data, and relates them to recent developments in equilibrium asset pricing theory. Data from other countries are examined to see which features of the U.S. experience apply more generally. The study argues that to make sense of asset market behavior one needs a model in which the market price of risk is high, time-varying, and correlated with the state of the economy. Models that have this feature, including models with habit-formation in utility, heterogeneous investors, and irrational expectations, are discussed. The main focus is on stock returns and short-term real interest rates, but bond returns are also considered.

Graph and download economic data for Interest Rates and Price Indexes; Effective Federal Funds Rate (Percent), Level (BOGZ1FL072052006Q) from Q3 1954 to Q2 2025 about federal, assets, interest rate, interest, price index, rate, indexes, price, and USA.

Graph and download economic data for Interest Rates and Price Indexes; Effective Federal Funds Rate (Percent), Level (BOGZ1FL072052006A) from 1954 to 2024 about federal, assets, interest rate, interest, rate, price index, indexes, price, and USA.

The data and programs replicate tables and figures from "A Quantity-Driven Theory of Term Premia and Exchange Rates," by Greenwood, Hanson, Stein, and Sunderam. Please see the Readme and Data Construction file for additional details.

Although the many central banks publish the yield-to-maturity of their Treasury bonds, the monthly returns earned by investors are typically not publicly available.This data set calculates monthly returns for:United States (starting 1947)Germany (starting 1972)Japan (starting 1974)Australia (starting 1969)Norway (starting 1921)Sweden (starting 1920)

Motivated by the evidence that risk premia are large and countercyclical, this paper studies a tractable real business cycle model with a small risk of economic disaster, such as the Great Depression. An increase in disaster risk leads to a decline of employment, output, investment, stock prices, and interest rates, and an increase in the expected return on risky assets. The model matches well data on quantities, asset prices, and particularly the relations between quantities and prices, suggesting that variation in aggregate risk plays a significant role in some business cycles. (JEL E13, E32, E44, G32)

Our paper can be used as a guide for those interested in learning about the GMM method with applications in Finance. These files can be used by readers who wish to follow the tutorial using the same data as the authors. 1) Before using the files with the datasets and the code, have a look at the readme file for further instructions. 2) file GMM_CKLS.R is based on the paper by Chan, Karolyi, Longstaff and Sanders (1992) and applies the GMM method to estimate the parameters of the SDE that describes the behavior of short-term continuous interest rates. 3) file GMM_CCAPM.R applies the GMM method on data comprised of 10 portfolios built according to the market value of shares traded on NYSE 4) files Data_CCAPM and Data_CKLS contain the dataset that needs to be uploaded with the code.

A yield curve (which can also be known as the term structure of interest rates) represents the relationship between market remuneration (interest) rates and the remaining time to maturity of debt securities. The information content of a yield curve reflects the asset pricing process on financial markets. When buying and selling bonds, investors include their expectations of future inflation, real interest rates and their assessment of risks. An investor calculates the price of a bond by discounting the expected future cash flows.

The European Central Bank estimates zero-coupon yield curves for the euro area and derives forward and par yield curves. A zero coupon bond is a bond that pays no coupon and is sold at a discount from its face value. The zero coupon curve represents the yield to maturity of hypothetical zero coupon bonds, since they are not directly observable in the market for a wide range of maturities. The yields must therefore be estimated from existing zero coupon bonds and fixed coupon bond prices or yields. The forward curve shows the short-term (instantaneous) interest rate for future periods implied in the yield curve. The par yield reflects hypothetical yields, namely the interest rates the bonds would have yielded had they been priced at par (i.e. at 100).

Bonds are removed if their yields deviate by more than twice the standard deviation from the average yield in the same maturity bracket. Afterwards, the same procedure is repeated.

The Federal Reserve's balance sheet has undergone significant changes since 2007, reflecting its response to major economic crises. From a modest *** trillion U.S. dollars at the end of 2007, it ballooned to approximately **** trillion U.S. dollars by October 29, 2025. This dramatic expansion, particularly during the 2008 financial crisis and the COVID-19 pandemic—both of which resulted in negative annual GDP growth in the U.S.—showcases the Fed's crucial role in stabilizing the economy through expansionary monetary policies. Impact on inflation and interest rates The Fed's expansionary measures, while aimed at stimulating economic growth, have had notable effects on inflation and interest rates. Following the quantitative easing in 2020, inflation in the United States reached ***** percent in 2022, the highest since 1991. However, by August 2025, inflation had declined to *** percent. Concurrently, the Federal Reserve implemented a series of interest rate hikes, with the rate peaking at **** percent in August 2023, before the first rate cut since September 2021 occurred in September 2024. Financial implications for the Federal Reserve The expansion of the Fed's balance sheet and subsequent interest rate hikes have had significant financial implications. In 2024, the Fed reported a negative net income of ***** billion U.S. dollars, a stark contrast to the ***** billion U.S. dollars profit in 2022. This unprecedented shift was primarily due to rapidly rising interest rates, which caused the Fed's interest expenses to soar to over *** billion U.S. dollars in 2023. Despite this, the Fed's net interest income on securities acquired through open market operations reached a record high of ****** billion U.S. dollars in the same year.

The present data collection consists of the following indicators:

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-earnings ratio (PE series), and (vii) industry (SECTOR series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations. Accordingly, our sample comprises a total number of 5,212 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

ABSTRACT Context: empirical problems in which the researcher is faced with a model that is partially specified. In these cases, the GMM method is the natural alternative for estimating the parameters of interest. Objective: the goal of this paper is to offer a tutorial that allows the researcher to understand both the theory and empirical aspects of the GMM method. Methods: we discuss the GMM concepts, forms of estimation, and limitations associated with the method. As a way of illustrating the method, we use two applications in the area of empirical finance. The first application is the estimation of the parameters of a consumption-based asset pricing models; the second is the estimation of the parameters of the evolution of the interest rate in continuous time. The data and codes in R are provided as online appendices. Conclusion: the GMM method can be used in problems where other methods such as maximum likelihood are not feasible, or even when the researcher wants to estimate a model partially specified.

The Global Macro-Financial Policy Effectiveness (2019–2025) dataset provides a structured and comprehensive view of key macroeconomic and financial indicators used to evaluate policy performance across varying global conditions. Covering the pre-pandemic, pandemic, and recovery phases, it captures the dynamic interplay between growth, inflation, interest rates, credit activity, leverage, asset prices, fiscal stance, and systemic stability. The dataset enables exploration of macro-financial linkages, policy impacts, and the evolving effectiveness of fiscal, monetary, and macroprudential measures during periods of economic expansion, contraction, and normalization. Each observation is labeled with a policy effectiveness category reflecting system resilience and recovery strength.

Count: 2,500 records (2019–2025) Features: 12 quantitative indicators + 1 target label

Column Descriptions:

Date: Represents the time period of observation between 2019 and 2025.

GDP_Growth: Annual real GDP growth rate indicating overall economic performance.

Inflation_Rate: Consumer price inflation reflecting price stability and purchasing power.

Interest_Rate: Central bank policy rate showing the stance of monetary policy.

Credit_Growth: Annual growth rate of private sector credit representing financial activity.

Leverage_Ratio: Measure of financial system leverage, calculated as assets-to-equity ratio.

Asset_Price_Index: Composite index tracking movements in stock and real estate prices.

Fiscal_Deficit: Government deficit-to-GDP ratio capturing fiscal expansion or restraint.

CentralBank_Liquidity: Level of liquidity injected or absorbed by the central bank.

Liquidity_Coverage_Ratio: Indicator of banking sector liquidity adequacy and short-term resilience.

NonPerforming_Loans: Ratio of loans in default or close to default to total loans.

Systemic_Risk_Index: Composite index quantifying financial stress and contagion potential.

Policy_Effectiveness_Label: Categorical variable representing overall policy outcome (0: Ineffective, 1: Moderately Effective, 2: Effective, 3: Highly Effective).

Strong returns in various financial markets and increased trading volumes have benefited businesses in the industry. Companies provide underwriting, brokering and market-making services for different financial instruments, including bonds, stocks and derivatives. Businesses benefited from improving macroeconomic conditions despite the high-interest-rate environment for most of the period due to inflationary pressures. However, the anticipation of interest rate cuts in the current year can limit interest income from fixed-income securities. As interest rates fall, fixed income securities will experience an outflow of capital and equities will experience an inflow of funds. The Fed is monitoring inflation, employment figures and the effects of tariffs along with other economic factors before making rate cut decisions. Overall, revenue has been growing at a CAGR of 8.5% to $491.0 billion over the past five years, including an expected increase of 1.8% in 2025 alone. Industry profit has grown during the same time due to greater interest income from bonds and will comprise 16.2% of revenue in the current year. While many industries struggled at the onset of the period due to economic disruptions stemming from the volatile economic environment and supply chain issues, businesses benefited from the volatility. Primarily, companies have benefited from increased trading activity on behalf of their clients due to fluctuations in asset prices. This has led to higher trade execution fees for firms at the onset of the period. Similarly, debt underwriting increased as many businesses have turned to investment bankers to help raise cash for various ventures. Also, improved scalability of operations, especially regarding trading services conducted by securities intermediaries, has helped increase industry profits. Structural changes have forced the industry's smaller businesses to evolve. Because competing in trading services requires massive investments in technology and compliance, boutique investment banks have alternatively focused on advising in merger and acquisition (M&A) activity. Boutique investment banks' total share of M&A revenue is forecast to grow through the end of 2030. Furthermore, the industry will benefit from improved macroeconomic conditions as inflationary pressures are expected to ease. This will help asset values rise and interest rate levels to be cut, thus allowing operators to generate more from equity underwriting and lending activities. Overall, revenue is forecast to grow at a CAGR of 1.4% to $526.8 billion over the five years to 2030.

The market prices of stocks and other assets tend to cluster on round fractions. A similar clustering is found in the interest rates paid on retail bank deposits. However, the theoretical rationales given for asset-price clustering are incompatible with the clustering of retail deposit rates. This paper proposes a theory based on depositors? limited recall. It shows that when banks exploit this phenomenon, deposit rates will tend to be set at round fractions and will be relatively sticky at these levels. The implications of this theoretical model are tested using money market deposit account and retail certificate of deposit interest-rate data from a sample of more than 500 banks.

Revenue for the Open-End Investment Funds industry has been increasing over the past five years. Open-end investment funds revenue has been growing slightly but remaining relatively steady at a CAGR of 0.0% to $196.1 billion over the past five years, including an expected increase of 4.2% in the current year. In addition, industry profit has climbed and comprises 33.1% of revenue in the current year. Overall, revenue has been increasing alongside overall asset growth, despite operators being forced to lower fees to meet shifting consumer preferences. The industry has encountered volatility due to the high-interest rate environment for most of the period. Higher interest rates reduce liquidity and make fixed income securities more attractive to investors due to less risk and more predictable interest payments. The industry has also encountered increased growth for ETFs and retail investors. The greatest shift in the industry has been an evolving investor preference for exchange-traded funds (ETFs). While mutual funds account for the majority of industry assets, growth in ETF assets has significantly outpaced that of mutual funds. Expenses that mutual fund investors incur have fallen from 0.5% of assets in 2018 to 0.4% in 2023, as industry operators have cut fees to attract new capital due to pressure from new funds (latest data available). Despite the high interest rate environment, the Fed slashed rates in 2024 and is anticipated to cut rates further in the latter part of 2025, which will boost asset prices. Open-end investment funds' revenue is expected to grow at a CAGR of 0.3% to $198.7 billion over the five years to 2030. The fears over inflation and a possible recession are expected to dominate the beginning of the outlook period. The Federal Reserve is expected to continue cutting interest rates as inflationary pressures ease. Investment companies' importance will continue to grow, with mutual funds and ETFs representing key channels for individual and institutional investors to access financial markets.