Search results

The purpose of this study is to assess the level and variability of Ghanaian property and liability insurer’s reserve estimates to examine its sources and ascertain if reserve errors are random or not (i.e. manipulated or not).

It uses information on insurer claim reserve provisions, claims outstanding, claims incurred and claims paid for the period of 2000-2010. Categorizing the sources of variation as endogenous and exogenous, the authors use the panel correlated standard error regression model to determine sources and magnitude of industry reserve error.

The study finds that size, age, lag of loss reserve error, inflation rate and real gross domestic product are significant in determining the degree of reserve error variation. Type of ownership (domestic or foreign) is, however, not a significant source of variation. Further, the authors found that industry reserve errors are random (not manipulated) across firms, suggesting that sampled insurers act independently on reserve error decision making and are not influenced by industry trends and competition.

The main research study limitation is the difficulty involved in obtaining annual statements from insurance companies in Ghana. Reluctance of companies to make statements available impeded on the smooth flow of the study during data collection.

Policy-wise, this suggest that regulatory bodies can uniquely set reserve error levels for existing firms with little influence on competition. Further, the Ghanaian insurance regulator does not to focus on the type of ownership (foreign or local) when setting regulatory standards. However, size of the company and age (length of operation) should be considered.

This paper is the first empirical study to examine the loss reserve error and loss reserve variability of Ghanaian property and liability insurance companies.

The purpose of this paper is to investigate two audit committee characteristics – independence and expertise of the audit committee – and the property‐liability insurers' financial reporting quality, which is proxied by loss reserve error.

The authors' hypotheses are tested using multivariate analysis where the loss reserve error is the dependent variable, and audit committee independence, and four types of audit committee financial expertise (accounting, finance, supervisory, and insurance expertise) are the testing variables.

It is found that accounting, finance, and insurance financial expertise are associated with more accurate loss reserve estimate. In contrast, a supervisory financial expertise and an independence audit committee are not found to be associated with better loss reserve quality.

The sample includes publicly‐held property‐liability insurers. Although the results from publicly‐held insurers could provide a good laboratory for such investigation in all insurers, they might be limited due to different organization structures of public vs private insurers.

The implications of the study are important for the SEC and NAIC. The results suggest that the requirements on the audit committee financial expertise would be necessary, even in highly regulated industry, such as property‐casualty insurance.

The paper contributes to the extant literature by studying audit committee characteristics in the insurance industry. It also contributes to the extant literature on audit committee effectiveness by decomposing the financial expertise into four types of financial expertise (accounting, finance, supervisory, or insurance expertise) and investigates which (if any) of these four types of expertise really drives the improvement of loss reserve quality.

The usual, simple and computationally expensive recovery payment method for a given reinsurance treaty, besides the total run-off triangle, builds a new run-off triangle, say recovery run-off triangle, for the reinsurer’s contribution and predicts the reinsurer’s contribution to the total loss reserves. This paper, without building a recovery run-off triangle, uses the available prior knowledge about a reinsurance treaty to predict the cedent’s loss reserve under five reinsurance treaties.

The authors propose a new solution to the problem of how to consider reserving issues when there is a reinsurance treaty for a portfolio of general insurance policies. Considering this when determining pricing or making capital decisions is very important.

In particular, it considers the quota share (QS) treaty, surplus (SPL) treaty, excess-of-loss (XL) treaty, largest claims reinsurance (LCR) treaty and excédent du coût moyen relatif (ECOMOR) treaty. Then, it develops a theoretical foundation for predicting the cedent’s loss reserve and evaluating such prediction using the mean square error of prediction (MSEP). The impact of such reinsurance treaties on the variability of the cedent’s loss reserve has been investigated through a simulation study.

This paper, without building a recovery run-off triangle, uses the available prior knowledge about a reinsurance treaty to predict the cedent’s loss reserve under five reinsurance treaties. In particular, it considers the QS treaty, SPL treaty, XL treaty, LCR treaty and ECOMOR treaty. Then, it develops a theoretical foundation for predicting the cedent’s loss reserve and evaluating such prediction using the MSEP. The impact of such reinsurance treaties on the variability of the cedent’s loss reserve has been investigated through a simulation study.

The purpose of this paper is to investigate coexistence of multiple distribution systems in property‐casualty (P/C) insurance industry in the USA.

Stochastic frontier analysis is used to measure cost and revenue efficiencies of P/C insurance companies utilizing different distribution systems.

Independent agent insurers are found to be cost inefficient compared to insurers with other distribution systems, but the independent agent insurers have better revenue efficiency compared to their long counterpart, the exclusive agent insurers. This study also documents that the direct writing system provides higher cost and revenue efficiencies than other distribution systems, although their efficiencies have been deteriorating during the same time period.

Future research could examine whether the findings change by measuring efficiencies with a non‐parametric method, i.e. data envelopment analysis.

A start‐up insurer should consider a direct writing system, which is most cost and revenue efficient.

This paper investigates efficiencies of insurers by four different distribution systems and tracks efficiency changes of insurers over 12‐year periods.

This study is concerned with evaluating the Federal Reserve forecasts of light motor vehicle sales. The goal is to assess accuracy gains from using consumer vehicle-buying attitudes and expectations about future business conditions derived from the long-running Michigan Surveys of Consumers.

Simplicity is a core principle in forecasting, and the literature provides plentiful evidence that combining forecasts from different methods and models reduces out-of-sample forecast errors if the methods and models are valid. As such, the authors construct a simple vector autoregressive (VAR) model that incorporates consumer vehicle-buying attitudes and expectations about future business conditions. Comparable forecasts of vehicle sales from this model are then combined with the Federal Reserve forecasts to assess accuracy gains.

The findings for 1994–2016 indicate that the Federal Reserve and VAR forecasts contain distinct and useful predictive information, and the combination of the two forecasts shows reductions in forecast errors that are more significant at longer horizons. The authors thus conclude that there are accuracy gains from using consumer survey responses.

This is the first study that is concerned with evaluating the Federal Reserve forecasts of vehicle sales and examines whether there are accuracy gains from using consumer vehicle-buying attitudes and expectations.

The objective of this study is to propose an economic model of the nominal money balances and reserves in the Turkish economy during the period 1960‐1988. As most of the variables show unit root non‐stationarity, an approach based on the error correction system (Phillips, 1991) is adopted. The estimated parameters of the long‐run money balance relationship based on this error correction system are very close to the Johansen‐Juselius (1990) vector autoregressive modelling approach. An error correction system and the vector autoregressive modelling approaches are alternative representations of the cointegrated systems. This study empirically demonstrates the closeness of the two systems using the data from the Turkish monetary sector. The econometric estimates of the elasticities are plausible. In small samples, both approaches may not yield almost identical estimates since the theory underlying these approaches is asymptotic.

The purpose of this paper is to examine the likely determinants of the demand for official international reserves (hereafter reserves) for India and China in the long run in a basic buffer stock model. The paper also examines the role of domestic money market disequilibrium in the short-run demand for official reserves for both the countries in a dynamic synthesis model.

The study used quarterly data for the time period 1993:Q1–2015:Q4. The long-run model is being estimated by following the Frenkel–Jovanovic (1981) buffer stock model and includes the determinants such as transaction motive variable (GDP or Imports), opportunity cost variable (domestic interest rate), precautionary motive variable (volatility of reserves) and exchange rate. The study also examined the role of domestic money market disequilibrium in addition to the above variables in the short-run reserve demand model. The money market disequilibrium term is expected to be negative and significant in the short run. The study employed autoregressive distributed lag bound testing approach to co-integration and unrestricted error-correction model (UECM) approach developed by Pesaran et al. (2001) for estimating the long-run and short-run models, respectively.

The co-integration test suggests the existence of long-run relationship between international reserves and its determinants. In the long run, all the variables are statistically significant with expected sign, except domestic interest rate variable for China. It is also found that, the money market disequilibrium term in the short run is negative and significant which validates that an excessive money demand (supply) induces an inflow (outflow) of international reserves for both India and China with a lag of four quarters. The recursive residual tests (CUSUM and CUSUMSQ) confirm the stability of both long-run and short-run reserve demand models.

The findings and policy implications of this study may be useful for the policy makers of the similar emerging economies for designing money and currency policies.

This paper is a comparative study which systematically analyzed the reserve demand behavior of the two emerging economies India and China. The study integrates the domestic money market with the international reserve demand behavior for these two economies.

This study aims to both test the asymmetric information hypothesis and explore the factors influencing the one‐ through four‐quarter‐ahead Federal Reserve inflation forecasts for 1983‐2002.

Encompassing tests are used to examine the asymmetric information hypothesis. In modeling the Federal Reserve inflation forecasts, the authors are mindful of alternative theories of inflation which emphasize such determinants as cost‐push, demand‐pull and inertial factors.

First, the Federal Reserve inflation forecasts embody useful predictive information beyond that contained in the private forecasts. Second, with the private forecasts controlled for, the near‐term Federal Reserve inflation forecasts make use of qualitative information, and the longer‐term forecasts are influenced by the forecasts of growth in both unit labor costs and aggregate demand as well as the preceding‐quarter inflation forecasts and monetary policy shifts.

The Federal Reserve forecasts are released to the public with a five‐year lag and are currently available up to the fourth quarter of 2002. This limits the use of the most up‐to‐date forecasts desirable for this study.

The factors influencing the Federal Reserve inflation forecasts are basically those emphasized publicly by monetary authorities. This finding points to the Fed's transparency and should thus help enhance its credibility with the public. Also, our results (which shed light on the predictive information in the Federal Reserve inflation forecasts not included in the private forecasts) are of value, since they can help the Fed better predict how inflation will respond to policy actions, and they can help the public form more informative inflationary expectations.

This study is motivated by the view that Democrats are concerned with reducing unemployment in the short‐run, while Republicans are concerned with keeping inflation low to promote economic stability and growth. The purpose of this paper is to ask whether the Federal Reserve forecasts of nonfarm payroll employment are accurate and free of systematic bias during the 1977‐2000 Democratic and Republican administrations.

The authors employ comparable forecasts from a univariate autoregressive integrated moving‐average (ARIMA) model to assess forecast accuracy. An ARIMA model efficiently utilizes past information and thus yields desirable forecasts commonly used as benchmarks.

Federal Reserve forecasts during the Democratic Administrations, while failing to outperform the ARIMA forecasts, display systematic under‐prediction. Such evidence is consistent with a discretionary approach to monetary policy when the bias is in the direction of full employment. During the Republican Administrations, the Federal Reserve forecasts, while superior to the ARIMA forecasts, are free of systematic bias. This, we argue, is consistent with a monetary policy that approximated the Taylor rule.

The distinct behavior over the two administrations is unique to the nonfarm payroll employment forecasts and cannot be substantiated for the Federal Reserve forecasts of other macroeconomic variables.

This study provides new insights into the monetary policies pursued during the 1977‐2000 Democratic and Republican administrations. The findings are useful and informative for the design and implementation of monetary policy.

The purpose of this paper is to present a simulation‐based approach for modeling multi‐year non‐life insurance risk in internal risk models. Strategic management in an insurance company requires a multi‐year time horizon for economic decision making, for example, in the context of internal risk models. In the literature to date, only the ultimate perspective and, more recently, the one‐year perspective (for Solvency II purposes) are considered.

The authors present a way of defining and calculating multi‐year claims development results and extend the simulation‐based algorithm (“re‐reserving”) for quantifying one‐year non‐life insurance risk, presented in Ohlsson and Lauzeningks, to a multi‐year perspective.

The multi‐year algorithm is applied to the chain ladder reserving model framework of Mack (1993).

The usefulness of the new multi‐year horizon is illustrated in the context of internal risk models by means of a case study, where the multi‐year algorithm is applied to a claims development triangle based on Mack and on England and Verrall. This algorithm has been implemented in an excel tool, which is given as supplemented material.

To the best of the authors' knowledge, there are no model approaches or studies on insurance risk for projection periods of not just one, but several, new accident years; this requires a suitable extension of the classical Mack model; however, consideration of multiple years is crucial in the context of enterprise risk management.